Eric Xing's Publications List (original) (raw)

Latest Preprints:

[AI4Bio]

• Caleb N. Ellington, Ning Sun, Nicholas Ho, Tianhua Tao, Sazan Mahbub, Dian Li, Yonghao Zhuang, Hongyi Wang, Le Song, E. P. Xing, Accurate and General DNA Representations Emerge from Genome Foundation Models at Scale,NeurIPS AIDrugX Workshop, 2024. (bioRXiv:10.1101/2024.12.01.625444)

• Shuxian Zou, Tianhua Tao, Sazan Mahbub, Caleb N. Ellington, Robin Algayres, Dian Li, Yonghao Zhuang, Hongyi Wang, Le Song, E. P. Xing, A Large-Scale Foundation Model for RNA Function and Structure Prediction,NeurIPS AIDrugX Workshop, (Spotlight), 2024. (bioRXiv:10.1101/2024.11.28.625345)

• Nicholas Ho, Caleb N. Ellington, Jinyu Hou, Sohan Addagudi, Shentong Mo, Tianhua Tao, Dian Li, Yonghao Zhuang, Hongyi Wang, Xingyi Cheng, Le Song, E. P. Xing, Scaling Dense Representations for Single Cell with Transcriptome-Scale Context,NeurIPS AIDrugX Workshop, 2024. (bioRXiv:10.1101/2024.11.28.625303)

• Jiayou Zhang, Barthelemey Meynard-Piganeau, James Gong, Xingyi Cheng, Yingtao Luo, Hugo Ly, Le Song, E. P. Xing, Balancing Locality and Reconstruction in Protein Structure Tokenizer, NeurIPS Machine Learning in Structural Biology Workshop, 2024. (bioRXiv:10.1101/2024.12.02.626366)

• Caleb Ellington, Benjamin Lengerich, Thomas B.K. Watkins, Jiekun Yang, Abhinav K Adduri, Sazan Mahbub, Hanxi Xiao, Manolis Kellis, E. P. Xing Learning to estimate sample-specific transcriptional networks for 7000 tumor,bioRxiv, 2023.12.01.569658, under review at PNAS, 2024.

• Ding Bai, Shentong Mo, Ruiyi Zhang, Yingtao Luo, Jiahao Gao, Jeremy Yang, Qiuyang Wu, Digvijay Singh, Hamidreza Rahmani, Tiffany Amariuta, Danielle Grotjahn, Sheng Zhong, Nathan Lewis, Wei Wang, Trey Ideker, Pengtao Xie, E. P. Xing, scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context in Single-Cell Transcriptomics, bioRxiv, 2024.11. 09.622759, under review at Nature Methods, 2024.

• Li Zhang, Han Guo, Leah Schaffer, Young Su Ko, Digvijay Singh, Hamid Rahmani, Danielle Grotjahn, Michael Gilson, Wei Wang, Trey Ideker, E. P. Xing, Pengtao Xie, ProteinAligner: A Multi-modal Pretraining Framework for Protein Foundation Models, bioRxiv, 2024.10. 06.616870, under review at Nature Communications, 2024.

• Charlotte Bunne, Yusuf Roohani, Yanay Rosen, Ankit Gupta, Xikun Zhang, Marcel Roed, Theo Alexandrov, Mohammed AlQuraishi, Patricia Brennan, Daniel B. Burkhardt, Andrea Califano, Jonah Cool, Abby F. Dernburg, Kirsty Ewing, Emily B. Fox, Matthias Haury, Amy E. Herr, Eric Horvitz, Patrick D. Hsu, Viren Jain, Gregory R. Johnson, Thomas Kalil, David R. Kelley, Shana O. Kelley, Anna Kreshuk, Tim Mitchison, Stephani Otte, Jay Shendure, Nicholas J. Sofroniew, Fabian Theis, Christina V. Theodoris, Srigokul Upadhyayula, Marc Valer, Bo Wang, Eric Xing, Serena Yeung-Levy, Marinka Zitnik, Theofanis Karaletsos, Aviv Regev, Emma Lundberg, Jure Leskovec, Stephen R. Quake, How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities, Cell, Volume 187, Issue 25, p7045-7063, December 12, 2024.

• Xi Fu, Shentong Mo, Alejandro Buendia, Anouchka Laurent, Anqi Shao, Maria del Mar Alvarez-Torres, Tianji Yu, Jimin Tan, Jiayu Su, Romella Sagatelian, Adolfo Ferrando, Alberto Ciccia, Yanyan Lan, David Owens, Teresa Palomero, E. P. Xing, Raul Rabadan, A foundation model of transcription across human cell types, Nature (2025). https://doi.org/10.1038/s41586-024-08391-z.

[Artificial General Intelligence]

• Z. Hu and E. P. Xing, World Model**,**In preparation, 2024.

• Jiannan Xiang, Guangyi Liu, Yi Gu, Qiyue Gao, Yuting Ning, Yuheng Zha, Zeyu Feng, Tianhua Tao, Shibo Hao, Yemin Shi, Zhengzhong Liu, E. P. Xing and Zhiting Hu, Pan: Towards General World Model with Natural Language Actions and Video States, arXiv:2406.09455, 2024.

[Large Language Models]

• Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, Ion Stoica, E. P. Xing, Vicuna: An open-source chat-bot impressing gpt-4 with 90% chatgpt quality, Blog, 2023.

Selected Publications:

• Zhengzhong Liu, Aurick Qiao, Willie Neiswanger, Hongyi Wang, Bowen Tan, Tianhua Tao, Junbo Li, Yuqi Wang, Suqi Sun, Omkar Pangarkar, Richard Fan, Yi Gu, Victor Miller, Yonghao Zhuang, Guowei He, Haonan Li, Fajri Koto, Liping Tang, Nikhil Ranjan, Zhiqiang Shen, Roberto Iriondo, Cun Mu, Zhiting Hu, Mark Schulze, Preslav Nakov, Timothy Baldwin, E. P. Xing, LLM360: Towards Fully Transparent Open-Source LLMs, Proceedings of the 1st Conference on Language Modeling, 2024. (CoLM '24)

• Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, E. P. Xing, Hao Zhang, Joseph E Gonzalez, Ion Stoica, Judging LLM-as-a-judge with MT-bench and Chatbot Arena,Advances in Neural Information Processing Systems 37, MIT Press, 2023. (NeurIPS 23)(NeurIPS 23)

• Z. Hu and E. P. Xing, Towards A "Standard Model" of Machine Learning, Harvard Data Science Review, 2022.

• L. Zheng, Z. Li, H Zhang, Y. Zhuang, Z. Chen, Y. Huang, Y. Wang, Y. Xu, D Zhuo, E. P. Xing, J. E. Gonzalez, and I. Stoica, Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning, The 16th USENIX Symposium on Operating Systems Design and Implementation, 2022.(OSDI 22)

• A. Qiao, S. K. Choe, S. J. Subramanya, W. Neiswanger, Q. Ho, H. Zhang, G. R. Ganger, and E. P. Xing, Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning, The 15th USENIX Symposium on Operating Systems Design and Implementation, 2021(OSDI '21). (Recipient of the Jay Lepreau Best Paper Award)

• H. Wang, S. Ge, Z. Lipton, and E. P. Xing, Learning Robust Global Representations by Penalizing Local Predictive Power,Advances in Neural Information Processing Systems, 2019(NeurIPS 2019).

• B. Aragam, C. Dan, P. Ravikumar, and E. P. Xing, Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering,Annals of Statistics, 2019

• H. Zhang, Y. Yu, J. Jiao, E. P. Xing, L. E. Ghaoui, M. Jordan, Theoretically Principled Trade-off between Robustness and Accuracy,Proceedings of the 36th International Conference on Machine Learning, 2019(ICML 2019).

• K. Kandasamy, W. Neiswanger, J. Schneider, B. Poczos, and E. P. Xing, Neural Architecture Search with Bayesian Optimisation and Optimal Transport,Advances in Neural Information Processing Systems 32.(NeurIPS '18) (Recipient of the Nvidia Pioneering Research Award.) (arXiv:1802.07191, communicated 11 Feb 2018.)

• X. Zheng, B. Aragam, P. Ravikumar, and E. P. Xing, DAGs with NO TEARS: Continuous Optimization for Structure Learning,Advances in Neural Information Processing Systems 32.(NeurIPS 18)

• Z. Hu, Z. Yang, X. Liang, R. Salakhutdinov and E. P. Xing, Controllable Text Generation,The 34th International Conference on Machine Learning.(ICML 2017).

• Z. Hu, X. Ma, Z. Liu, E. Hovy and E.P. Xing, Harnessing Deep Neural Networks with Logic Rules,54th Annual Meeting of the Association for Computational Linguistics.(ACL 2016). (Recipient of the OUTSTANDING PAPER Award) (arXiv:1603.06318, communicated 21 Mar 2016.)

• E. P. Xing, Q. Ho, P. Xie, W. Dai, Strategies and Principles of Distributed Machine Learning on Big Data, Engineering, Volume:2, pp179 - 95, 2016.

• J. Zhu, N. Chen, and E. P. Xing, Bayesian Inference with Posterior Regularization, and applications to Infinite Latent SVMs , Journal of Machine Learning Research, 15:1799-1847, 2014.

• W. Neiswanger, C. Wang and E. P. Xing, Asymptotically Exact, Embarrassingly Parallel MCMC,Proceedings of the 30th International Conference on Conference on Uncertainty in Artificial Intelligence(UAI 2014). (arXiv:1311.4780, communicated 19 Nov 2013.)

• Q. Ho, J. Cipar, H. Cui, J.-K. Kim, S. Lee, P. B. Gibbons, G. Gibson, G. R. Ganger and E. P. Xing, More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server, Advances in Neural Information Processing Systems 27 (NIPS 2013). [Supplemental Material]

• S. Kim and E. P. Xing, Tree-Guided Group Lasso for Multi-Response Regression with Structured Sparsity, with applications to eQTL Mapping,Annals of Applied Statistics, Volume 6, Number 3 (2012), 1095-1117. [pdf]

• M. Kolar, L. Song, A. Ahmed, and E. P. Xing, Estimating Time-Varying Networks ,Annals of Applied Statistics, Vol. 4, No. 1, 94 - 123, 2010.

• S. Hanneke, W. Fu and E. P. Xing, Discrete Temporal Models of Social Networks, Electronic Journal of Statistics Vol. 4 (2010) 585-605. (arXiv:0908.1258, communicated August 2009.)

• E. Airoldi, D. Blei, S. Fienberg, and E. P. Xing, Mixed Membership Stochastic Blockmodel, Journal of Machine Learning Research, 9(Sep):1981--2014, 2008.

• E. P. Xing, A.Y. Ng, M.I. Jordan and S. Russell, Distance Metric Learning, with application to Clustering with side-information,Advances in Neural Information Processing Systems 16 (NIPS2002), (eds. Becker et al.) MIT Press, 521-528, 2002.(data,code.)

ALL Publications (in Chronical Order):

2025

• Xi Fu, Shentong Mo, Alejandro Buendia, Anouchka Laurent, Anqi Shao, Maria del Mar Alvarez-Torres, Tianji Yu, Jimin Tan, Jiayu Su, Romella Sagatelian, Adolfo Ferrando, Alberto Ciccia, Yanyan Lan, David Owens, Teresa Palomero, E. P. Xing, Raul Rabadan, A foundation model of transcription across human cell types, Nature (2025). https://doi.org/10.1038/s41586-024-08391-z.

2024

• Charlotte Bunne, Yusuf Roohani, Yanay Rosen, Ankit Gupta, Xikun Zhang, Marcel Roed, Theo Alexandrov, Mohammed AlQuraishi, Patricia Brennan, Daniel B. Burkhardt, Andrea Califano, Jonah Cool, Abby F. Dernburg, Kirsty Ewing, Emily B. Fox, Matthias Haury, Amy E. Herr, Eric Horvitz, Patrick D. Hsu, Viren Jain, Gregory R. Johnson, Thomas Kalil, David R. Kelley, Shana O. Kelley, Anna Kreshuk, Tim Mitchison, Stephani Otte, Jay Shendure, Nicholas J. Sofroniew, Fabian Theis, Christina V. Theodoris, Srigokul Upadhyayula, Marc Valer, Bo Wang, Eric Xing, Serena Yeung-Levy, Marinka Zitnik, Theofanis Karaletsos, Aviv Regev, Emma Lundberg, Jure Leskovec, Stephen R. Quake, How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities,Cell, Volume 187, Issue 25, p7045-7063, December 12, 2024.

• G. Zhang, Z. Luo, J. Huang, S. Lu, E. P. Xing, Semantic-aligned matching for enhanced detr convergence and multi-scale feature fusion, International Journal of Computer Vision, 1-20, 2024. (IJCV 24)

• Aviv Bick, Kevin Li, E. P. Xing, J Zico Kolter, Albert Gu, Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models, Advances in Neural Information Processing Systems 38, MIT Press, 2024. (NeurIPS '24)

• Lingjing Kong, Guangyi Chen, Petar Stojanov, Haoxuan Li, E. P. Xing, Kun Zhang, Towards Understanding Extrapolation: a Causal Lens., Advances in Neural Information Processing Systems 38, MIT Press, 2024. (NeurIPS '24)

• Lingjing Kong, Guangyi Chen, Biwei Huang, E. P. Xing, Yuejie Chi, Kun Zhang, Learning Discrete Concepts in Latent Hierarchical Models., Advances in Neural Information Processing Systems 38, MIT Press, 2024. (NeurIPS '24)

• Sukmin Yun, Haokun Lin, Rusiru Thushara, Mohammad Qazim Bhat, Yongxin Wang, Zutao Jiang, Mingkai Deng, Jinhong Wang, Tianhua Tao, Junbo Li, Haonan Li, Preslav Nakov, Timothy Baldwin, Zhengzhong Liu, E. P. Xing, Xiaodan Liang, Zhiqiang Shen, Web2Code: A Large-scale Webpage-to- Code Dataset and Evaluation Framework for Multimodal LLMs., Advances in Neural Information Processing Systems 38, MIT Press, 2024. (NeurIPS '24)

• Zhengzhong Liu, Aurick Qiao, Willie Neiswanger, Hongyi Wang, Bowen Tan, Tianhua Tao, Junbo Li, Yuqi Wang, Suqi Sun, Omkar Pangarkar, Richard Fan, Yi Gu, Victor Miller, Yonghao Zhuang, Guowei He, Haonan Li, Fajri Koto, Liping Tang, Nikhil Ranjan, Zhiqiang Shen, Roberto Iriondo, Cun Mu, Zhiting Hu, Mark Schulze, Preslav Nakov, Timothy Baldwin, E. P. Xing, LLM360: Towards Fully Transparent Open-Source LLMs., Proceedings of the 1st Conference on Language Modeling, 2024. (CoLM '24)

• Tianhua Tao, Junbo Li, Bowen Tan, Hongyi Wang, William Marshall, Bhargav M Kanakiya, Joel Hestness, Natalia Vassilieva, Zhiqiang Shen, E. P. Xing, Zhengzhong Liu, Crystal: Illuminating LLM Abilities on Language and Code., Proceedings of the 1st Conference on Language Modeling, 2024. (CoLM '24)

• Dacheng Li, Rulin Shao, Anze Xie, E. P. Xing, Xuezhe Ma, Ion Stoica, Joseph E. Gonzalez, Hao Zhang, DISTFLASHATTN: Distributed Memory-efficient Attention for Long-context LLMs Training., Proceedings of the 1st Conference on Language Modeling, 2024. (CoLM '24)

• Somanshu Singla, Zhen Wang, Tianyang Liu, Abdullah Ashfaq, Zhiting Hu, and E. P. Xing, Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models., Proceeding of the 2024 Conference on Empirical Methods on Natural Language Processing. (EMNLP '24)

• Han Guo, William Brandon, Radostin Cholakov, Jonathan Ragan-Kelley, E. P. Xing and Yoon Kim,Fast Matrix Multiplications for Lookup Table-Quantized LLMs., Proceeding of the 2024 Conference on Empirical Methods on Natural Language Processing. (EMNLP '24)

• Ding Bai, Caleb Ellington, Shentong Mo, Le Song, and E. P. Xing,AttentionPert: Accurately Modeling Multiplexed Genetic Perturbations with Multi-scale Effects., Proceedings of the 32nd International Conference on Intelligence Systems for Molecular Biology, 2024. (ISMB '24)

• Guangyi Liu, Yu Wang, Zeyu Feng, Qiyu Wu, Liping Tang, Yuan Gao, Zhen Li, Shuguang Cui,Julian McAuley, E. P. Xing, Zichao Yang, Zhiting Hu,Generating, Reconstructing, and Representing Discrete and Continuous Data: Generalized Diffusion with Learnable Encoding-Decoding., Proceedings of the 41st International Conference on Machine Learning, 2024. (ICML '24)

• Yue Huang et. al.,Position Paper: TrustLLM: Trustworthiness in Large Language Models., Proceedings of the 41st International Conference on Machine Learning, 2024. (ICML '24)

• Jannik Deuschel, Caleb Ellington, Yingtao Luo, Ben Lengerich, Pascal Friederich, E. P. Xing, Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning., Proceedings of the 41st International Conference on Machine Learning, 2024. (ICML '24)

• YiFan Zhang, Hanlin Zhang, Li Erran Li, E. P. Xing, Evaluating Step-by-Step Reasoning through Symbolic Verification., The 2024 Conference of the North American Chapter of the Association for Computational Linguistics. (NAACL '24)

• Hanlin Zhang, YiFan Zhang, Yaodong Yu, Dhruv Madeka, Dean Foster, E. P. Xing, Himabindu Lakkaraju, Sham M. Kakade, A Study on the Calibration of In-context Learning., The 2024 Conference of the North American Chapter of the Association for Computational Linguistics. (NAACL '24)

• Bowen Tan, Yun Zhu, Lijuan Liu, Hongyi Wang, Yonghao Zhuang, Jindong Chen, Zhiting Hu, E. P. Xing, RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs., The 2024 Conference of the North American Chapter of the Association for Computational Linguistics. (NAACL '24)(Best Demo Paper Award runner up)

• Hanoona Abdul Rasheed, Muhammad Maaz, Sahal Shaji Mullappilly, Abdelrahman M Shaker, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, E. P. Xing, Ming-Hsuan Yang, Fahad Khan, GLaMM: Pixel Grounding Large Multimodal Model., Proceedings of the 37th IEEE Conference on Computer Vision and Pattern Recognition, 2024. (CVPR '24)

• Jiahui Zhang, Fangneng Zhan, MUYU XU, Shijian Lu, E. P. Xing, FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization., Proceedings of the 37th IEEE Conference on Computer Vision and Pattern Recognition, 2024. (CVPR '24)

• Adilbek Karmanov, Dayan Guan, Shijian Lu, Abdulmotaleb El Saddik, E. P. Xing, Efficient Test-Time Adaptation of Vision-Language Models., Proceedings of the 37th IEEE Conference on Computer Vision and Pattern Recognition, 2024. (CVPR '24)

• S. Bian, D. Li, H. Wang, E. P. Xing, S. Venkataraman, Does Compressing Activations Help Model Parallel Training?., Proceedings of 7th Conference on Machine Learning and Systems, 2024. (MLsys '24)

• X. Wang, C. Li, Z. Wang, F. Bai, H. Luo, J. Zhang, N. Jojic, E. P. Xing, Z. Hu, PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization, Proceedings of 12th International Conference on Learning Representations, 2024. (ICLR 24).

• H. Guo, P. Greengard, E. P. Xing, Y. Kim, LQ-LoRA: Low-rank plus Quantized Matrix Decomposition for Efficient Language Model Finetuning, Proceedings of 12th International Conference on Learning Representations, 2024. (ICLR 24).

• H. Wang, F. M. Polo, Y. Sun, S. Kundu, M. Yurochkin, E. P. Xing, Fusing Models with Complementary Expertise, Proceedings of 12th International Conference on Learning Representations, 2024. (ICLR 24).

• L. Zheng, W. L. Chiang, Y. Sheng, T. Li, S. Zhuang, Z. Wu, Y. Zhuang, Z. Li, Z. Lin, E. P. Xing, J. E. Gonzalez, I. Stoica, H. Zhang, Lmsys-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset, Proceedings of 12th International Conference on Learning Representations, 2024. (ICLR 24).

2023

• F. Zhan, Y. Yu, R. Wu, J. Zhang, S. Lu, L. Liu, A. Kortylewsk, C. Theobalt, E. P. Xing, Multimodal Image Synthesis and Editing: A Survey and Taxonomy, IEEE Transaction on Pattern Analysis and Machine Intelligence, 2023.

• H. Zhang, S. Lin, W. Liu, P. Zhou, J. Tang, X. Liang, E. P. Xing, Iterative Graph Self-Distillation, IEEE Transactions on Knowledge and Data Engineering, 2023.

• Y. F. Zhang, H. Zhang, Z. C. Lipton, L. E. Li, E. P. Xing, Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation, Transactions on Machine Learning Research, 2023.

• Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, E. P. Xing, Hao Zhang, Joseph E Gonzalez, Ion Stoica, Judging LLM-as-a-judge with MT-bench and Chatbot Arena., Advances in Neural Information Processing Systems 37, MIT Press, 2023. (NeurIPS 23).

• B. Tan, Y. Zhu, L. Liu, E. P. Xing, Z. Hu, J. Chen, Cappy: Outperforming and Boosting Large MultiTask LMs with a Small Scorer, Advances in Neural Information Processing Systems 37, MIT Press, 2023. (NeurIPS 23).

• J. Li, A. Li, C. Tian, Q. Ho, E. P. Xing, H. Wang, FedNAR: Federated Optimization with Normalized Annealing Regularization, Advances in Neural Information Processing Systems 37, MIT Press, 2023. (NeurIPS 23).

• X. Song, W. Yao, Y. Fan, X. Dong, G. Chen, J. C. Niebles, E. P. Xing, K. Zhang, Temporally Disentangled Representation Learning under Unknown Nonstationarity, Advances in Neural Information Processing Systems 37, MIT Press, 2023. (NeurIPS 23).

• K. Liu, F. Zhan, J. Zhang, M. Xu, Y. Yu, A. El Saddik, C. Theobalt, E. P. Xing, S. Lu, Weakly Supervised 3D Open-vocabulary Segmentation, Advances in Neural Information Processing Systems 37, MIT Press, 2023. (NeurIPS 23).

• S. K. Choe, S. V. Mehta, H. Ahn, W. Neiswanger, P. Xie, E. Strubell, and E. P. Xing, Making Scalable Meta Learning Practical, Advances in Neural Information Processing Systems 37, MIT Press, 2023. (NeurIPS 23) .

• Z. Yin, E. P. Xing, Z. Shen, Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective, Advances in Neural Information Processing Systems 37, MIT Press, 2023. (NeurIPS 23).

• L. Kong, B. Huang, F. Xie, E. P. Xing, Y. Chi, K. Zhang, Identification of Nonlinear Latent Hierarchical Models, Advances in Neural Information Processing Systems 37, MIT Press, 2023. (NeurIPS 23).

• H. Yan, L. Kong, L. Gui, Y. Chi, E. P. Xing, Y. He, K. Zhang, Counterfactual Generation with Identifiability Guarantee, Advances in Neural Information Processing Systems 37, MIT Press, 2023. (NeurIPS 23).

• S. Hao, B. Tan, K. Tang, B. Ni, X. Shao, h. Zhang, E. P. Xing, and Z. Hu, BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models, Proceedings of The 61st Annual Meeting of the Association for Computational Linguistics, 2023. (ACL 23).

• H. Zhang, J. Huang, Z. Li, M. NAIK and E. P. Xing, Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming, Proceedings of The 61st Annual Meeting of the Association for Computational Linguistics, 2023. (ACL 23).

• L. Kong, M. Q. Ma, G. Chen, E. P. Xing, Yuejie Chi, Louis-Philippe Morency, and K. Zhang, Understanding Masked Autoencoders via Hierarchical Latent Variable Models, Proceedings of the 35th IEEE Conference on Computer Vision and Pattern Recognition, 2023. (CVPR 23).

• K. Liu, F. Zhan, Y. Chen, J. Zhang, Y. Yu, A. El Saddik, S. Lu, and E. P. Xing, StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields, Proceedings of the 35th IEEE Conference on Computer Vision and Pattern Recognition, 2023. (CVPR 23).

• A. Xiao, J. Huang, W. Xuan, R. Ren, K. Liu, D. Guan, A. El Saddik, S. Lu, and E. P. Xing, 3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds, Proceedings of the 35th IEEE Conference on Computer Vision and Pattern Recognition, 2023. (CVPR 23).

• K. Cui, Y. Yu, F. Zhan, S. Liao, S. Lu, and E. P. Xing, KD-GAN: Data Limited Image Generation via Knowledge Distillation, Proceedings of the 35th IEEE Conference on Computer Vision and Pattern Recognition, 2023. (CVPR 23).

• H. Wang, S. Agarwal, P. U-chupala, Y. Tanaka, E. P. Xing, D. Papailiopoulos, Cuttlefish: Low-Rank Model Training without All the Tuning, Proceedings of 6th Conference on Machine Learning and Systems, 2023. (MLsys 23).

• Y. Zhuang, H. Zhao, L. Zheng, Z. Li, E. P. Xing, Q. Ho, J. E. Gonzalez, I. Stoica, H. Zhang, On Optimizing the Communication of Model Parallelism, Proceedings of 6th Conference on Machine Learning and Systems, 2023. (MLsys 23).

• S. K. Choe, W. Neiswanger, P. Xie, and E. P. Xing, Betty: An Automatic Differentiation Library for Multilevel Optimization, Proceedings of 11th International Conference on Learning Representations, 2023. (ICLR 23).

• D. Li, H. Wang, R. Shao, H. Guo, E. P. Xing, and H. Zhang, MPCFORMER: Fast, Performant, and Private Transformer Inference with MPC, Proceedings of 11th International Conference on Learning Representations, 2023. (ICLR 23).

• H. Guo, P. Greengard, H. Wang, A. Gelman, Y. Kim, and E. P. Xing, Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach, Proceedings of 11th International Conference on Learning Representations, 2023. (ICLR 23).

2022

• Z. Hu and E. P. Xing, Towards A "Standard Model" of Machine Learning, Harvard Data Science Review, 2022.

• A. Lavin, C. G. Lee, A. Visnjic, S. Ganju, D. Newman, S. Ganguli, D. Lange, A. G. Baydin, A. Sharma, S. Zheng, A. Gibson, E. P. Xing, C. Mattman, J. Parr, and Y. Gal, Technology Readiness Levels for Machine Learning Systems, Nature Communication, 2022.

• N. Dong, M. Kampffmeyer, I. Voiculescu, E. P. Xing, Federated Partially Supervised Learning with Limited Decentralized Medical Images, IEEE Transactions on Medical Imaging, 2022.

• G. Zhang, Z. Luo, K. Cui, S. Lu, E. P. Xing, Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.

• S. Lin, C. Liu, Z. Hu, P. Zhou, S. Wang, R. Zhao, Y. Zheng, L. Lin, E. P. Xing, X. Liang, Prototypical Graph Contrastive Learning, IEEE Transactions on Neural Networks and Learning Systems, 2022.

• H. Wang, O. Lopez, E. P. Xing, and W. Wu, Kernel Mixed Model for Transcriptome Association Study, Journal of Computational Biology, 2022.

• H. Guo, B. Tan, Z. Liu, E. P. Xing and Z. Hu, Efficient (Soft) Q-Learning for Text Generation with Limited Good Data, Proceeding of the 2022 Conference on Empirical Methods on Natural Language Processing. (EMNLP 22).

• M. Deng, J. Wang, C.-P. Hsieh, Y. Wang, H. Guo, T. Shu, M. Song, E. P. Xing and Z. Hu, RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning, Proceeding of the 2022 Conference on Empirical Methods on Natural Language Processing. (EMNLP 22).

• J. Xiang, Z. Liu, Y. Zhou, E. P. Xing and Z. Hu, ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models, Proceeding of the 2022 Conference on Empirical Methods on Natural Language Processing. (EMNLP 22).

• J. Huang, K. Cui, D. Guan, A. Xiao, F. Zhan, S. Lu, S. Liao, and E. P. Xing, Masked Generative Adversarial Networks are Robust Generation Learners, Advances in Neural Information Processing Systems 36, MIT Press, 2022. (NeurIPS 22).

• K. Sreenivasan, J. Sohn, L. Yang, M. Grinde, A. Nagle, H. Wang, E. P. Xing, K. Lee, and D. Papailiopoulos, Rare Gems: Finding Lottery Tickets at Initialization, Advances in Neural Information Processing Systems 36, MIT Press, 2022. (NeurIPS 22).

• D. Li, H. Wang, E. P. Xing and Hao Zhang, AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness, Advances in Neural Information Processing Systems 36, MIT Press, 2022. (NeurIPS 22).

• Z. Shen, Z. Liu and E. P. Xing, Sliced Recursive Transformer, Proceeding of the 18th European Conference of Computer Vision, 2022. (ECCV 22).

• Z. Shen and E. P. Xing, A Fast Knowledge Distillation Framework for Visual Recognition, Proceeding of the 18th European Conference of Computer Vision, 2022. (ECCV 22).

• Z. Shen and E. P. XingZ. Liu, Z. Shen, Y. Long, E. P. Xing, K.-T. Cheng, C. Leichner, Data-Free Neural Architecture Search via Recursive Label Calibration, Proceeding of the 18th European Conference of Computer Vision, 2022. (ECCV 22).

• H. Wang, Z. Huang, X. Wu and E. P. Xing, Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation, Proceedings of The 28th ACM SIGKDD Conference on knowledge Discovery and Data Mining, 2022. (KDD 22).

• X. Huang, Z. Shen, S. Li, Z. Liu, X. Hu, J. Wicaksana, E. P. Xing, K-T. Cheng, SDQ: Stochastic Differentiable Quantization with Mixed Precision, Proceedings of the 39th International Conference on Machine Learning, 2022. (ICML 22).

• H. Wang, Z. Huang, H. Zhang, Y. J. Lee, and E. P. Xing, Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features, Proceedings of the 38th International Conference on Conference on Uncertainty in Artificial Intelligence, 2022. (UAI 22).

• L. Zheng, Z. Li, H Zhang, Y. Zhuang, Z. Chen, Y. Huang, Y. Wang, Y. Xu, D Zhuo, E. P. Xing, J. E. Gonzalez, and I. Stoica, Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning, The 16th USENIX Symposium on Operating Systems Design and Implementation, 2022. (OSDI 22).

• A. Chavan, Z. Shen, Zh. Liu, Ze. Liu, K-T. Cheng, and E. P. Xing, Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space, Proceedings of the 34th IEEE Conference on Computer Vision and Pattern Recognition, 2022. (CVPR 22).

• Ze. Liu, K-T. Cheng, D. Huang, Z. Shen, and E. P. Xing, Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation, Proceedings of the 34th IEEE Conference on Computer Vision and Pattern Recognition, 2022. (CVPR 22).

• H. Wang, Z. Huang, D. Huang, Y. Lee, and E. P. Xing, The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization, Proceedings of the 34th IEEE Conference on Computer Vision and Pattern Recognition, 2022. (CVPR 22).

• H. Zhang, Y-F. Zhang, W. Liu, A. Weller, B. Scho ̈lkopf, and E. P. Xing, Towards Principled Disentanglement for Domain Generalization, Proceedings of the 34th IEEE Conference on Computer Vision and Pattern Recognition, 2022. (CVPR 22).

• H. Wang, O. Lopez, W. Wu and E. P. Xing, Gene Set Priorization Guided by Regulatory Networks with p-values through Kernel Mixed Model, Proceedings of the 26th Annual International Conference on Research in Computational Molecular Biology, 2022. (RECOMB 22).

• B. Lengerich, E. P. Xing, and R. Caruana, Dropout as a Regularizer of Interaction Effects, Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, 2022. (AISTATS 22).

• B. Garg, L. Zhang, P. Sridhara, R. Hosseini, E. P. Xing, and P. Xie, Learning from Mistakes - A Framework for Neural Architecture Search,Thirty-sixth AAAI Conference on Artificial Intelligence, 2022. (AAAI 2022).

• Z. Shen, Z. Liu, Z. Liu, M. Savvides, T. Darrell, E. P. Xing, Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning,Thirty-sixth AAAI Conference on Artificial Intelligence, 2022. (AAAI 2022).

2021

• X. Chen, H. Sun, C. Ellington, E. P. Xing, and L. Song Multi-task Learning of Order-Consistent Causal Graphs,Advances in Neural Information Processing Systems 35, MIT Press, 2021. (NeurIPS 2021).

• M. Deng, B. Tan, Z. Liu, E. P. Xing and Z. Hu, Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation,Proceeding of the 2021 Conference on Empirical Methods on Natural Language Processing. (EMNLP 2021).

• H. Yao, Y.X. Wu, M. Al-Shedivat, and E. P. Xing, Knowledge-Aware Meta-learning for Low-Resource Text Classification,Proceeding of the 2021 Conference on Empirical Methods on Natural Language Processing. (EMNLP 2021).

• S. Ge, H. Wang, A. Alavi, E. P. Xing, Z. Bar-Joseph, Supervised adversarial alignment of single-cell RNA-seq data. , Journal of Computational Biology, 28(5):501 - 13, 2021.

• Z. Wang, Y. Ni, B. Jing, D. Wang, H. Zhang, E. P. Xing, DNB: A Joint Learning Framework for Deep Bayesian Nonparametric Clustering. , IEEE Transactions on Neural Networks and Learning Systems, 2021.

• K. Tran, W. Neiswanger, K. Broderick, E. P. Xing, J Schneider, and Z. Ulissi, Computational catalyst discovery: Active classification through myopic multiscale sampling,The Journal of Chemical Physics, 2021.

• H. Wang, F. Pei, M. Vanyukov, I. Bahar, W. Wu, and E. P. Xing, Coupled Mixed Model for Joint Genetic Analysis of Complex Disorders with Two Independently Collected Data Sets,BMC Bioinformatics, vol. 21, 2021.

• J. Chen, J. Tang, J. Qin, X. Liang, L. Liu, E. P. Xing, and L. Lin, GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning,Proceedings of The 59th Annual Meeting of the Association for Computational Linguistics, 2021. (ACL '21).

• X. He, Z. Cai, W. Wei, Y. Zhang, L. Mou, E. P. Xing, and P. Xie, Towards Visual Question Answering on Pathology Images,Proceedings of The 59th Annual Meeting of the Association for Computational Linguistics, 2021. (ACL '21).

• M. Zhou, Z. Li, B. Tan, G. Zeng, W. Yang, X. He, Z. Ju, S. Chakravorty, S. Chen, X. Yang, Y. Zhang, Q. Wu, Z. Yu, K. Xu, E. P. Xing, and P. Xie, On the Generation of Medical Dialogs for COVID-19,Proceedings of The 59th Annual Meeting of the Association for Computational Linguistics, 2021. (ACL '21).

• B. Tan, Z. Yang, M. AI-Shedivat, E. P. Xing, Z. Hu, Progressive Generation of Long Text,The 2021 Conference of the North American Chapter of the Association for Computational Linguistics. (NAACL '21).

• A. Qiao, S. K. Choe, S. J. Subramanya, W. Neiswanger, Q. Ho, H. Zhang, G. R. Ganger, and E. P. Xing, Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning,The 15th USENIX Symposium on Operating Systems Design and Implementation, 2021. (OSDI '21). (Recipient of the Jay Lepreau Best Paper Award)

• M. Al-Shedivat, J. Gillenwater, E. P. Xing, A. Rostamizadeh, Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms,Proceedings of 9th International Conference on Learning Representations, 2021. (ICLR '21).

• B. Boecking, W. Neiswanger, E. P. Xing, A. Dubrawski, Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling,Proceedings of 9th International Conference on Learning Representations, 2021. (ICLR '21).

• M. Al-Shedivat, L. Li, E. P. Xing, and A. Talwalkar, On Data Efficiency of Meta-learning for Personalized Federated Learning,Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2020. (AISTATS '21).

• S. Bang, P. Xie, H. Lee, W. Wu and E. P. Xing, Explaining a Black--Box by Using a Deep Variational information Bottleneck Approach,Thirty-fifth AAAI Conference on Artificial Intelligence, 2021. (AAAI 2021).

2020

• Y Zheng, H Wang, Y Zhang, X Gao, E. P. Xing, and M Xu Poly (A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly (A) signal without prior knowledge from target species,PLoS Computational Biology, 16 (11), e1008297, 2020

• M. Al-Shedivat, A. Dubey, and E. P. Xing Contextual Explanation Networks,Journal of Machine Learning Research, 21 (194), 1-44, 2020.

• K. Kandasamy, K. Vysyaraju, W. Neiswanger, B. Paria, C. Collins, J. Schneider, B. Poczos, and E. P. Xing Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly,Journal of Machine Learning Research, 21 (81), 1-27, 2020.

• K. Tran, W. Neiswanger, J. Yoon, Q. Zhang, E. P. Xing, Z. Ulissi, Methods for comparing uncertainty quantifications for material property predictions,Machine Learning: Science and Technology, Volume 1, Number 2, 2020.

• S. Kadambi, Z. Wang, E. P. Xing, Z. Ulissi, WGAN Domain Adaptation for the Joint Optic Disc-and- Cup Segmentation in Fundus Images,International Journal of Computer Assisted Radiology and Surgery, Volume 1, Number 2, 2020.

• H. Wang, MM. Vanyukov, W. Wu, and E. P. Xing, Discovering weaker genetic associations guided by known associations,BMC Medical genomics, vol. 13, Suppl. 3, 2020.

• H. Zhang, Y. Li, Z. Deng, X. Liang, L. Carin, and E. P. Xing AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning,Advances in Neural Information Processing Systems 34, MIT Press, 2020. (NeurIPS 2020).

• Y. Wu, P. Zhou, A. Wilson, E. P. Xing, and Z. Hu Improving GAN Training with Probability Ratio Clipping and Sample Reweighting,Advances in Neural Information Processing Systems 34, MIT Press, 2020. (NeurIPS 2020).

• G. Plumb, M. Al-Shedivat, A. Cabrera, A. Oerer, E. P. Xing, and A. Talwalkar Regularizing Black-box Models for Improved Interpretability,Advances in Neural Information Processing Systems 34, MIT Press, 2020. (NeurIPS 2020).

• B. Tan, L. Qin, E. P. Xing, and Z. Hu Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach,Proceeding of the 2020 Conference on Empirical Methods on Natural Language Processing. (EMNLP 2020).

• S. Lin, W. Wang, Z. Yang, X. Liang, F. F. Xu, , E. P. Xing, and Z. Hu Data-to-Text Generation with Style Imitation,Proceeding of the 2020 Conference on Empirical Methods on Natural Language Processing. (EMNLP 2020).

• Z. Liu, G. Ding, A. Bukkittu, M. Gupta, P. Gao, A. Ahmed, S. Zhang, X. Gao, S. Singhavi, L. Li, W. Wei, Z. Hu, H. Shi, X. Liang, T. Mitamura, E. Xing and Z. Hu. A Data-Centric Framework for Composable NLP Workflows,Proceeding of the 2020 Conference on Empirical Methods on Natural Language Processing. (EMNLP 2020 Demo).

• Z. Huang, H Wang, H. Dong, and E. P. Xing Self-Challenging Improves Cross-Domain Generalization,Proceedings of the 16th European Conference on Computer Vision, 2020. (ECCV 20)

• C. Song, S. Zhang, N. Sadoughi, P. Xie and E. P. Xing Generalized Zero-Shot Text Classification for ICD Coding,29th International Joint Conference on Artificial Intelligence (IJCAI 20), 2020.

• K. Dubey, M. Zhang, E. P. Xing, and S. Williamson Distributed, partially collapsed MCMC for Bayesian Nonparametrics,Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020. (AISTATS 20)

• X. Zheng, C. Dan, B. Aragam, P. Ravikumar, and E. P. Xing Learning Sparse Nonparametric DAGs,Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020. (AISTATS 20)

• K. Korovina, S. Xu, K. Kandasamy, W. Neiswanger, B. Poczos, J. Schneider, and E. P. Xing ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations,Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020. (AISTATS 20)

• H. Wang, X. Wu, Z. Huang, and E. P. Xing High-frequency Component Helps Explain the General- ization of Convolutional Neural Networks,Proceedings of the 32nd IEEE Conference on Computer Vision and Pattern Recognition, 2020. (CVPR 20)

• S. Ge, H. Wang, A. Alavi, E. P. Xing, and Z. Bar-Joseph, Supervised Adversarial Alignment of Single-Cell RNA-seq Data,Proceedings of the 24th Annual International Conference on Research in Computational Molecular Biology, 2020. (RECOMB 20)

2019

• H. Wang, T. Yue, J. Yang, W. Wu, and E. P. Xing, Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studie,BMC Bioinformatics, vol. 20, Suppl. 23, 2019.

• B. Aragam, C. Dan, P. Ravikumar, and E. P. Xing, Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering,Annals of Statistics, 2019

• M. Marchetti-Bowick, Y. Yu, W. Wu, and E. P. Xing, A penalized regression model for the joint estimation of eQTL associations and gene network structure,Annals of Applied Statistics, Vol. 13, No. 1, 248-270, 2019

• M. Sachan, A. Dubey, E. Hovy, D. Roth, T. Mitchell and E. P. Xing, Discourse in Multimedia: A Case Study in Information Extraction,Computational Linguistics journal, 2019

• M. Kampffmeyer, N. Dong, X. Liang, Y. Zhang and E. P. Xing, ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation,IEEE Transactions on Image Processing, 28(5), 2518-2529

• B. Lengerich, B. Aragam, and E. P. Xing, Learning Sample-Specific Models with Low-Rank Personalized Regression,Advances in Neural Information Processing Systems, 2019(NeurIPS 2019).

• H. Wang, S. Ge, Z. Lipton, and E. P. Xing, Learning Robust Global Representations by Penalizing Local Predictive Power,Advances in Neural Information Processing Systems, 2019(NeurIPS 2019).

• B. Huang, K. Zhang, P. Xie, M. Gong, E. P. Xing, Specific and Shared Causal Relation Modeling and Mechanism-based Clustering,Advances in Neural Information Processing Systems, 2019(NeurIPS 2019).

• Z. Hu, B. Tan, R. Salakhutdinov, T. Mitchell, and E. P. Xing, Learning Data Manipulation for Augmentation and Weighting,Advances in Neural Information Processing Systems, 2019(NeurIPS 2019).

• Z. Hu, H. Shi, B. Tan, W. Wang, Z. Yang, T. Zhao, J. He, L. Qin, D. Wang, X. Ma, Z. Liu, X. Liang, W. Zhu, D. Sachan, and E. P. Xing, Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation,Proceedings of The 57th Annual Meeting of the Association for Computational Linguistics, 2019(ACL 2019). (Nomination for the Best Paper Award)

• B. Jing, Z. Wang, and E. P. Xing, Show, Describe and Conclude: On Exploiting the Structure Infor- mation of Chest X-ray Report,Proceedings of The 57th Annual Meeting of the Association for Computational Linguistics, 2019(ACL 2019).

• A. Qiao, B. Aragam, B. Zhang, E. P. Xing, Fault Tolerance in Iterative-Convergent Machine Learning,Proceedings of the 36th International Conference on Machine Learning, 2019(ICML 2019).

• H. Zhang, Y. Yu, J. Jiao, E. P. Xing, L. E. Ghaoui, M. Jordan, Theoretically Principled Trade-off between Robustness and Accuracy,Proceedings of the 36th International Conference on Machine Learning, 2019(ICML 2019).

• K. Xu, M. Lam, J. Pang, X. Gao, C. Band, P. Mathur, F. Papay, A. K. Khanna, J. B. Cywinski, K. Maheshwari, P. Xie, E. P. Xing, Multimodal Machine Learning for Automated ICD Coding,Conference on Machine Learning for Healthcare, 2019(MLCH 2019).

• Z. Wang, N. Dong, S. D. Rosario, M. Xu, P. Xie, and E. P. Xing, Ellipse Detection of Optic Disc-and- Cup Boundary in Fundus Image with Unsupervised Domain Adaption,The IEEE International Symposium on Biomedical Imaging, 2019(ISBI 2019).

• J. Kim, A. Aghayev, A. Gibson, and E. P. Xing, Titan: Simplifying Distributed Machine Learning Programming Without Introducing A New Programming Model,USENIX Annual Technical Conference, 2019(ATC 2019).

• J. Wei, G. Gibson, P. Gibbons, and E. P. Xing, Automating Dependence-Aware Parallelization of Machine Learning Training on Distributed Shared Memory,Proceedings of European Conference on Computer Systems(EuroSys 2019).

• M. Kampffmeyer, Y. Chen, X. Liang, H. Wang, Y. Zhang, E. P. Xing, Rethinking Knowledge Graph Propagation for Zero-Shot Learning,Proceedings of the 31st IEEE Conference on Computer Vision and Pattern Recognition, 2019.(CVPR 2019).

• W. Dai, Y. Zhou, N. Dong, H. Zhang, and E. P. Xing, Toward Understanding the Impact of Staleness in Distributed Machine Learning,Proceedings of Seventh International Conference on Learning Representations(ICLR 2019).

• H. Wang, Z. He, Z. C. Lipton, and E. P. Xing, Learning Robust Representations by Projecting Superficial Statistics Out,Proceedings of Seventh International Conference on Learning Representations(ICLR 2019).

• Y. Li, X. Liang, Z. Hu, Y. Chen, and E. P. Xing, Graph Transformer,Proceedings of Seventh International Conference on Learning Representations(ICLR 2019).

• H. Xu, H. Zhang, Z. Hu, X. Liang, R. Salakhutdinov, and E. P. Xing, AutoLoss: Learning Discrete Schedule for Alternate Optimization,Proceedings of Seventh International Conference on Learning Representations(ICLR 2019).

• H. Wang, D. Sun, and E. P. Xing, What If We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks,Proceedings of Thirty-third AAAI Conference on Artificial Intelligence(AAAI 2019).

• Y. Li, X. Liang, Z. Hu, and E. P. Xing, Knowledge Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation,Proceedings of Thirty-third AAAI Conference on Artificial Intelligence(AAAI 2019).

• H. Wang, Z. Wu and E. P. Xing, Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications,Proceedings of 24th Pacific Symposium on Biocomputing(PSB 2019).

• H. Wang, X. Liu, Y. Tao, W. Ye, Q. Jin, W. W. Cohen and E. P. Xing, Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning,Proceedings of 24th Pacific Symposium on Biocomputing(PSB 2019).

2018

• P. Xie and E. P. Xing, Diversity-Promoting Bayesian Learning of Latent Variable Models,Journal of Machine Learning Research, 2018

• Y. Zhou, Y. Liang, Y. Yu, W. Dai and E. P. Xing, Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters,Journal of Machine Learning Research, 2018

• H. Wang, X. Liu, Y. Xiao, M. Xu and E. P. Xing, Multiplex Confounding Factor Correction for Genomic Association Mapping with Squared Sparse Linear Mixed Model,Methods, 2018 Aug 1; 145: 33?40.

• H. Wang, B. Aragam and E. P. Xing, Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies,Methods, 2018 2018 Aug 1; 145: 2?9.

• H. Wang, B. J. Lengerich, B. Aragam and E. P. Xing, Precision Lasso: Accounting for Correlations and Linear Dependencies in High-Dimensional Genomic Data,Bioinformatics, PMID: 30184048 DOI:10.1093/bioinformatics/bty750 , 2018.

• X. Zheng, B. Aragam, P. Ravikumar, and E. P. Xing, DAGs with NO TEARS: Continuous Optimization for Structure Learning,Advances in Neural Information Processing Systems 32.(NeurIPS '18)

• K. Kandasamy, W. Neiswanger, J. Schneider, B. Poczos, and E. P. Xing, Neural Architecture Search with Bayesian Optimisation and Optimal Transport,Advances in Neural Information Processing Systems 32.(NeurIPS '18) (Recipient of the Nvidia Pioneering Research Award.)

• Z. Yang, Z. Hu, C. Dyer, E. P. Xing and T. Berg-Kirkpatrick, Unsupervised Text Style Transfer using Language Models as Discriminators,Advances in Neural Information Processing Systems 32.(NeurIPS '18)

• C. Y. Li, X. Liang, Z. Hu, and E. P. Xing, Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation,Advances in Neural Information Processing Systems 32.(NeurIPS '18)

• C. Dan, L. Liu, B. Aragam, P. Ravikumar, and E. P. Xing, The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models,Advances in Neural Information Processing Systems 32.(NeurIPS '18)

• Z. Hu, Z. Yang, R. Salakhutdinov, X. Liang, L. Qin, H. Dong, and E. P. Xing, Deep Generative Models with Learnable Knowledge Constraints,Advances in Neural Information Processing Systems 32.(NeurIPS '18)

• M. Sachan, A. Dubey, T. Mitchell, D. Roth, and E. P. Xing, Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems,Advances in Neural Information Processing Systems 32.(NeurIPS '18)

• X. Liang, Z. Hu, and E. P. Xing, Symbolic Graph Reasoning Meets Convolutions,Advances in Neural Information Processing Systems 32.(NeurIPS '18)

• B. Jing, P. Xie, and E. P. Xing, On the Automatic Generation of Medical Imaging Reports,Proceedings of The 56th Annual Meeting of the Association for Computational Linguistics(ACL '18)

• P. Xie, H. Shi, Ming Zhang, and E. P. Xing, A Neural Architecture for Automated ICD Coding,Proceedings of The 56th Annual Meeting of the Association for Computational Linguistics(ACL '18)

• X. Liang, H. Zhou, and E. P. Xing, Dynamic-Structured Semantic Propagation Network,Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition(CVPR '18)

• P. Xie, H. Zhang, Y. Zhu, and E. P. Xing, Nonoverlap-Promoting Variable Selection,Proceedings of the 35th International Conference on Machine Learning(ICML '18)

• P. Xie, W. Wu, Y. Zhu, and E. P. Xing, Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis,Proceedings of the 35th International Conference on Machine Learning(ICML '18)

• J. Foerster, G. Farquhar, M. Al-Shedivat, T. Rockt‰schel, E. P. Xing and S. Whiteson, DiCE: The Infinitely Differentiable Monte-Carlo Estimator,Proceedings of the 35th International Conference on Machine Learning(ICML '18)

• J. Oliva, A. Dubey, M. Zaheer, B. Poczos, R. Salakhutdinov, E. P. Xing and J. Schneider Transformation Autoregressive Networks,Proceedings of the 35th International Conference on Machine Learning(ICML '18)

• L. Lee, E. Parisotto, D. S. Chaplot, E. P. Xing and R. Salakhutdinov Gated Path Planning Networks,Proceedings of the 35th International Conference on Machine Learning(ICML '18)

• P Xie, J. Kim, Q. Ho, Y. Yu, and E. P. Xing, Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design,Proceedings of ACM Symposium on Cloud Computing 2018(SoCC'18)

• A. Qiao, A. Aghayev, W. Yu, H. Chen, Q. Ho, G. A. Gibson, and E. P. Xing, Litz: Elastic Framework for High-Performance Distributed Machine Learning,Proceedings of USENIX Annual Technical Conference 2018(ATC'18)

• S. Xu, H. Zhang, G. Neubig, W. Dai, J. Kim, Z. Deng, Q. Ho, G. Yang, and E. P. Xing, Cavs: An Efficient Runtime System for Dynamic Neural Networks,Proceedings of USENIX Annual Technical Conference 2018(ATC'18)

• L. Yang, X. Liang, T. Wang, and E. P. Xing, Real-to-Virtual Domain Unification for End-to-End Autonomous Driving,Proceeding of the 15th European Conference of Computer Vision(ECCV'18)

• X. Liang, T. Wang, L. Yang, and E. P. Xing, CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving,Proceeding of the 15th European Conference of Computer Vision(ECCV'18)

• X. Liang, H. Zhang, L. Lin, and E. P. Xing, Generative Semantic Manipulation with Mask-Contrasting GAN,Proceeding of the 15th European Conference of Computer Vision(ECCV'18)

• M. Sachan, and E. P. Xing, Parsing to Programs: A Framework for Situated QA,Proceedings of The 24th ACM SIGKDD Conference on knowledge Discovery and Data Mining(KDD'18)

• B. J. Lengerich, B. Aragam, and E. P. Xing, Personalized Regression Enables Sample-Specific Pan-Cancer Analysis,Proceedings of the Twenty-sixth International Conference on Intelligence Systems for Molecular Biology(ISMB'18)

• M. Sachan and E. P. Xing, Self-Training for Jointly Learning to Ask and Answer Questions,Proceedings of The 2018 Conference of the North American Chapter of the Association for Computational Linguistics(NAACL'18)

• P. Xie and E. P. Xing, Learning Balanced Distance Metrics,Proceedings of the 21st International Conference on Artificial Intelligence and Statistics(AISTATS'18)

• Z. Hu, Z. Yang, X. Liang, R. Salakhutdinov, and E. P. Xing, On Unifying Deep Generative Models,Proceedings of 6th International Conference on Learning Representations(ICLR'18)

2017

• S. Lee, N. Gornitz, E. P. Xing, D. Heckerman, C. Lippert Ensembles of Lasso Screening Rules,IEEE Transaction on Pattern Analysis and Machine Intelligence, 2017 (10.1109/TPAMI.2017.2765321)

• X Chang, YL Yu, Y Yang and E. P. Xing Semantic pooling for complex event analysis in untrimmed videos,IEEE Transaction on Pattern Analysis and Machine Intelligence, 39 (8), 1617-1632, 2017

• S. Lee, H. Wang and E. P. Xing Backward Genotype-Transcript-Phenotype Association Mapping,Methods, Volume 129, 1 October 2017, Pages 18-23.

• H. Zhang, Z. Deng, X. Liang, L. Yang, S. Xu, J. Zhu, and E. P. Xing, Structured Generative Adversarial Networks,Proceedings of Advances in Neural Information Processing Systems 31(NIPS '17). (Recipient of the Nvidia Pioneering Research Award)

• P. Xie and E. P. Xing Deep Conditional Determinantal Point Process for Large-Scale Multi-Label Classification on Youtube-8M and Open Images Datasets,Proceedings of 19th International Conference on Computer Vision(ICCV '17)

• P. Goyal, Z. Hu, X.~Liang, C. Wang, and E. P. Xing Nonparametric Variational Auto-encoders for Hierarchical Representation Learning,Proceedings of 19th International Conference on Computer Vision(ICCV '17)

• X. Liang, Z. L. Lee, W. Dai, and E. P. Xing Dual Motion GAN for Future-Flow Embedded Video Prediction,Proceedings of 19th International Conference on Computer Vision(ICCV '17)

• X. Liang, Z. Hu, H. Zhang, C. Gan, and E. P. Xing Recurrent Topic-Transition GAN for Visual Paragraph Generation,Proceedings of 19th International Conference on Computer Vision(ICCV '17)

• M. Sachan, K. Dubey, and E. P. Xing From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems,Proceeding of the 2017 Conference on Empirical Methods on Natural Language Processing.(EMNLP '17)

• P. Xie, B. Poczos, and E. P. Xing Near-Orthogonality Regularization in Kernel Methods,Proceedings of the 33rd International Conference on Conference on Uncertainty in Artificial Intelligence.(UAI '17)

• I. Yen, X. Huang, W. Dai, P. Ravikumar, I. Dhillon, and E. P. Xing, A Parallel and Primal-Dual Sparse Method for Extreme Classification,Proceedings of The 23rd ACM SIGKDD Conference on knowledge Discovery and Data Mining(KDD 2017).

• J. He, Z. Hu, T. Berg-Kirkpatrick, Y. Huang, and E. P. Xing, Efficient Correlated Topic Modeling with Topic Embedding,Proceedings of The 23rd ACM SIGKDD Conference on knowledge Discovery and Data Mining(KDD 2017).

• K. Zhang, C. Liu, J. Zhang, H. Xiong, E. P. Xing, and J. Ye, Finding Structures of Large Matrices Through Compression,Proceedings of The 23rd ACM SIGKDD Conference on knowledge Discovery and Data Mining(KDD 2017).

• W. Neiswanger and E. P. Xing, Post-Inference Prior Swapping,The 34th International Conference on Machine Learning.(ICML 2017).

• P. Xie and E. P. Xing, Cosine Similarity Constrained Latent Space Models,The 34th International Conference on Machine Learning.(ICML 2017).

• P. Xie, A. Singh and E. P. Xing, Uncorrelation and Evenness: A New Diversity-Promoting Regularizer,The 34th International Conference on Machine Learning.(ICML 2017).

• Z. Hu, Z. Yang, X. Liang, R. Salakhutdinov and E. P. Xing, Controllable Text Generation,The 34th International Conference on Machine Learning.(ICML 2017).

• H. Zhang, Z. Zeng, W. Dai, Q. Ho and E. P. Xing, Poseidon: An Efficient Communication Interface for Distributed Deep Learning on GPU Clusters.,USENIX Annual Technical Conference (ATC 2017).

• P. Xie and E.P. Xing, A Constituent-Centric Neural Architecture for Reading Comprehension,55th Annual Meeting of the Association for Computational Linguistics.(ACL 2017).

• L. Qin, Z. Zhang, H. Zhao, Zhiting Hu and E.P. Xing, Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification ,55th Annual Meeting of the Association for Computational Linguistics.(ACL 2017).

• X. Liang, L. Lin, X. Shen, J. Feng, S. Yan and E. P. Xing, Interpretable Structure-Evolving LSTM,Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2017).

• X. Liang, L. Lee, and E. P. Xing, Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection,Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2017).

• M. Law, Y. Yu, R. Urtasun, R. S. Zemel and E. P. Xing, Efficient Multiple Instance Metric Learning using Weakly Supervised Data,Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2017).

• M. Xu, X. Chai, H. Muthakana, X. Liang, G. Yang, T. Zeev-Ben-Mordehai and E. P. Xing, Deep learning based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms,the Twenty-fifth International Conference on Intelligence Systems for Molecular Biology (ISMB 2017). _Bioinformatics (2017) in press._[pdf]

• Y. Zhou, K. Yuan, Y. Yu, X. Ni, P. Xie, E. P. Xing and S. Xu, Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with polynomial functions,Heredity (Edinb), 118(5):503-510, 2017.

• M. Al-Shedivat, A. G. Wilson, Y. Saatchi, Z. Hu, and E. P. Xing, Learning Scalable Deep Kernels with Recurrent Structure,Journal of Machine Learning Research, in press, 2017. (arXiv:1610.08936, communicated 27 Oct 2016.)

2016

• A. Dubey, S. J. Reddi, S. Williamson, B. Poczos, A. J. Smola and E. P. Xing, Variance Reduction in Stochastic Gradient Langevian Dynamics, Advances in Neural Information Processing Systems 30 (NIPS 2016).

• M. Al-Shedivat, K. Kandasamy and E. P. Xing, Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices, Advances in Neural Information Processing Systems 30 (NIPS 2016).

• Z. Hu, A. Wilson, R. Salakhutdinov and E. P. Xing, Stochastic Variational Deep Kernel Learning, Advances in Neural Information Processing Systems 30 (NIPS 2016).

• E. P. Xing, Q. Ho, P. Xie, W. Dai, Strategies and Principles of Distributed Machine Learning on Big Data, Engineering, Volume:2, pp179 - 95, 2016.

• Z. Hu, Z. Yang, R. Salakhutdinov and E. P. Xing, Deep Neural Networks with Massive Learned Knowledge,Proceeding of the 2016 Conference on Empirical Methods on Natural Language Processing.(EMNLP 2016).

• M. Sachan and E.P. Xing, Easy Questions First? Curriculum Learning for Question Answering ,54th Annual Meeting of the Association for Computational Linguistics.(ACL 2016).

• Z. Hu, X. Ma, Z. Liu, E. Hovy and E.P. Xing, Harnessing Deep Neural Networks with Logic Rules,54th Annual Meeting of the Association for Computational Linguistics.(ACL 2016). (Recipient of the OUTSTANDING PAPER Award) (arXiv:1603.06318, communicated 21 Mar 2016.)

• H. Zhang, Z. Hu, Y. Deng, M. Sachan, Z. Yan and E.P. Xing, Learning Concept Taxonomies from Multi-modal Data,54th Annual Meeting of the Association for Computational Linguistics.(ACL 2016).

• M. Sachan and E.P. Xing, Machine Comprehension using Rich Semantic Representations,54th Annual Meeting of the Association for Computational Linguistics.(ACL 2016).

• M. Sachan and E.P. Xing, Science Question Answering using Instructional Materials,54th Annual Meeting of the Association for Computational Linguistics.(ACL 2016).

• P. Xie, J. Kim, Y. Zhou, Q. Ho, A. Kumar, Y. Yu and E. P. Xing, Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting,Proceedings of the 32nd International Conference on Conference on Uncertainty in Artificial Intelligence(UAI 2016). (arXiv:1311.4780, communicated 19 Nov 2013.)

• Y. Tan, Z. Fan, G. Li, F. Wang, Z. Li, S. Liu, Q. Pan, Q. Ho and E. P. Xing, Scalable Time-Decaying Adaptive Prediction Algorithm,Proceedings of The 22nd ACM SIGKDD Conference on knowledge Discovery and Data Mining(KDD 2016).

• S. Lee, S. Kong and E. P. Xing, A Network-driven Approach for Genome-wide Association Mapping,the Twenty-fourth International Conference on Intelligence Systems for Molecular Biology (ISMB 2016). _Bioinformatics (2016) in press._[pdf]

• Y. Wang, W. Neiswanger, W. Dai, V. Sadhanala, S. Sra and E. P. Xing, Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms,The 33rd International Conference on Machine Learning.(ICML 2016).

• P. Xie, J. Zhu and E. P. Xing, Diversity-Promoting Bayesian Learning of Latent Variable Models,The 33rd International Conference on Machine Learning.(ICML 2016).

• Z. Hu, G. Luo, M. Sachan, Z. Nie and E.P. Xing, Grounding Topic Models with Knowledge Bases,The 25th International Joint Conference on Artificial Intelligence.(IJCAI 2016).

• J. Kim, Q. Ho, S. Lee, X. Zheng, W. Dai, G. Gibson and E.P. Xing, STRADS: A Distributed Frame- work for Scheduled Model Parallel Machine Learning,European Conference on Computer Systems, 2016(EuroSys 2016).

• H. Cui, H. Zhang, G. R. Ganger, P. B. Gibbons and E.P. Xing, GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server,European Conference on Computer Systems, 2016(EuroSys 2016).

• M. Law, Y. Yu, M. Gord and E. P. Xing, Closed-Form Training of Mahalanobis Distance for Supervised Clustering,Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2016).

• X. Chang, Y. Yu, Y. Yang and E. P. Xing, They Are Not Equally Reliable: Semantic Event Search using Differentiated Concept Classifiers.,Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2016).

• A. Wilson, Z. Hu, R. Salakhudinov and E. P. Xing Deep Kernel Learning,Proceedings of the 19th International Conference on Artificial Intelligence and Statistics.(AISTATS 2016). [Supplemental Material]

• Y. Zhou, Y. Yu, Y. Liang and E. P. Xing On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System,Proceedings of the 19th International Conference on Artificial Intelligence and Statistics.(AISTATS 2016).

• H. Cheng, Y. Yu, X.Zhang, E. P. Xing, and D. Schurmans Scalable and Sound Low-Rank Tensor Learning,Proceedings of the 19th International Conference on Artificial Intelligence and Statistics.(AISTATS 2016).

• A. Wilson, W. Herlands, S. Flaxman, H. Nickisch, W. Van Panhuis, D. Neill and E. P. Xing Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces,Proceedings of the 19th International Conference on Artificial Intelligence and Statistics.(AISTATS 2016). [Supplemental Material]

• A. Dubey, J. Oliva, A. Wilson, E. P. Xing, B. Poczos, and J. Schneider, Bayesian Nonparametric Kernel-Learning,Proceedings of the 19th International Conference on Artificial Intelligence and Statistics.(AISTATS 2016).

• M. Marchetti-Bowick, J. Yin, J. Howrylak and E. P. Xing, A time-varying group sparse additive model for genome-wide association studies of dynamic complex traits, Bioinformatics, 32 (19):btw347, 2016. [pdf]

• L. Song, H. Liu, A. Parikh, and E. P. Xing, Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Structure Learning,Journal of Machine Learning Research, 2016. (arXiv:1401.3940, communicated 16 Jan 2014.)

• Q. Ho, J. Yin and E. P. Xing, Latent Space Inference of Internet-Scale Networks, Journal of Machine Learning Research, 17(78):1 - 41, 2016.

• J. Howrylak, M. Moll, B. Raby, S. Weiss, W. Wu, and E. P. Xing, Gene Expression Profiling of Asthma Phenotypes Demonstrates Molecular Signatures of Atopy and Asthma Control, Journal of Allergy and Clinical Immunology, Volume 137, Issue 5, Pages 1390- 1397, 2016.

• S. Lee, A. Lozano, P. Kambadur, and E. P. Xing, An Efficient Nonlinear Regression Approach for Genome-wide Detection of Marginal and Interacting Genetic Variations, Journal of Computational Biology, 23(5):372 - 89, 2016.

• Z. Guo, Z. Zhang, E. P. Xing, and C. Faloutsos, Multimodal Data Mining in a Multimedia Database Based on Structured Max Margin Learning, ACM Transactions on Knowledge Discovery from Data, Volume 10 Issue 3, February 2016.

2015

• E. P. Xing, Q. Ho, W. Dai, J. Kim, J. Wei, S. Lee, X. Zheng, P. Xie, A. Kumar, and Y. Yu, Petuum: A new Platform for Distributed Machine Learning on Big Data,IEEE Transactions on Big Data, Volume:1 Issue:2, pp49 - 67, 2015. (arXiv:1312.7651, communicated 30 Dec 2013.)

• Bin Zhao and E. P. Xing, Sparse Output Coding for Scalable Visual Recognition,International Journal of Computer Vision, 119:60 - 75. [pdf]

• W. Wang, Y. Liang, Lixin Shen, and E. P. Xing, Nonparametric Decentralized Detection and Sparse Sensor Selection via Weighted Kernel,IEEE Transactions on Signal Processing, Volume:64, Issue:2, pp306 - 321, 2015.

• A. Martins, M. Figueiredo, P. Aguiar, N.A. Smith and E. P. Xing, AD3: Alternating Directions Dual Decomposition for MAP Inference in Graphical Models , Journal of Machine Learning Research, 16(Mar): 495-545, 2015. (arXiv:1212.6550, communicated 28 Dec 2012.)

• A. Wilson, C. Lucas, C. Dann and E. P. Xing, The Human Kernel,Advances in Neural Information Processing Systems 29 (eds. Daniel Lee and Masashi Sugiyama), MIT Press, 2015.(NIPS 2015).

• J. Wei, W. Dai, A. Qiao, H. Cui, Q. Ho, G. R. Ganger, P. B. Gibbons, G. A. Gibson, and E.P. Xing, Managed Communication and Consistency for Fast Data-Parallel Iterative Analytics,ACM Symposium on Cloud Computing, 2015(SoCC 2015). (Recipient of the Best Paper Award)

• E. P. Xing, Q. Ho, W. Dai, J. Kim, J. Wei, S. Lee, X. Zheng, P. Xie, A. Kumar, and Y. Yu, Petuum: A new Platform for Distributed Machine Learning on Big Data,Proceedings of The 21st ACM SIGKDD Conference on knowledge Discovery and Data Mining(KDD 2015).

• P. Xie, Y. Deng and E. P. Xing, Diversifying Restricted Boltzmann Machine for Document Modeling,Proceedings of The 21st ACM SIGKDD Conference on knowledge Discovery and Data Mining(KDD 2015).

• X. Zheng, Y. Yu and E. P. Xing, Linear Time Samplers for Supervised Topic Models using Compositional Proposals,Proceedings of The 21st ACM SIGKDD Conference on knowledge Discovery and Data Mining(KDD 2015).

• H. Zhang, G. Kim and E. P. Xing, Dynamic Topic Modeling for Monitoring Market Competition from Online Text and Image Data,Proceedings of The 21st ACM SIGKDD Conference on knowledge Discovery and Data Mining(KDD 2015).

• X. Chang, Y. Yang, A. G. Hauptmann, E. P. Xing and Y. Yu, Semantic Concept Discovery for Large- Scale Zero-Shot Event Detection,The 24th International Joint Conference on Artificial Intelligence.(IJCAI 2015).

• M. Sachan, E. Hovy and E.P. Xing, An Active Learning approach to Coreference Resolution,The 24th International Joint Conference on Artificial Intelligence.(IJCAI 2015).

• M. Sachan, A. Dubey, M.Richardson and E.P. Xing, Learning Answer-Entailing Structures for Machine Comprehension ,53rd Annual Meeting of the Association for Computational Linguistics.(ACL 2015). (Recipient of an Honorable Mentioning)

• Z. Hu, P. Huang, Y. Deng, Y. Gao and E. P. Xing, Entity Hierarchy Embedding,53rd Annual Meeting of the Association for Computational Linguistics.(ACL 2015).

• X. Chang, Y. Yu and E. P. Xing, Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM,The 32th International Conference on Machine Learning.(ICML 2015).

• Z. Hu, Q. Ho, A. Dubey and E. P. Xing, Large-scale Distributed Dependent Nonparametric Trees,The 32th International Conference on Machine Learning.(ICML 2015).

• Z. Hu, J. Yao, B. Cui and E. P. Xing, Community Level Diffusion Extraction,Proceedings of the 2015 ACM SIGMOD international conference on Management of data.(SIGMOD 2015).

• P. Xie, D. Yang and E. P. Xing, Incorporating Word Correlation Knowledge into Topic Modeling,Conference of the North American Chapter of the Association for Computational Linguistics.(NAACL 2015).

• J. Yuan, F. Gao, Q. Ho, W. Dai, J. Wei, X. Zheng, E. P. Xing, T. Liu and W. Ma, LightLDA: Big Topic Models on Modest Compute Clusters,Proceedings of the 24th international conference on World Wide Web.(WWW 2015). (arXiv:1412.1576, communicated 4 Dec 2014.)

• Y. Yu, X. Zheng, M. Marchetti-Bowick and E. P. Xing, Minimizing Nonconvex Non-Separable Functions,Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics.(AISTATS 2015).

• J. Oliva, W. Neiswanger, B. Poczos, E. P. Xing and J. Schneider, Fast Function to Function Regression,Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics.(AISTATS 2015).

• P. Xie and E. P. Xing, Integrating Image Clustering and Codebook Learning,Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.(AAAI 2015).

• P. Xie, Y. Pei, Y. Xie, and E. P. Xing, Mining User Interests from Personal Photos,Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.(AAAI 2015).

• W. Dai, A. Kumar, J. Wei. Q. Ho, G. Gibson and E. P. Xing, High-Performance Distributed ML at Scale through Parameter Server Consistency Models,Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.(AAAI 2015).(arXiv:1312.7869, communicated 30 Dec 2013.) [Supplemental Material]

• S. Lee, A. Lozano, P. Kambadur and E. P. Xing, An Efficient Nonlinear Regression Approach for Genome-Wide Detection of Marginal and Interacting Genetic Variations,19th International Conference on Research in Computational Molecular Biology.(RECOMB 2015).

• W. Wang, Y. Liang and E. P. Xing, Collective Support Recovery for Multi-Design Multi-Response Linear Regression,IEEE Transactions on Information Theory, VOL. 61, NO. 1, 2015.

2014

• J. Eisenstein, B. O’Connor, N. A. Smith, and E. P. Xing, Diffusion of Lexical Change in Social Media,PLoS One, volume 9, Issue 11, e113114, 2014. (arXiv:1210.5268, communicated 18 Oct 2012.) [pdf preprint]

• S. Lee, J. K. Kim, X. Zheng, Q. Ho, G. A. Gibson, and E. P. Xing, On Model Parallelization and Scheduling Strategies for Distributed Machine Learning,Advances in Neural Information Processing Systems 28 (eds. Corinna Cortes and Neil Lawrence), MIT Press, 2014.(NIPS 2014).(arXiv:1312.5766, communicated 19 Dec 2013.) [Supplemental Material]

• A. Dubey, Q. Ho, S.Williamson, and E. P. Xing, Dependent Nonparametric Trees for Dynamic Hierarchical Clustering,Advances in Neural Information Processing Systems 28 (eds. Corinna Cortes and Neil Lawrence), MIT Press, 2014.(NIPS 2014). [Supplemental Material]

• A. P. Parikh, A. Saluja, C. Dyer and E. P. Xing, Language Modeling with Power Low Rank Ensembles,Proceeding of the 2014 Conference on Empirical Methods on Natural Language Processing.(EMNLP 2014). (Recipient of the runner-up for BEST PAPER Award) (arXiv:1312.7077, communicated 26 Dec 2013.) [Supplemental Material]

• W. Neiswanger, C. Wang and E. P. Xing, Asymptotically Exact, Embarrassingly Parallel MCMC,Proceedings of the 30th International Conference on Conference on Uncertainty in Artificial Intelligence(UAI 2014). (arXiv:1311.4780, communicated 19 Nov 2013.)

• W. Neiswanger, C. Wang, Q. Ho and E. P. Xing, Modeling Citation Networks using Latent Random Offsets,Proceedings of the 30th International Conference on Conference on Uncertainty in Artificial Intelligence(UAI 2014).

• A. Dubey, S.Williamson and E. P. Xing, Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models,Proceedings of the 30th International Conference on Conference on Uncertainty in Artificial Intelligence**(UAI 2014).**[Supplemental Material]

• H. Cui, A. Tumanov, J. Wei, L. Xu, W. Dai, J. Haber-Kucharsky, Q. Ho, G. R. Ganger, P. B. Gibbons, G. A. Gibson, and E.P. Xing, Exploiting Iterative-ness for Parallel ML Computations,ACM Symposium on Cloud Computing, 2014(SoCC 2014).

• H. Cui, J. Cipar, Q. Ho, J-K Kim, S. Lee, A. Kumar, J. Wei, W. Dai, G. R. Ganger, P. B. Gibbons, G. A. Gibson, and E. P. Xing, Exploiting Bounded Staleness to Speed up Big Data Analytics,USENIX Annual Technical Conference, June 19-20, 2014. Philadelphia, PA (ATC 2014).

• A. Parikh, R. Curtis, I. Kuhn, S. Becker, M. Bissell, E. P. Xing and W. Wu, Network Analysis of Breast Cancer Progression and Reversal Using a Tree-evolving Network Algorithm,PLoS Computational Biology, Volume 10, Issue 7, e1003713, 2014. [pdf preprint].

• E. P. Xing, R. Curtis, G. Schoenherr, S. Lee, J. Yin, K. Puniyani, W. Wu, P. Kinnaird, GWAS in a Box: Statistical and Visual Analytics of Structured Associations via GenAMap,PLoS One, Volume 9, Issue 6, e97524, 2014. [pdf preprint].

• S. Shringarpure and E. P. Xing, Effects of Sample Selection Bias on the Accuracy of Population Structure and Ancestry Inference,Genes, Genomes, Genetics, vol. 4 no. 5 901-911, 2014, [pdf preprint].

• D. Yogatama, C. Wang, B.R. Routledge, N.A. Smith and E. P. Xing, Dynamic Language Models for Streaming Text, Transactions of the Association for Computational Linguistics 2:181-192, 2014 (TACL 2014).

• A. Parikh, S. Cohen and E. P. Xing, Spectral Unsupervised Parsing with Additive Tree Metrics,The 52nd Annual Meeting of the Association for Computational Linguistics (ACL 2014).

• G. Kim and E. P. Xing, Reconstructing Storyline Graphs for Image Recommendation from Web Community Photos,Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2014).

• G. Kim, L. Sigal and E. P. Xing, Jointly Summarizing Large-Scale Web Images and Videos for the Storyline Reconstruction,Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2014).

• B. Zhao and E. P. Xing, Quasi Real-Time Summarization for Consumer Videos,Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2014).

• B. Zhao and E. P. Xing, Hierarchical Feature Hashing for Fast Dimensionality Reduction,Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2014).

• M. Kolar, H. Liu and E. P. Xing, Graph Estimation From Multi-attribute Data , Journal of Machine Learning Research, 15:1713-1750, 2014. (arXiv:1210.7665, communicated 29 Oct 2012.)

• J. Zhu, N. Chen, and E. P. Xing, Bayesian Inference with Posterior Regularization, and applications to Infinite Latent SVMs , Journal of Machine Learning Research, 15:1799-1847, 2014. (arXiv:1210.1766)

• A. Kumar, A. Beutel, Q. Ho and E. P. Xing, Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data,Proceedings of the 17th International Conference on Artificial Intelligence and Statistics(AISTATS 2014).

• J. B. Oliva, W. Neiswanger, B. Poczos, J. Schneider and E. P. Xing, Fast Distribution To Real Regression,Proceedings of the 17th International Conference on Artificial Intelligence and Statistics(AISTATS 2014).

• W. Neiswanger, F. Wood and E. P. Xing, The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling,Proceedings of the 17th International Conference on Artificial Intelligence and Statistics(AISTATS 2014).

• A. Beutel, A. Kumar, E. E. Papalexakis, P. P. Talukdar, C. Faloutsos and E. P. Xing, FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop,Proceedings of The 14th SIAM International Conference on Data Mining(SDM 2014).

• G. Kim and E. P. Xing, Visualizing Brand Associations from Web Community Photos,Proceedings of The 7th ACM International Conference on Web Search and Data Mining(WSDM 2014). [software and project page]

• M. Sachan, A. Dubey, S. Srivastava, E. P. Xing and Eduard Hovy, Spatial Compactness meets Topical Consistency: Jointly modeling Links and Content for Community Detection ,Proceedings of The 7th ACM International Conference on Web Search and Data Mining(WSDM 2014).

• A. P. Parikh, W. Wu, and E. P. Xing, Robust Reverse Engineering of Dynamic Gene Networks under Sample Heterogeneity, Pacific Symposium on Biocomputing 2014 (PSB 2014).

2013

• Q. Ho, J. Cipar, H. Cui, J.-K. Kim, S. Lee, P. B. Gibbons, G. Gibson, G. R. Ganger and E. P. Xing, More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server, Advances in Neural Information Processing Systems 27 (NIPS 2013). [Supplemental Material]

• S. Williamson, S. N. MacEachern and E. P. Xing, Restricting exchangeable nonparametric distributions, Advances in Neural Information Processing Systems 27 (NIPS 2013). [Supplemental Material] (arXiv:1209.1145)

• J. Yin, Q. Ho and E. P. Xing, A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks, Advances in Neural Information Processing Systems 27 (NIPS 2013). [Supplemental Material]

• C. Wang, X. Chen, A. Smola and E. P. Xing, Variance Reduction for Stochastic Gradient Optimization, Advances in Neural Information Processing Systems 27 (NIPS 2013). [Supplemental Material]

• G. Kim and E. P. Xing, Discovering Pictorial Brand Associations from Large-Scale Online Image Data,Proceedings of The 7th ACM International Conference on Web Search and Data Mining(lsviscom2013). [software and project page] (Recipient of the BEST PAPER Award)

• K Puniyani and E. P. Xing, GINI:From ISH images to Gene Interaction Networks,PLoS Computational Biology, 9(10): e1003227, 2013 [pdf preprint].

• M. Yamada, W. Jitkrittum, L. Sigal, E. P. Xing and M. Sugiyama, High-Dimensional Feature Selection by Feature-Wise Non-Linear Lasso,Neural Computation, Vol. 26, No. 1, Pages 185-20, 2013. [pdf preprint] (arXiv:1202.0515)

• P. Xie and E. P. Xing, Integrating Document Clustering and Topic Modeling,Proceedings of the 29th International Conference on Conference on Uncertainty in Artificial Intelligence(UAI 2013).

• T. Bahadori, Y. Liu and E. P. Xing, Fast Structure Learning in Generalized Stochastic Processes with Latent Factors,Proceedings of The 19th ACM SIGKDD Conference on knowledge Discovery and Data Mining(KDD 2013).

• P. Xie and E. P. Xing, Multi-Modal Distance Metric Learning,23rd International Joint Conference on Artificial Intelligence(IJCAL 2013).

• A. Ahmed and E. P. Xing, Scalable Dynamic Nonparametric Bayesian Models of Content and Users,23rd International Joint Conference on Artificial Intelligence**(IJCAL 2013).**[Invited to Sister Conference Best Papers Track, based on KDD12 best Ph.D. dissertation award]

• J. Cipar, Q. Ho, J.-K. Kim, S. Lee, G. R. Ganger, G. Gibson, K. Keeton and E. P. Xing, Solving the straggler problem with bounded staleness,The 14th Workshop on Hot Topics in Operating Systems (HotOS XIV).

• R.E. Curtis, S. Kim, J. L. Woolford, W. Xu and E. P. Xing, Structured association analysis leads to insight into Saccharomyces cerevisiae gene regulation by finding multiple contributing eQTL hotspots associated with functional gene modules, BMC Genomics 2013, vol. 14, no. 196, 2013. [pdf]

• G. Kim and E. P. Xing, Jointly Aligning and Segmenting Multiple Web Photo Streams for the Inference of Collective Photo Storylines,Proceedings of 26th IEEE Conference on Computer Vision and Pattern Recognition**(CVPR 2013).**[software and project page][Supplemental Material]

• B. Zhao and E. P. Xing, Sparse Output Coding for Large-scale Visual Recognition,Proceedings of 26th IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2013).

• W. Wang, Y. Liang and E. P. Xing, Block Regularized Lasso for Multivariate Multi-Response Linear Regression,Proceedings of the 16th International Conference on Artifical Intelligence and Statistics (AISTAT 2013).

• M. Kolar, H. Liu and E. P. Xing, Markov Network Estimation From Multi-attribute Data,The 30th International Conference on Machine Learningy (ICML 2013).

• L. Song, M. Ishteva, A.P. Parikh, H. Park and E. P. Xing, Hierarchical Tensor Decomposition for Latent Tree Graphical Models,The 30th International Conference on Machine Learningy (ICML 2013).

• R. Ranganath, C. Wang, D. Blei and E. P. Xing, An adaptive learning rate for stochastic variational inference,The 30th International Conference on Machine Learningy (ICML 2013).

• A. Dubey, S. Williamson and E. P. Xing, Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models,The 30th International Conference on Machine Learningy (ICML 2013). [Supplemental Material]

• K Puniyani and E. P. Xing, NP-MuScL: Unsupervised global prediction of interaction networks from multiple data sources,Proceedings of the Seventeenth Annual International Conference on Research in Computational Molecular Biology (RECOMB2013). [software]

• A. Dubey, S. Williamson and E. P. Xing, A nonparametric mixture model for topic modeling over time, Proceedings of The Thirteenth SIAM International Conference on Data Mining(SDM2013). (arXiv:1208.4411, communicated 22 Aug 2012.)

• G. Kim and E. P. Xing, Time-Sensitive Web Image Ranking and Retrieval via Dynamic Multi-Task Regression,Proceedings of The 6th ACM International Conference on Web Search and Data Mining(WSDM 2013). [software and project page]

2012

• M. Kolar and E. P. Xing, Estimating Networks With Jumps, Electronic Journal of Statistics Vol. 6 (2012) 2069-2106. (arXiv:1012.3795, communicated 17 Dec 2010.)

• Q. Ho, J. Yin and E. P. Xing, On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks, Advances in Neural Information Processing Systems 26 (NIPS 2012). [Supplemental Material] [software]

• Q. Jiang, J. Zhu, M. Sun and E. P. Xing, Monte Carlo Methods for Maximum Margin Supervised Topic Models, Advances in Neural Information Processing Systems 26 (NIPS 2012). [Supplemental Material] [software]

• K. Fukumasu, K. Eguchi and E. P. Xing, Symmetric Correspondence Topic Models for Multilingual Text Analysis, Advances in Neural Information Processing Systems 26 (NIPS 2012).

• K. Puniyani and E. P. Xing, Inferring gene interaction networks from ISH images via kernelized graphical models, Proceeding of the 13th European Conference of Computer Vision (ECCV 2012).

• A. Parikh, L. Song, M. Ishteva, G. Teodoru and E. P. Xing, A Spectral Algorithm for Latent Junction Trees, Proceedings of the 27th International Conference on Conference on Uncertainty in Artificial Intelligence (UAI 2012). [Supplemental Material]

• J. Zhu, A. Ahmed, and E. P. Xing, MedLDA: Maximum Margin Supervised Topic Models, Journal of Machine Learning Research 13 (2012) 2237-2278. (arXiv:0912.5507)

• Q. Ho, A. Parikh and E. P. Xing, A Multiscale Community Blockmodel for Network Exploration,Journal of American Statistical Association, volume 107, issue 499, 916-934, 2012. [pdf preprint]

• K. Sohn, Z. Ghahramani and E. P. Xing, Robust Estimation of Local Genetic Ancestry in Admixed Populations Using a Non-parametric Bayesian Approach, Genetics, vol 191, no. 4, 2012. [pdf preprint] [Supplemental Material] [software]

• R.E. Curtis, J. Xiang, A. Parikh, P. Kinnaird and E. P. Xing, Enabling dynamic network analysis through visualization in TVNViewer, BMC Bioinformatics, vol. 13, no. 204, 2012. [pdf] [software]

• R.E. Curtis, A. Goyal and E. P. Xing, Enhancing the usability and performance of structured as- sociation mapping algorithms using automation, parallelization, and visualization in the GenAMap software system, BMC Genetics, vol. 13, no. 24, 2012. [pdf] [software]

• J. Hu, Y. Liang and E. P. Xing, Nonparametric Decentralized Detection Based on Weighted Count Kernel,Proceedings of the 2012 IEEE International Symposium on Information Theory (ISIT 2012).

• J. Yin, X. Chen and E. P. Xing, Group Sparse Additive Models,The 29th International Conference on Machine Learningy (ICML 2012).

• M. Kolar and E. P. Xing, Consistent Covariance Selection From Data With Missing Values,The 29th International Conference on Machine Learningy (ICML 2012).

• G. Kim, L. Fei-Fei and E. P. Xing, Web Image Prediction Using Multivariate Point Processes,Proceedings of The 18th ACM SIGKDD Conference on knowledge Discovery and Data Mining(KDD 2012). [software and project page]

• G. Kim and E. P. Xing, On Multiple Foreground Cosegmentation,Proceedings of 25th IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2012). [Supplemental Material] [software and project page]

• S. Lee and E. P. Xing, Leveraging Input and Output Structures For Joint Mapping of Epistatic and Marginal eQTLs,the Twenties International Conference on Intelligence Systems for Molecular Biology (ISMB 2012). _Bioinformatics (2012) 28 (12): i137-i146._[pdf]

• Y. Zhang, D.-Y. Yeung and E. P. Xing, Supervised Probabilistic Robust Embedding with Sparse Noise,Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2012).

• N. Chen, J. Zhu, and E. P. Xing, Predictive Subspace Learning for Multi-view Data: a Large Margin Approach, IEEE Transaction on Pattern Analysis and Machine Intelligence, 2012. [Supplemental Material]

• S. Kim and E. P. Xing, Tree-Guided Group Lasso for Multi-Response Regression with Structured Sparsity, with applications to eQTL Mapping,Annals of Applied Statistics, Volume 6, Number 3 (2012), 1095-1117. [pdf]

• X. Chen, Q. Lin, S. Kim, J. Carbonell and E. P. Xing, Smoothing Proximal Gradient Method for General Structured Sparse Regression, Annals of Applied Statistics, Volume 6, Number 2 (2012), 719-752. [pdf]

• J. Eisenstein, D.H. Chau, A. Kittur, and E. P. Xing, TopicViz: Semantic Navigation of Document Collections, Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, Work-in-Progress Paper (CHI 2012).

• Q. Ho, J. Eisenstein and E. P. Xing, Document Hierarchies from Text and Links, Proceedings of the International World Wide Web Conference (WWW 2012).

• R.E. Curtis, J. Yin, P. Kinnaird and E. P. Xing, Finding Genome-Transcriptome-Phenome Association With Structured Association Mapping And Visualization In GenAMap, Pacific Symposium on Biocomputing 2012 (PSB 2012). [software and project page]

2011

• L. Song, A. Parikh and, and E. P. Xing, Kernel Embeddings of Latent Tree Graphical Models, Advances in Neural Information Processing Systems 25 (NIPS 2011). [Supplemental Material]

• J. Zhu, N. Chen and E. P. Xing, Infinite Latent SVM for Classification and Multi-task Learning, Advances in Neural Information Processing Systems 25 (NIPS 2011).

• B. Zhao, L. Fei-Fei and E. P. Xing, Large-Scale Category Structure Aware Image Categorization, Advances in Neural Information Processing Systems 25 (NIPS 2011).

• X. Chen, Q. Lin, S. Kim, J. Carbonell and E. P. Xing, Smoothing Proximal Gradient Method for General Structured Sparse Learning, Proceedings of the 27th International Conference on Conference on Uncertainty in Artificial Intelli- gence (UAI 2011).

• J. Zhu and E. P. Xing, Sparse Topical Coding, Proceedings of the 27th International Conference on Conference on Uncertainty in Artificial Intelli- gence (UAI 2011).

• G. Kim, E. P. Xing, L. Fei-Fei and T. Kanade, Distributed Cosegmentation via Submodular Optimization on Anisotropic Diffusion,Proceedings of 13th International Conference on Computer Vision**(ICCV 2011).**[Supplemental Material] [software and project page]

• R.E. Curtis, P. Kinnaird and E. P. Xing, GenAMap: Visualization Strategies for Structured Association Mapping, 1st IEEE Symposium on Biological Data Visualization, 2011. [software]

• R.E. Curtis, A. Yuen, L. Song, A. Goyal; and E. P. Xing, TVNViewer: An interactive visualization tool for exploring networks that change over time or space, Bioinformatics 2011. [software]

• J. Zhu, N. Lao, N. Chen and E. P. Xing, Conditional Topical Coding: an Efficient Topic Model Conditioned on Rich Features, Proceedings of The 17th ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2011).

• J. Zhu, N. Chen and E. P. Xing, Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines,The 28th International Conference on Machine Learningy (ICML 2011).

• A.F.T. Martins, M.A.T. Figueiredo, P.M.Q. Aguiar, N.A. Smith and E. P. Xing, An Augmented Lagrangian Approach to Constrained MAP Inference,The 28th International Conference on Machine Learningy (ICML 2011).

• A. Parikh, L. Song and E. P. Xing, A Spectral Algorithm for Latent Tree Graphical Models,The 28th International Conference on Machine Learningy (ICML 2011).

• H. Kamisetty, E. P. Xing, C. J. Langmead, Approximating Correlated Equilibria using Relaxations on the Marginal Polytope,The 28th International Conference on Machine Learningy (ICML 2011).

• J. Eisenstein, A. Ahmed and E. P. Xing, Sparse Additive Generative Models of Text,The 28th International Conference on Machine Learningy (ICML 2011).

• A. Parikh*, W. Wu* (equal contributing 1st author), R. Curtis and E. P. Xing, Reverse Engineering Tree-Evolving Gene Networks Underlying Developing Biological Lineages,the Nineteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2011). Bioinformatics, Volume 27, Issue 13Pp. i196-i204. [pdf] [software] (Recipient of the BEST PAPER Award)

• S. Shringarpure, D. Won and E. P. Xing, StructHDP: Automatic inference of number of clusters from admixed genotype data,the Nineteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2011). _Bioinformatics, Volume 27, Issue 13Pp. i324-i332._[pdf] [software]

• Q. Ho, A. Parikh, L. Song and E. P. Xing, Multiscale Community Blockmodel for Network Exploration,Proceedings of the 14th International Conference on Artifical Intelligence and Statistics (AISTAT 2011). [Supplemental Material]

• Q. Ho, L. Song and E. P. Xing, Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks,Proceedings of the 14th International Conference on Artifical Intelligence and Statistics (AISTAT 2011). [Supplemental Material]

• A.F.T. Martins, M.A.T. Figueiredo, P.M.Q. Aguiar, N.A. Smith and E. P. Xing, Online Learning of Structured Predictors with Multiple Kernels,Proceedings of the 14th International Conference on Artifical Intelligence and Statistics (AISTAT 2011). [Supplemental Material]

• M. Kolar and E. P. Xing, On Time Varying Undirected Graphs,Proceedings of the 14th International Conference on Artifical Intelligence and Statistics (AISTAT 2011). [Supplemental Material]

• A. Ahmed, Q. Ho, C. H. Teo, J. Eisenstein, A. Smola and E. P. Xing, Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text,Proceedings of the 14th International Conference on Artifical Intelligence and Statistics (AISTAT 2011). [Supplemental Material]

• B. Zhao, L. Fei-Fei and E. P. Xing, Online Detection of Unusual Events in Videos via Dynamic Sparse Coding, Proceeding of the 24th IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2011). Watch a movie of our detection demo on youtube!

• J. Eisenstein, N. Smith and E. P. Xing, Discovering Sociolinguistic Associations with Structured Sparsity,The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011).

• A. Ahmed, Q. Ho, J. Eisenstein, E. P. Xing, A. Smola and C. H. Teo, Unified Analysis of Streaming News, Proceedings of the International World Wide Web Conference (WWW 2011).

• S. Kim and E. P. Xing, Exploiting Genome Structure in Association Analysis,_Journal of Computational Biology, Volume: 18,Pages: 1-16, 2011._[Journal Link]

2010

• S. Lee, J. Zhu, and E. P. Xing, Adaptive Multi-Task Lasso: with application to eQTL detection, Advances in Neural Information Processing Systems 24 (NIPS 2010).

• N. Chen, J. Zhu, and E. P. Xing, Predictive Subspace Learning for Multi-view Data: a Large Margin Approach, Advances in Neural Information Processing Systems 24 (NIPS 2010).

• J. Zhu, J. Li, L. Fei-Fei, and E. P. Xing, Large Margin Learning of Upstream Scene Understanding Models, Advances in Neural Information Processing Systems 24 (NIPS 2010).

• J. Li, H. Su, E. P. Xing, and L. Fei-Fei, Object Bank: A High-Level Image Representation for Scene Classification and Semantic Feature Sparsification, Advances in Neural Information Processing Systems 24 (NIPS 2010).

• Q. Ho, A.P. Parikh, L. Song and E. P. Xing, Infinite Hierarchical MMSB Model for Nested Communities/Groups in Social Networks,Manuscript, arXiv:1010.1868, communicated Oct 2010.

• A.F.T. Martins, M.A.T. Figueiredo, P.M.Q. Aguiar, N.A. Smith and E. P. Xing, Online Multiple Kernel Learning for Structured Prediction,Manuscript, arXiv:1005.2770, communicated Oct 2010.

• A. Ahmed and E. P. Xing, Staying Informed: Supervised and Semi-Supervised Multi-view Topical Analysis of Ideological Perspective, 2010 Conference on Empirical Methods on Natural Language Processing (EMNLP 2010).

• J. Eisenstein, B. O'Connor, N. A. Smith, and E. P. Xing, A Latent Variable Model for Geographic Lexical Variation, 2010 Conference on Empirical Methods on Natural Language Processing (EMNLP 2010).

• A. F. T. Martins, N. A. Smith, E. P. Xing, M. Figueiredo, and P. Aguiar, Turbo Parsers: Dependency Parsing by Approximate Variational Inference, 2010 Conference on Empirical Methods on Natural Language Processing (EMNLP 2010).

• S. Hanneke, W. Fu and E. P. Xing, Discrete Temporal Models of Social Networks, Electronic Journal of Statistics Vol. 4 (2010) 585-605. (arXiv:0908.1258, communicated August 2009.)

• A. Ahmed and E. P. Xing, Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream, Proceedings of the 26th International Conference on Conference on Uncertainty in Artificial Intelli- gence (UAI 2010).

• G. Kim, E. P. Xing, and A. Torralba, Modeling and Analysis of Dynamic Behaviors of Web Image Collections, Proceeding of the 12th European Conference of Computer Vision (ECCV 2010).

• J. Zhu, N. Lao and E. P. Xing, Grafting-Light: Fast, Incremental Feature Selection and Structure Learning of Markov Random Fields, Proceedings of The 16th ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2010).

• M. Kolar, A. Parikh and E. P. Xing, On Sparse Nonparametric Conditional Covariance Selection,The 27th International Conference on Machine Learningy (ICML 2010).

• S. Kim and E. P. Xing, Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity,The 27th International Conference on Machine Learningy (ICML 2010).

• J. Zhu and E. P. Xing, Conditional Topic Random Fields,The 27th International Conference on Machine Learningy (ICML 2010).

• K. Puniyani, S. Kim and E. P. Xing, Multi-Population GWA Mapping via Multi-Task Regularized Regression,the Eighteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2010). Bioinformatics 2010 26(12):i208-i216.

• K. Puniyani, C. Faloutsos and E. P. Xing, SPEX2 : Automated Concise Extraction of Spatial Gene Expression Patterns from Fly Embryo ISH Images,the Eighteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2010). Bioinformatics 2010 26(12):i47-i56.

• M. Kolar and E. P. Xing, Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit,Proceedings of the 13th International Conference on Artifical Intelligence and Statistics (AISTAT 2010). [Supplemental Material]

• S. Lee, E. P. Xing and M. Brudno, MoGUL: Detecting Common Insertions and Deletions in a Population,Proceedings of the Fourteenth Annual International Conference on Research in Computational Molecular Biology (RECOMB2010). [software]

2009

• J. Zhu, A. Ahmed, and E. P. Xing, MedLDA: A General Framework of Maximum Margin Supervised Topic Models,Manuscript, arXiv:0912.5507, communicated Dec 2009.

• E.P. Xing, W. Fu, and L. Song, A State-Space Mixed Membership Blockmodel for Dynamic Network Tomography, Annals of Applied Statistics, Vol. 4, No. 2, 535 - 566, 2010. (earlier version appeared in arXiv:0901.0135)

• M. Kolar, L. Song, A. Ahmed, and E. P. Xing, Estimating Time-Varying Networks , Annals of Applied Statistics, Vol. 4, No. 1, 94 - 123, 2010. (earlier version appeared in arXiv:0812.5087)

• M. Kolar, L. Song and E. P. Xing, Sparsistent Learning of Varying-coefficient Models with Structural Changes,Proceeding of the 23rd Neural Information Processing Systems, (NIPS 2009). [appendix available soon]

• L. Song, M. Kolar and E. P. Xing, Time-Varying Dynamic Bayesian Networks,Proceeding of the 23rd Neural Information Processing Systems, (NIPS 2009). [appendix available soon]

• X. Yang, S. Kim and E. P. Xing, Heterogeneous Multitask Learning with Joint Sparsity Constraints,Proceeding of the 23rd Neural Information Processing Systems, (NIPS 2009).

• S. Kim and E. P. Xing, Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity,Manuscript, arXiv:0909.1373, communicated September 2009.

• M. Kolar and E. P. Xing, Sparsistent Estimation of Time-Varying Discrete Markov Random Fields,Manuscript, arXiv:0907.2337, communicated July 2009.

• J. Zhu and E. P. Xing, Maximum Entropy Discrimination Markov Networks, Journal of Machine Learning Research, 10(Nov):2531-2569, 2009.

• S. Kim and E. P. Xing, Statistical Estimation of Correlated Genome Associations to a Quantitative Trait Network, PLoS Genetics 5(8): e1000587, 2009. [pdf] [software]

• A. Ahmed and E. P. Xing, TESLA: Recovering Time-Varying Networks of Dependencies in Social and Biological Studies, Proc. Natl. Acad. Sci. USA, vol. 106, no. 29, 11878-11883. [pdf] [supplemental material] [software]

• J. Zhu, A. Ahmed and E. P. Xing, MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification,The 26th International Conference on Machine Learningy (ICML 2009).

• J. Zhu and E. P. Xing, On Primal and Dual Sparsity of Markov Networks,The 26th International Conference on Machine Learningy (ICML 2009).

• W. Fu, L. Song and E. P. Xing, Dynamic Mixed Membership Block Model for Evolving Networks,The 26th International Conference on Machine Learningy (ICML 2009).

• A. Martins, N. Smith and E. P. Xing, Polyhedral Outer Approximations with Application to Natural Language Parsing,The 26th International Conference on Machine Learningy (ICML 2009).

• A. Martins, N. Smith and E. P. Xing, Concise Integer Linear Programming Formulations for Dependency Parsing,The 47th Annual Meeting of the Association for Computational Linguistics (ACL 2009). (Recipient of the BEST PAPER Award)

• J. Zhu, E.P. Xing and B. Zhang, Primal Sparse Max-Margin Markov Networks,The 15th ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2009).

• A. Ahmed, E.P. Xing, W. Cohen, and R. Murphy, Structured Correspondence Topic Models for Mining Captioned Figures in Biological Literature,The 15th ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2009).

• S. Kim, K. Sohn and E. P. Xing, A Multivariate Regression Approach to Association Analysis of Quantitative Trait Network,The Seventeenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2009). Bioinformatics 2009 25(12):i204-i212.

• L. Song, M. Kolar and E. P. Xing, KELLER: Estimating Time-Evolving Interactions Between Genes,The Seventeenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2009). Bioinformatics 2009 25(12):i128-i136. [software]

• W. Fu, P. Ray and E. P. Xing, DISCOVER: A feature-based discriminative method for motif search in complex genomes,The Seventeenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2009). _Bioinformatics 2009 25(12):i321-i329._[software]

• S. Shringarpure and E. P. Xing, mStruct: Inference of Population Structure in Light of Both Genetic Admixing and Allele Mutations, Genetics, Vol 182, issue 2, 2009. [pdf] [software]

• J. A. Howrylak, T. Dolinay, L. Lucht, Z. Wang, D. C. Christiani, J. M. Sethi1, E. P. Xing, M. P. Donahoe and A. M. K. Choi, Discovery of the gene signature for acute lung injury in patients with sepsis, Physiol. Genomics 37: 133-139, 2009.

• S. Hanneke and E. P. Xing, Network Completion and Survey Sampling,Proceedings of the 12th International Conference on Artifical Intelligence and Statistics (AISTAT 2009)

• K. Sohn and E. P. Xing, A Hierarchical Dirichlet Process Mixture Model For Haplotype Reconstruction From Multi-Population Data,Annals of Applied Statistics, Vol. 3, no. 2, 791-821, 2009. [pdf] [software]

• A. Martins, N. A. Smith, E. P. Xing, M. Figueiredo, and P. Aguiar, Nonextensive Information Theoretic Kernels on Measures,Journal of Machine Learning Research, 2009, Vol 10, pp935-975.

2008

• A. Ahmed, L. Song, and E. P. Xing, Time-Varying Networks: Recovering Temporally Rewiring Genetic Networks During the Life Cycle of Drosophila melanogaster,Manuscript, arXiv:0901.0138.

• W. Fu, L. Song, and E. P. Xing, A State-Space Mixed Membership Blockmodel for Dynamic Network Tomography,Manuscript, arXiv:0901.0135.

• M. Kolar, L. Song, A. Ahmed, and E. P. Xing, Estimating Time-Varying Networks ,Manuscript, arXiv:0812.5087.

• S. Kim, K. Sohn, and E. P. Xing, A Multivariate Regression Approach to Association Analysis of Quantitative Trait Network ,Manuscript, arXiv:0811.2026.

• M. Kolar and E. P. Xing, Improved Estimation of High-dimensional Ising Models ,Manuscript, arXiv:0811.1239.

• J. Zhu, E. P. Xing, and B. Zhang, Partially Observed Maximum Entropy Discrimination Markov Networks,Proceeding of the 22nd Neural Information Processing Systems, (NIPS 2008).

• E. Airoldi, D. Blei, S. Fienberg, and E. P. Xing, Mixed Membership Stochastic Blockmodel, Journal of Machine Learning Research, 9(Sep):1981--2014, 2008.
  A shorter version of this paper appears in Proceeding of the 22nd Neural Information Processing Systems, (NIPS 2008).

• A. F.T. Martins, D. Das, N. A. Smith, and E. P. Xing, Stacking Dependency Parser,Proceedings of Conference on Empirical Methods in Natural Language Processing, (EMNLP 2008).

• A. Ahmed, K. Yu, W. Xu, Y. Gong, and E. P. Xing, Training Hierarchical Feed-forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks ,Proceeding of the 10th European Conference of Computer Vision,(ECCV 2008).

• W.-H. Lin, E. P. Xing, and A. Hauptmann, A Joint Topic and Perspective Model for Ideological Discourse,Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, (ECML/PKDD 2008).

• R. Nallapati, A. Ahmed, E. P. Xing, and W. Cohen, Sparse Feature Joint Latent Topic Models for Text and Citations ,Proceedings of The Fourteen ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (KDD 2008).

• S. Kim and E. P. Xing, Structured Feature Selection in High-Dimensional Space via Block Regularized Regression,Proceedings of the 24th International Conference on Conference on Uncertainty in Artificial Intelligence, (UAI 2008).

• J. Zhu, E. P. Xing, B. Zhang, Laplace Maximum Margin Markov Networks ,Proceedings of the 25th International Conference on Machine Learning (ICML 2008). (A longer version is available in CMU-MLD Technical Report 08-104with the same title.)

• A. Martins, M. Figueiredo, P. Aguiar, N. A. Smith and E. P. Xing, Nonextensive Entropic Kernels,Proceedings of the 25th International Conference on Machine Learning (ICML 2008). (A longer version is available soon in CMU-MLD Technical Report 08-106 with the same title.)

• S. Shringarpure and E. P. Xing, mStruct: A New Admixture Model for Inference of Population Structure in Light of Both Genetic Admixing and Allele Mutations ,Proceedings of the 25th International Conference on Machine Learning (ICML 2008). (A longer version is available soon in CMU-MLD Technical Report 08-105with the same title.)

• P. Ray, S. Shringarpure, M. Kolar and E. P. Xing, CSMET: Comparative Genomic Motif Detection via Multi-Resolution Phylogenetic Shadowing, PLoS Computational Biology, 4(6): e1000090. [pdf] [supplemental material] [software]

• H. Kamisetty, E. P. Xing, C. J. Langmead, Free Energy Estimates of All-atom Protein Structures Using Generalized Belief Propagation,Journal of Computational Biology, 15(7): 755-766, 2008. [pdf]

• F. Gou, L. Li, C. Faloutsos and E. P. Xing, C-DEM: A Multi-Modal Query System for Drosophila Embryo Databases, Proceedings of The 34th International Conference on Vary Large Data Bases (VLDB2008).

• A. Ahmed and E. P. Xing, Dynamic Non-Parametric Mixture Models and the Recurrent Chinese Restaurant Process, Proceedings of The Eighth SIAM International Conference on Data Mining (SDM2008).

• Z. Guo, Z. Zhang, E. P. Xing and C. Faloutsos, Semi-supervised Learning Based on Semiparametric Regularization, Proceedings of The Eighth SIAM International Conference on Data Mining (SDM2008).

• T. Lin, P. Ray, G. K. Sandve, S. Uguroglu, and E. P. Xing, BayCis: a Bayesian hierarchical HMM for cis-regulatory module decoding in metazoan genomes,Proceedings of the Twelfth Annual International Conference on Research in Computational Molecular Biology (RECOMB2008). [software]

• J. Yang, R. Yan, Y. Liu, and E. P. Xing, Harmonium Models for Video Classification,Journal of Statistical Analysis and Data Mining, Vol 1, issue 1, p23-37, 2008.

• W. Wu and E. P. Xing, A Survey of cDNA Microarray Normalization and a Comparison by k-NN Classification,in Methods in Microarray Normalization (Ed. S. Phillip), CRC Press. p81-120, 2008.

2007

• B. Zhao and E. P. Xing, HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation,Advances in Neural Information Processing Systems 20 (NIPS2007).

• L. Chang, N. Pollard, T. Michell and E. P. Xing, Feature selection for grasp recognition from optical markers,Proceedings of the 2007 IEEE/RSJ Intl. Conference on Intelligent Robots and Systems, 2007 (IROS2007).

• E. P. Xing and K. Sohn, A Nonparametric Bayesian Approach for Haplotype Reconstruction from Single and Multi-Population Data**,**CMU-MLD Technical Report 07-107.

• K. Sohn and E. P. Xing, Spectrum: Joint Bayesian Inference of Population Structure and Recombination Event,The Fifteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2007). [software]

• F. Guo, S. Hanneke, W. Fu and E. P. Xing, Recovering Temporally Rewiring Networks: A model-based approach,Proceedings of the 24th International Conference on Machine Learning (ICML 2007).

• L. Gu, E. P. Xing, and T. Kanade, Learning GMRF Structures for Spatial Priors,Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007).

• Z. Guo, Z. Zhang, E. P. Xing, C. Faloutsos, Enhanced Max Margin Learning on Multimodal Data Mining in a Multimedia Database,The Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007).

• Z. Guo, Z. Zhang, E. P. Xing, C. Faloutsos, A Max Margin Framework on Image Annotation and Multimodal Image Retrieval,IEEE International Conference on Multimedia & Expo (ICME 2007).

• E. P. Xing, M. Jordan, and R. Roded, Bayesian Haplotype Inference via the Dirichlet Process,Journal of Computational Biology, Volume 14, Number 3, Pp. 267-284, 2007.

• E. P. Xing and K. Sohn, Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space,Bayesian Analysis, Volunn 2, Number 2, 2007. A synopsis of this paper also appears in Bayesian Statistics 8, the Proc. Valencia / ISBA 8th World Meeting on Bayesian Statistics .

• A. Ahmed and E. P. Xing, On Tight Approximate Inference of Logistic-Normal Admixture Model,Proceedings of the Eleventh International Conference on Artifical Intelligence and Statistics (AISTAT 2007).

• J. Yang, Y. Liu, E. P. Xing and A. Hauptmann, Harmonium-Based Models for Semantic Video Representation and Classification ,Proceedings of The Seventh SIAM International Conference on Data Mining(SDM 2007). (Recipient of the BEST PAPER Award)

• H. Kamisetty, E. P. Xing, C. J. Langmead, Free Energy Estimates of All-atom Protein Structures Using Generalized Belief Propagation,The Eleventh Annual International Conference on Research in Computational Molecular Biology(RECOMB 2007).

• Y. Shi, F. Guo, W. Wu and E. P. Xing, GIMscan: A New Statistical Method for Analyzing Whole-Genome Array CGH Data,The Eleventh Annual International Conference on Research in Computational Molecular Biology(RECOMB 2007).

2006

• F. Guo, W. Fu, Y. Shi and E. P. Xing,Reverse engineering temporally rewiring gene networks**,The NIPS workshop on New Problems and Methods in Computational Biology(NIPS2006)**.

• F. Li, Y. Yang and E. P. Xing, Inferring regulatory networks using a hierarchical Bayesian graphical Gaussian model**,**CMU-MLD Technical Report 06-117.

• Y. Shi, F. Guo, W. Wu and E. P. Xing, GIMscan: A New Statistical Method for Analyzing Whole-Genome Array CGH Data**,**CMU-MLD Technical Report 06-115.

• E. P. Xing and K. Sohn, A New Nonparametric Bayesian Model for Genetic Inference in Open Ancestral Space**,**CMU-MLD Technical Report 06-111.

• K. Sohn and E. P. Xing, Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space, Advances in Neural Information Processing Systems 19 (NIPS2006). (full oral presentation, top 3%)

• J-Y Pan, A. Balan, E.P. Xing, A. Traina and C. Faloutsos, Automatic Mining of Fruit Fly Embryo Images, The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006).

• B. Zhao and E.P Xing, BiTAM: Bilingual Topic AdMixture Models for Word Alignment, The joint conference of the International Committee on Computational Linguistics and the Association for Computational Linguistics (ACL 2006).

• T. Lin, E.W. Myers and E.P. Xing, Interpreting Anonymous DNA Samples From Mass Disasters --- probabilistic forensic inference using genetic markers, Bioinformatics 22(14):e298-e306. (special issue for The Fourteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2006)).

• E.P. Xing, K. Sohn, M.I. Jordan and Y.W. Teh, Bayesian Multi-Population Haplotype Inference via a Hierarchical Dirichlet Process Mixture, Proceedings of the 23rd International Conference on Machine Learning (ICML 2006).

• S. Hanneke and E.P Xing, Discrete Temporal Models of Social Networks, In proceedings of the Workshop on Statistical Network Analysis_, the 23rd International Conference on Machine Learning_ (ICML-SNA 2006).

• G. Fan and E.P Xing, Bayesian Exponential Family Harmoniums, CMU-MLD Technical Report 06-103.

• E.M. Airoldi, D.M. Blei, S.E. Fienberg, E.P. Xing, Latent mixed-membership allocation models of relational and multivariate attribute data, Valencia & ISBA Joint World Meeting on Bayesian Statistics (2006).

• E.M Airodi, D.M. Blei, E.P. Xing and S.E. Fienberg, Mixed membership stochastic block models for relational data, with applications to protein-protein interactions, Proceedings of International Biometric Society-ENAR Annual Meetings (2006). (Recipient of the John Van Ryzin Award).

2005

• E.P. Xing, On Topic Evolution **.**CMU-CALD Technical Report 05-115.

• E.P. Xing, Dynamic Nonparametric Bayesian Models and the Birth-Death Process . CMU-CALD Technical Report 05-114.

• W. Wu, N. Dave, G.C. Tseng, T. Richards, E.P. Xing, and N. Kaminsky, Comparison of normalization methods for CodeLink Bioarray data . BMC Bioinformatics 2005, 6:309 (28 Dec, 2005).

• F. Li, Y. Yang and E. P. Xing, From Lasso regression to Feature vector machine, Advances in Neural Information Processing Systems 18 (NIPS2005).

• E. Airoldi, D. Blei, E.P. Xing and S. Fienberg, A Latent Mixed Membership Model for Relational Data. Workshop on Link Discovery: Issues, Approaches and Applications (LinkKDD-2005).

• E.P. Xing, R. Yan and A. G. Hauptmann, Mining Associated Text and Images with Dual-Wing Harmoniums. Uncertainty in Artificial Intelligence, 2005 (UAI2005).

• Y. Liu, E.P. Xing, and J. Carbonell, Predicting Protein Folds with Structural Repeats Using a Chain Graph Model. Proceedings of the 22st International Conference on Machine Learning (ICML2005).

• B. Zhao, E.P Xing, and A. Waibel, Bilingual Word Spectral Clustering for Statistical Machine Translation. ACL-Workshop-WPT, Ann Arbor MI, 2005.

2004

• E.P. Xing, Probabilistic graphical models and algorithms for genomic analysis. Ph.D. Thesis, University of California, Berkeley, July 2004.

• E.P. Xing, M.I Jordan and S. Russell, Graph partition strategies for generalized mean field inference. Uncertainty in Artificial Intelligence 20 (UAI2004), (eds. Meek and Halpern) AUAI Press, 602-610, 2004. [ps]

• E.P. Xing, R. Sharan and M.I Jordan, Bayesian Haplotype Inference via the Dirichlet Process. Proceedings of the 21st International Conference on Machine Learning (ICML2004), (eds. Greiner and Schuurmans), ACM Press, 879-886, [ps]. An earlier version of this paper also appeared as a book chapter in Lecture Notes in Bioinformatics, Special issue for 2nd RECOMB Satellite Workshop on Computational Methods for SNPs and Haplotypes, 2004. (ps).

• E.P. Xing and R. M. Karp, MotifPrototyper: a profile Bayesian model for motif family. Proc. Natl. Acad. Sci. vol. 101, no. 29, 10523-10528, 2004. [pnas site].

2003

• E.P. Xing, M.I. Jordan, and S. Russell, A generalized mean field algorithm for variational inference in exponential families, Uncertainty in Artificial Intelligence (UAI2003), (eds. Meek and Kjaelff) Morgan Kaufmann Publishers, 583-591, 2003. (This paper was awarded a Runner-up Best Student Paper Award.) (ps,code.)

• E.P. Xing and M.I Jordan, On semidefinite relaxation for normalized k-cut and connections to spectral clustering.Technical Report CSD-03-1265, Division of Computer Science, University of California, Berkeley, 2003. (ps)

• E.P. Xing, W. Wu, M.I. Jordan and R.M. Karp, LOGOS: A modular Bayesian model for de novo motif detection. Journal of Bioinformatics and Computational biology 2004, 2(1), 127-154. An earlier version of this paper was also presented in IEEE Computer Society Bioinformatics Conference, CSB2003.(ps)

• E.P. Xing, Feature Selection in Microarray Analysis, in D.P. Berrar, W. Dubitzky and M. Granzow (Eds.), A Practical Approach to Microarray Data Analysis, Kluwer Academic Publishers, 2003.

2002

• E.P. Xing, M.I. Jordan, R.M. Karp and S. Russell, A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences. Advances in Neural Information Processing Systems 16 (NIPS2002), (eds. Becker et al.) MIT Press, 2002.(ps) A long version with more biological results is available here.

• E.P. Xing, A.Y. Ng, M.I. Jordan and S. Russell, Distance Metric Learning, with application to Clustering with side-information,Advances in Neural Information Processing Systems 16 (NIPS2002),(eds. Becker et al.) MIT Press, 521-528, 2002. (ps,data,code.)

2001

• E.P. Xing, M.I. Jordan and R.M. Karp, Feature selection for high-dimensional genomic microarray data, Proceedings of the Eighteenth International Conference on Machine Learning, (ICML2001).(ps)

• E.P. Xing and R.M. Karp, CLIFF: Clustering of High-Dimensional Microarray Data via Iterative Feature Filtering Using Normalized Cuts, Bioinformatics 17 Suppl 1:S306-15, 2001. (special issue for The Ninth International Conference on Intelligence Systems for Molecular Biology, (ISMB 2001)).

• E.P. Xing, D. Wolf, I. Dubchak, S. Spengler, M. Zorn, C. Kulikowski, I. Muchnik, Automatic discovery of sub-molecular sequence domains in multi-aligned sequences: a dynamic programming algorithm for multiple alignment segmentation, Journal of Theoretical Biology, 21;212(2):129-39, 2001.

pre2000

• E.P. Xing, C. Kulikowski, I. Muchnik, I. Dubchak, D. Wolf, S. Spengler and M. Zorn, Analysis of ribosomal RNA sequences by combinatorial clustering, Proceedings, The Seventh International Conference on Intelligence Systems for Molecular Biology (ISMB99),(Eds. T. Lengauer et al.) AAAI/MIT Press, Menlo Park, CA. P. 287-296, 1999.

• E.P. Xing, Y. Nie, Y-L Song, G-Y. Yang, C. C. Cai, L-D. Wang, C. S. Chung, Mechanisms of inactivation of p14ARF, p15INK4b and _p16INK4a_genes in human esophageal squamous cell carcinoma: _p14ARF_is potentially another inactivation hotspot within the 9p21 gene cluster,Clinical Cancer Research, 1999 Oct; 5(10): 2704-13.

• E. P. Xing, G-Y. Yang, L-D. Wang, C. S. Yang, Loss of Heterozygous of the Rb gene correlates with pRb protein expression and associates with p53 alteration in human esophageal cancer,Clinical Cancer Research, 1999 May; 5(5): 1231-40.

• E. P. Xing, Y. Nie, L-D. Wang, G-Y. Yang, C.S. Yang, Aberrant methylation of p16INK4a and deletion of p15INK4b are frequent events in human esophageal cancer, Carcinogenesis, 1999 Jan; 20(1): 77-84.

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