Stefano Ermon (original) (raw)

	Stefano Ermon Follow Me Associate Professor Department of Computer Science Stanford University Office: Gates Building #330 Phone: (650) 498-9942 Email: ermon AT cs.stanford.edu Group Website Research Blog

About Me

I am an Associate Professor in the Department of Computer Science at Stanford University. I am affiliated with the Artificial Intelligence Lab. I am also a fellow of the Woods Institute for the Environment. My research is in machine learning and generative AI. I like to develop principled methods motivated by concrete real-world applications and problems of broad societal relevance.

Teaching

• Winter 2022/2023: Probabilistic Graphical Models (CS 228)
• Winter 2021/2022: Probabilistic Graphical Models (CS 228)
• Fall 2021/2022: Deep Generative Models (CS 236)
• Fall 2021/2022: Data for Sustainable Development (CS 325B)
• Winter 2020/2021: Probabilistic Graphical Models (CS 228)
• Winter 2019/2020: Probabilistic Graphical Models (CS 228)
• Fall 2019/2020: Deep Generative Models (CS 236)

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Honors and Awards

• ICML 2024 Best Paper Award [Press]
• ICLR 2022 Outstanding Paper Award [Press]
• ICLR 2021 Outstanding Paper Award [Press]
• ISSNAF Young Investigator Award [Press]
• Sloan Research Fellowship [Press]
• Microsoft Research Faculty Fellowship [Press]
• Bloomberg Data Science Research Grant [Press]
• AFOSR Young Investigator Award [Press]
• IJCAI Computers and Thought Award [Press]
• ONR Young Investigator Award [Press]
• Hellman Fellowship [Press]
• AWS Machine Learning Research Award
• First Place, World Bank Big Data Innovation Challenge [Press]
• NSF CAREER Award
• Sony Faculty Innovation Award [Press]
• AAAI 2017 Outstanding Paper Award [Press]
• AAAI 2017 Best Student Paper Award (Computational Sustainability Track) [Press]
• 10 World Changing Ideas of 2016, Scientific American [Press]
• Finalist, NVIDIA Global Impact Award [Press]
• UAI 2013 Facebook Best Student Paper Award and Best Paper Award Runner-up
• CP 2010 Best Student Paper Award

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Publications

2025

• Joshua Kazdan, Hao Sun, Jiaqi Han, Felix Petersen, Frederick Vu, Stefano Ermon
  CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion [**PDF**]
  ICLR-25. To appear in Proc. 13th International Conference on Learning Representations, 2025.
• Jeremy Andrew Irvin, Emily Ruoyu Liu, Joyce C. Chen, Ines Dormoy, Jinyoung Kim, Samar Khanna, Zhuo Zheng, Stefano Ermon
  TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data [**PDF**]
  ICLR-25. To appear in Proc. 13th International Conference on Learning Representations, 2025.
• Juntong Shi, Minkai Xu, Harper Hua, Hengrui Zhang, Stefano Ermon, Jure Leskovec
  TabDiff: a Multi-Modal Diffusion Model for Tabular Data Generation [**PDF**]
  ICLR-25. To appear in Proc. 13th International Conference on Learning Representations, 2025.
• Minkai Xu, Tomas Geffner, Karsten Kreis, Weili Nie, Yilun Xu, Jure Leskovec, Stefano Ermon, Arash Vahdat
  Energy-Based Diffusion Language Models for Text Generation [**PDF**]
  ICLR-25. To appear in Proc. 13th International Conference on Learning Representations, 2025.
• Haowei Lin, Shanda Li, Haotian Ye, Yiming Yang, Stefano Ermon, Yitao Liang, Jianzhu Ma
  TFG-Flow: Training-free Guidance in Multimodal Generative Flow [**PDF**]
  ICLR-25. To appear in Proc. 13th International Conference on Learning Representations, 2025.
• Silas Alberti, Kenan Hasanaliyev, Manav Shah, Stefano Ermon
  Data Unlearning in Diffusion Models
  ICLR-25. To appear in Proc. 13th International Conference on Learning Representations, 2025.
• Minkai Xu, Jiaqi Han, Mingjian Jiang, Yuxuan Song, Stefano Ermon
  f-PO: Generalizing Preference Optimization with f-divergence Minimization
  AISTATS-25. To appear in Proc. 28th International Conference on Artificial Intelligence and Statistics, 2025.

2024

• Eric Nguyen, Michael Poli, Matthew G. Durrant, Brian Kang, Dhruva Katrekar, David B. Li, Liam J. Bartie, Armin W. Thomas, Samuel H. King, Garyk Brixi, Jeremy Sullivan, Madelena Y. Ng, Ashley Lewis, Aaron Lou, Stefano Ermon, Stephen A. Baccus, Tina Hernandez-Boussard, Christopher Re, Patrick D. Hsu, Brian L. Hie
  Sequence modeling and design from molecular to genome scale with Evo (Science Cover)
  Science. In Science, 15 Nov 2024, Vol. 386, No. 6723. DOI: 10.1126/science.ado9336.
• Felix Petersen, Hilde Kuehne, Christian Borgelt, Julian Welzel, Stefano Ermon
  Convolutional Differentiable Logic Gate Networks (Oral Presentation) [**PDF**]
  NeurIPS-24. In Proc. 38th Annual Conference on Neural Information Processing Systems, 2024.
• Haotian Ye, Haowei Lin, Jiaqi Han, Minkai Xu, Sheng Liu, Yitao Liang, Jianzhu Ma, James Zou, Stefano Ermon
  TFG: Unified Training-Free Guidance for Diffusion Models (Spotlight Presentation) [**PDF**]
  NeurIPS-24. In Proc. 38th Annual Conference on Neural Information Processing Systems, 2024.
• Felix Petersen, Christian Borgelt, Stefano Ermon
  TrAct: Making First-layer Pre-Activations Trainable [**PDF**]
  NeurIPS-24. In Proc. 38th Annual Conference on Neural Information Processing Systems, 2024.
• Jiaqi Han, Minkai Xu, Aaron Lou, Haotian Ye, Stefano Ermon
  Geometric Trajectory Diffusion Models [**PDF**]
  NeurIPS-24. In Proc. 38th Annual Conference on Neural Information Processing Systems, 2024.
• Zhuo Zheng, Yanfei Zhong, Liangpei Zhang, Stefano Ermon
  Segment Any Change [**PDF**]
  NeurIPS-24. In Proc. 38th Annual Conference on Neural Information Processing Systems, 2024.
• Siyi Gu, Minkai Xu, Alexander S Powers, Weili Nie, Tomas Geffner, Karsten Kreis, Jure Leskovec, Arash Vahdat, Stefano Ermon
  Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization [**PDF**]
  NeurIPS-24. In Proc. 38th Annual Conference on Neural Information Processing Systems, 2024.
• Zhengbang Zhu, Minghuan Liu, Liyuan Mao, Bingyi Kang, Minkai Xu, Yong Yu, Stefano Ermon, Weinan Zhang
  MADiff: Offline Multi-agent Learning with Diffusion Models [**PDF**]
  NeurIPS-24. In Proc. 38th Annual Conference on Neural Information Processing Systems, 2024.
• Gabriel Nobis, Maximilian Springenberg, Marco Aversa, Michael Detzel, Rembert Daems, Roderick Murray-Smith, Shinichi Nakajima, Sebastian Lapuschkin, Stefano Ermon, Tolga Birdal, Manfred Opper, Christoph Knochenhauer, Luis Oala, Wojciech Samek
  Generative Fractional Diffusion Models [**PDF**]
  NeurIPS-24. In Proc. 38th Annual Conference on Neural Information Processing Systems, 2024.
• Nikil Roashan Selvam, Amil Merchant, Stefano Ermon
  Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations [**PDF**]
  NeurIPS-24. In Proc. 38th Annual Conference on Neural Information Processing Systems, 2024.
• Syrine Belakaria, Benjamin Letham, Jana Doppa, Barbara E Engelhardt, Stefano Ermon, Eytan Bakshy
  Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes [**PDF**]
  NeurIPS-24. In Proc. 38th Annual Conference on Neural Information Processing Systems, 2024.
• Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon
  PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher [**PDF**]
  NeurIPS-24. In Proc. 38th Annual Conference on Neural Information Processing Systems, 2024.
• Felix Petersen, Christian Borgelt, Tobias Sutter, Hilde Kuehne, Oliver Deussen, Stefano Ermon
  Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms [**PDF**]
  NeurIPS-24. In Proc. 38th Annual Conference on Neural Information Processing Systems, 2024.
• Nemin Wu, Qian Cao, Zhangyu Wang, Zeping Liu, Yanlin Qi, Jielu Zhang, Joshua Ni, X. Angela Yao, Hongxu Ma, Lan Mu, Stefano Ermon, Tanuja Ganu, Akshay Nambi, Ni Lao, Gengchen Mai
  TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning [**PDF**]
  NeurIPS-24 (Datasets and Benchmarks Track). In Proc. 38th Annual Conference on Neural Information Processing Systems (Datasets and Benchmarks Track), 2024.
• Aaron Lou, Chenlin Meng, Stefano Ermon
  Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution [**PDF**]
  ICML-24. In Proc. 41th International Conference on Machine Learning, 2024.
  ICML Best Paper Award. 10 out of 2610 accepted papers and 9473 submissions.
• Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar
  Equivariant Graph Neural Operator for Modeling 3D Dynamics [**PDF**]
  ICML-24. In Proc. 41th International Conference on Machine Learning, 2024.
• Rohin Manvi, Samar Khanna, Marshall Burke, David B. Lobell, Stefano Ermon
  Large Language Models are Geographically Biased [**PDF**]
  ICML-24. In Proc. 41th International Conference on Machine Learning, 2024.
• Rom Parnichkun, Stefano Massaroli, Alessandro Moro, Michael Poli, Jimmy T.H. Smith, Ramin Hasani, Mathias Lechner, Qi An, Christopher Re, Hajime Asama, Stefano Ermon, Taiji Suzuki, Atsushi Yamashita
  State-Free Inference of State-Space Models: The *Transfer Function* Approach [**PDF**]
  ICML-24. In Proc. 41th International Conference on Machine Learning, 2024.
• Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, Bin Cui
  Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs [**PDF**]
  ICML-24. In Proc. 41th International Conference on Machine Learning, 2024.
• Anikait Singh, Fahim Tajwar, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar
  Understanding Preference Fine-Tuning for Large Language Models [**PDF**]ICML-24. In Proc. 41th International Conference on Machine Learning, 2024.
• Michael Poli, Armin W Thomas, Eric Nguyen, Stefano Massaroli, Pragaash Ponnusamy, Bj�rn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian Hie, Stefano Ermon, Christopher Re, Ce Zhang
  Mechanistic Design and Scaling of Hybrid Architectures [**PDF**]
  ICML-24. In Proc. 41th International Conference on Machine Learning, 2024.
• Charlotte Nicks, Eric Mitchell, Rafael Rafailov, Archit Sharma, Christopher D Manning, Chelsea Finn, Stefano Ermon
  Language Model Detectors Are Easily Optimized Against [**PDF**]
  ICLR-24. In Proc. 12th International Conference on Learning Representations, 2024.
• Linqi Zhou, Aaron Lou, Samar Khanna, Stefano Ermon
  Denoising Diffusion Bridge Models [**PDF**]
  ICLR-24. In Proc. 12th International Conference on Learning Representations, 2024.
• Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim, Wei-Hsiang Liao, Yuki Mitsufuji, J. Zico Kolter, Ruslan Salakhutdinov, Stefano Ermon
  Manifold Preserving Guided Diffusion [**PDF**]
  ICLR-24. In Proc. 12th International Conference on Learning Representations, 2024.
• Chris Cundy, Stefano Ermon
  SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking [**PDF**]
  ICLR-24. In Proc. 12th International Conference on Learning Representations, 2024.
• Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, Stefano Ermon
  Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion [**PDF**]
  ICLR-24. In Proc. 12th International Conference on Learning Representations, 2024.
• Rohin Manvi, Samar Khanna, Gengchen Mai, Marshall Burke, David Lobell, Stefano Ermon
  GeoLLM: Extracting Geospatial Knowledge from Large Language Models [**PDF**]
  ICLR-24. In Proc. 12th International Conference on Learning Representations, 2024.
• Samar Khanna, Patrick Liu, Linqi Zhou, Chenlin Meng, Robin Rombach, Marshall Burke, David Lobell, Stefano Ermon
  DiffusionSat: A Generative Foundation Model for Satellite Imagery [**PDF**]
  ICLR-24. In Proc. 12th International Conference on Learning Representations, 2024.
• Ling Yang, Zhilong Zhang, Zhaochen Yu, Jingwei Liu, Minkai Xu, Stefano Ermon, Bin Cui
  Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing [**PDF**]
  ICLR-24. In Proc. 12th International Conference on Learning Representations, 2024.
• Chris Cundy, Rishi Desai, Stefano Ermon
  Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients [**PDF**]
  AISTATS-24. In Proc. 27th International Conference on Artificial Intelligence and Statistics, 2024.
• Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec
  Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution [**PDF**]
  AAAI-24. In Proc. 38th AAAI Conference on Artificial Intelligence, 2024.
• Jonathan Xu, Amna Elmustafa, Liya Weldegebriel, Emnet Negash, Richard Lee, Chenlin Meng, Stefano Ermon, David Lobell
  HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing [**PDF**]
  AAAI-24. In Proc. 38th AAAI Conference on Artificial Intelligence, 2024.

2023

• Yuxuan Song, Jingjing Gong, Minkai Xu, Ziyao Cao, Yanyan Lan, Stefano Ermon, Hao Zhou, Wei-Ying Ma
  Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation [**PDF**]
  NeurIPS-23. In Proc. 37th Annual Conference on Neural Information Processing Systems, 2023.
• Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh, Nima Anari
  Parallel Sampling of Diffusion Models [**PDF**]
  NeurIPS-23. In Proc. 37th Annual Conference on Neural Information Processing Systems, 2023.
• Charles Thomas Marx, Sofian Zalouk, Stefano Ermon
  Calibration by Distribution Matching: Trainable Kernel Calibration Metrics [**PDF**]
  NeurIPS-23. In Proc. 37th Annual Conference on Neural Information Processing Systems, 2023.
• Can Qin, Shu Zhang, Ning Yu, Yihao Feng, Xinyi Yang, Yingbo Zhou, Huan Wang, Juan Carlos Niebles, Caiming Xiong, Silvio Savarese, Stefano Ermon, Yun Fu, Ran Xu
  UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild [**PDF**]
  NeurIPS-23. In Proc. 37th Annual Conference on Neural Information Processing Systems, 2023.
• Eric Nguyen, Michael Poli, Marjan Faizi, Armin W Thomas, Michael Wornow, Callum Birch-Sykes, Stefano Massaroli, Aman Patel, Clayton M. Rabideau, Yoshua Bengio, Stefano Ermon, Christopher Re, Stephen Baccus
  HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution [**PDF**]
  NeurIPS-23. In Proc. 37th Annual Conference on Neural Information Processing Systems, 2023.
• Stefano Massaroli, Michael Poli, Daniel Y Fu, Hermann Kumbong, David W. Romero, Rom Nishijima Parnichkun, Aman Timalsina, Quinn McIntyre, Beidi Chen, Atri Rudra, Ce Zhang, Christopher Re, Stefano Ermon, Yoshua Bengio
  Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions [PDF]
  NeurIPS-23. In Proc. 37th Annual Conference on Neural Information Processing Systems, 2023.
• Aaron Lou, Minkai Xu, Adam Farris, Stefano Ermon
  Scaling Riemannian Diffusion Models [**PDF**]
  NeurIPS-23. In Proc. 37th Annual Conference on Neural Information Processing Systems, 2023.
• Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher Manning, Chelsea Finn
  Direct Preference Optimization: Your Language Model is Secretly a Reward Model [**PDF**]
  NeurIPS-23. In Proc. 37th Annual Conference on Neural Information Processing Systems, 2023.
• Tony Lee, Michihiro Yasunaga, Chenlin Meng, Yifan Mai, Joon Sung Park, Agrim Gupta, Yunzhi Zhang, Deepak Narayanan, Hannah Benita Teufel, Marco Bellagente, Minguk Kang, Taesung Park, Jure Leskovec, Jun-Yan Zhu, Li Fei-Fei, Jiajun Wu, Stefano Ermon, Percy Liang
  Holistic Evaluation of Text-to-Image Models [**PDF**]
  NeurIPS-23 (dataset and benchmark track). In Proc. 37th Annual Conference on Neural Information Processing Systems (dataset and benchmark track), 2023.
• Alexandre Lacoste, Nils Lehmann, Pau Rodriguez, Evan David Sherwin, Hannah Kerner, Bj�rn L�tjens, Jeremy Andrew Irvin, David Dao, Hamed Alemohammad, Alexandre Drouin, Mehmet Gunturkun, Gabriel Huang, David Vazquez, Dava Newman, Yoshua Bengio, Stefano Ermon, Xiao Xiang Zhu
  GEO-Bench: Toward Foundation Models for Earth Monitoring [**PDF**]
  NeurIPS-23 (dataset and benchmark track). In Proc. 37th Annual Conference on Neural Information Processing Systems (dataset and benchmark track), 2023.
• Bram Wallace, Akash Gokul, Stefano Ermon, Nikhil Naik
  End-to-End Diffusion Latent Optimization Improves Classifier Guidance [**PDF**]
  ICCV-23. To appear in Proc. International Conference on Computer Vision, 2023.
• Aaron Lou, Stefano Ermon
  Reflected Diffusion Models [**PDF**]
  ICML-23. In Proc. 40th International Conference on Machine Learning, 2023.
• Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon
  FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation [**PDF**]
  ICML-23. In Proc. 40th International Conference on Machine Learning, 2023.
• Naoki Murata, Koichi Saito, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon
  GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration [**PDF**]
  ICML-23. In Proc. 40th International Conference on Machine Learning, 2023.
• Minkai Xu, Alexander S Powers, Ron O. Dror, Stefano Ermon, Jure Leskovec
  Geometric Latent Diffusion Models for 3D Molecule Generation [**PDF**]
  ICML-23. In Proc. 40th International Conference on Machine Learning, 2023.
• Andy Shih, Dorsa Sadigh, Stefano Ermon
  Long Horizon Temperature Scaling [**PDF**]
  ICML-23. In Proc. 40th International Conference on Machine Learning, 2023.
• Linqi Zhou, Michael Poli, Winnie Xu, Stefano Massaroli, Stefano Ermon
  Deep Latent State Space Models for Time-Series Generation [**PDF**]
  ICML-23. In Proc. 40th International Conference on Machine Learning, 2023.
• Gengchen Mai, Ni Lao, Yutong He, Jiaming Song, Stefano Ermon
  CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations [**PDF**]
  ICML-23. In Proc. 40th International Conference on Machine Learning, 2023.
• Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, Christopher Re
  Hyena Hierarchy: Towards Larger Convolutional Language Models [**PDF**]
  ICML-23. In Proc. 40th International Conference on Machine Learning, 2023.
• Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans
  On Distillation of Guided Diffusion Models [**PDF**]
  CVPR-23. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2023.
  Best Paper Award Nomination
• Benedikt Boecking, Nicholas Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski
  Generative Modeling Helps Weak Supervision (and Vice Versa) [**PDF**]
  ICLR-23. In Proc. 11th International Conference on Learning Representations, 2023.
• Xuan Su, Jiaming Song, Chenlin Meng, Stefano Ermon
  Dual Diffusion Implicit Bridges for Image-to-Image Translation [**PDF**]
  ICLR-23. In Proc. 11th International Conference on Learning Representations, 2023.
• Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon
  Extreme Q-Learning: MaxEnt RL without Entropy [**PDF**]
  ICLR-23. In Proc. 11th International Conference on Learning Representations, 2023.
• Kuno Kim, Stefano Ermon
  Understanding and Adopting Rational Behavior by Bellman Score Estimation [**PDF**]
  ICLR-23. In Proc. 11th International Conference on Learning Representations, 2023.
• Michael Poli, Stefano Massaroli, Stefano Ermon, Bryan Wilder, Eric Horvitz
  Ideal Abstractions for Decision-Focused Learning [**PDF**]
  AISTATS-23. In Proc. 26th International Conference on Artificial Intelligence and Statistics, 2023.
• Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Luke Huan
  But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI [**PDF**]
  AISTATS-23. In Proc. 26th International Conference on Artificial Intelligence and Statistics, 2023.
• Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon
  Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching [**PDF**]
  AAAI-23. In Proc. 37th AAAI Conference on Artificial Intelligence, 2023.

2022

• Chenlin Meng, Kristy Choi, Jiaming Song, Stefano Ermon
  Concrete Score Matching: Generalized Score Matching for Discrete Data [**PDF**]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon
  Generalizing Bayesian Optimization with Decision-theoretic Entropies [**PDF**]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Andy Shih, Dorsa Sadigh, Stefano Ermon
  Training and Inference on Any-Order Autoregressive Models the Right Way (Oral Presentation) [**PDF**]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Michael Poli, Winnie Xu, Stefano Massaroli, Chenlin Meng, Kuno Kim, Stefano Ermon
  Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations [**PDF**]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Michael Poli, Stefano Massaroli, Federico Berto, Jinkyoo Park, Tri Dao, Christopher R�, Stefano Ermon
  Transform Once: Efficient Operator Learning in Frequency Domain [**PDF**]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David Lobell, Stefano Ermon
  SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery [**PDF**]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Divyansh Garg, Skanda Vaidyanath, Kuno Kim, Jiaming Song, Stefano Ermon
  LISA: Learning Interpretable Skill Abstractions from Language [**PDF**]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Yann Dubois, Stefano Ermon, Tatsunori Hashimoto, Percy Liang
  Improving Self-Supervised Learning by Characterizing Idealized Representations [**PDF**]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger
  Exploration via Planning for Information about the Optimal Trajectory [**PDF**]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Muyang Li, Ji Lin, Chenlin Meng, Stefano Ermon, Song Han, Jun-Yan Zhu
  Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models [**PDF**]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song
  Denoising Diffusion Restoration Models [**PDF**]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Tri Dao, Daniel Fu, Stefano Ermon, Atri Rudra, Christopher R�
  FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness [**PDF**]
  NeurIPS-22. In Proc. 36th Annual Conference on Neural Information Processing Systems, 2022.
• Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon
  Modular Conformal Calibration [**PDF**]
  ICML-22. In Proc. 39th International Conference on Machine Learning, 2022.
• Rui Shu, Stefano Ermon
  Bit Prioritization in Variational Autoencoders via Progressive Coding [**PDF**]
  ICML-22. In Proc. 39th International Conference on Machine Learning, 2022.
• Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon
  A General Recipe for Likelihood-free Bayesian Optimization [**PDF**]
  ICML-22. In Proc. 39th International Conference on Machine Learning, 2022.
• Chenlin Meng, Linqi Zhou, Kristy Choi, Tri Dao, Stefano Ermon
  ButterflyFlow: Building Invertible Layers with Butterfly Matrices [**PDF**]
  ICML-22. In Proc. 39th International Conference on Machine Learning, 2022.
• Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani
  Imitation Learning by Estimating Expertise of Demonstrators [**PDF**]
  ICML-22. In Proc. 39th International Conference on Machine Learning, 2022.
• Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon
  SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations [**PDF**]
  ICLR-22. In Proc. 10th International Conference on Learning Representations, 2022.
• Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon
  Comparing Distributions by Measuring Differences that Affect Decision Making [**PDF**]
  ICLR-22. In Proc. 10th International Conference on Learning Representations, 2022.
  ICLR Outstanding Paper Award
• Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger
  An Experimental Design Perspective on Exploration in Reinforcement Learning [**PDF**]
  ICLR-22. In Proc. 10th International Conference on Learning Representations, 2022.
• Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang
  GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation [**PDF**]
  ICLR-22. In Proc. 10th International Conference on Learning Representations, 2022.
• Yang Song, Liyue Shen, Lei Xing, Stefano Ermon
  Solving Inverse Problems in Medical Imaging with Score-Based Generative Models [**PDF**]
  ICLR-22. In Proc. 10th International Conference on Learning Representations, 2022.
• Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon
  Density Ratio Estimation via Infinitesimal Classification [**PDF**]
  AISTATS-22. In Proc. 25th International Conference on Artificial Intelligence and Statistics, 2022.
• Chenlin Meng, Enci Liu, Willie Neiswanger, Jiaming Song, Marshall Burke, David Lobell, Stefano Ermon
  IS-Count: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling [**PDF**]
  AAAI-22. In Proc. 36th AAAI Conference on Artificial Intelligence, 2022.

More Publications

2021

• Christopher Yeh, Chenlin Meng, Sherrie Wang, Anne Driscoll, Erik Rozi, Patrick Liu, Jihyeon Lee, Marshall Burke, David Lobell, Stefano Ermon
  SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning [**Website**]
  NeurIPS-21 (Datasets & Benchmarks Track). In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Lantao Yu, Jiaming Song, Yang Song, Stefano Ermon
  Pseudo-Spherical Contrastive Divergence [**PDF**]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Chris Cundy, Aditya Grover, Stefano Ermon
  BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery [**PDF**]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon
  Reliable Decisions with Threshold Calibration [**PDF**]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon
  Estimating High Order Gradients of the Data Distribution by Denoising [**PDF**]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Mike Wu, Noah Goodman, Stefano Ermon
  Improving Compositionality of Neural Networks by Decoding Representations to Inputs [**PDF**]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, Stefano Ermon
  IQ-Learn: Inverse soft-Q Learning for Imitation (Spotlight Presentation) [**PDF**]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon
  CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [**PDF**]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David Lobell, Stefano Ermon
  Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis [**PDF**]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon
  Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration [**PDF**]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon
  D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation [**PDF**]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Yang Song, Conor Durkan, Iain Murray, Stefano Ermon
  Maximum Likelihood Training of Score-Based Diffusion Models (Spotlight Presentation) [**PDF**]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Andy Shih, Dorsa Sadigh, Stefano Ermon
  HyperSPNs: Compact and Expressive Probabilistic Circuits [**PDF**]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Robin Swezey, Aditya Grover, Bruno Charron, Stefano Ermon
  PiRank: Scalable Learning To Rank via Differentiable Sorting [**PDF**]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Kuno Kim, Akshat Jindal, Yang Song, Jiaming Song, Yanan Sui, Stefano Ermon
  Imitation with Neural Density Models [**PDF**]
  NeurIPS-21. In Proc. 35th Annual Conference on Neural Information Processing Systems, 2021.
• Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lobell, Stefano Ermon
  Geography-Aware Self-Supervised Learning [**PDF**]
  ICCV-21. In Proc. 18th International Conference on Computer Vision, 2021.
• Kristy Choi, Madeline Liao, Stefano Ermon
  Featurized Density Ratio Estimation [**PDF**]
  UAI-21. In Proc. 37th Conference on Uncertainty in Artificial Intelligence, 2021.
• Willie Neiswanger, Ke Alexander Wang, Stefano Ermon
  Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information [**PDF**]
  ICML-21. In Proc. 38th International Conference on Machine Learning, 2021.
• Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon
  Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving [**PDF**]
  ICML-21. In Proc. 38th International Conference on Machine Learning, 2021.
• Kuno Kim, Shivam Garg, Kirankumar Shiragur, Stefano Ermon
  Reward Identification in Inverse Reinforcement Learning [**PDF**]
  ICML-21. In Proc. 38th International Conference on Machine Learning, 2021.
• Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon
  Temporal Predictive Coding For Model-Based Planning In Latent Space [**PDF**]
  ICML-21. In Proc. 38th International Conference on Machine Learning, 2021.
• Jihyeon Lee, Nina Brooks, Fahim Tajwar, Marshall Burke, Stefano Ermon, David Lobell, Debashish Biswas, Stephen Luby
  Scalable Deep Learning to Identify Brick Kilns and Aid Regulatory Capacity [**PDF**]
  PNAS. In Proceedings of the National Academy of Sciences, 27 Apr 2021, 118 (17). DOI: 10.1073/pnas.2018863118.
• Marshall Burke, Anne Driscoll, David Lobell, Stefano Ermon
  Using Satellite Imagery to Understand and Promote Sustainable Development [**PDF**]
  Science. In Science, 19 Mar 2021, Vol. 371, No. 6535. DOI: 10.1126/science.abe8628.
• Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
  Score-Based Generative Modeling through Stochastic Differential Equations [**PDF**]
  ICLR-21. In Proc. 9th International Conference on Learning Representations, 2021.
  ICLR Outstanding Paper Award
• Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon
  Improved Autoregressive Modeling with Distribution Smoothing (Oral Presentation) [**PDF**]
  ICLR-21. In Proc. 9th International Conference on Learning Representations, 2021.
• Jiaming Song, Chenlin Meng, Stefano Ermon
  Denoising Diffusion Implicit Models [**PDF**]
  ICLR-21. In Proc. 9th International Conference on Learning Representations, 2021.
• Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon
  Anytime Sampling for Autoregressive Models via Ordered Autoencoding [**PDF**]
  ICLR-21. In Proc. 9th International Conference on Learning Representations, 2021.
• Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, Stefano Ermon
  Negative Data Augmentation [**PDF**]
  ICLR-21. In Proc. 9th International Conference on Learning Representations, 2021.
• Andy Shih, Arjun Sawhney, Jovana Kondic, Stefano Ermon, Dorsa Sadigh
  On the Critical Role of Conventions in Adaptive Human-AI Collaboration [**PDF**]
  ICLR-21. In Proc. 9th International Conference on Learning Representations, 2021.
• Shengjia Zhao, Stefano Ermon
  Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration [**PDF**]
  AISTATS-21. In Proc. 24th International Conference on Artificial Intelligence and Statistics, 2021.
• Jihyeon Lee, Dylan Grosz, Sicheng Zeng, Burak Uzkent, Marshall Burke, David Lobell, Stefano Ermon
  Predicting Livelihood Indicators from Crowdsourced Street Level Images [**PDF**]
  AAAI-21. In Proc. 35th AAAI Conference on Artificial Intelligence, 2021.
• Kumar Ayush, Burak Uzkent, Marshall Burke, David Lobell, Stefano Ermon
  Efficient Poverty Mapping from High Resolution Remote Sensing Images [**PDF**]
  AAAI-21. In Proc. 35th AAAI Conference on Artificial Intelligence, 2021.

2020

• Jiaming Song, Stefano Ermon
  Multi-label Contrastive Predictive Coding (Oral Presentation) [**PDF**]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon
  Belief Propagation Neural Networks [**PDF**]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Yang Song, Stefano Ermon
  Improved Techniques for Training Score-Based Generative Models [**PDF**]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
  Autoregressive Score Matching [**PDF**]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Andy Shih, Stefano Ermon
  Probabilistic Circuits for Variational Inference in Discrete Graphical Models [**PDF**]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Yusuke Tashiro, Yang Song, Stefano Ermon
  Diversity can be Transferred: Output Diversification for White- and Black-box Attacks [**PDF**]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher R�
  HiPPO: Recurrent Memory with Optimal Polynomial Projections (Spotlight Presentation) [**PDF**]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu
  Efficient Learning of Generative Models via Finite-Difference Score Matching [**PDF**]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, Tengyu Ma
  MOPO: Model-based Offline Policy Optimization [**PDF**]
  NeurIPS-20. In Proc. 34th Annual Conference on Neural Information Processing Systems, 2020.
• Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
  Training Deep Energy-Based Models with f-Divergence Minimization [**PDF**]
  ICML-20. In Proc. 37th International Conference on Machine Learning, 2020.
• Shengjia Zhao, Tengyu Ma, Stefano Ermon
  Individual Calibration with Randomized Forecasting [**PDF**]
  ICML-20. In Proc. 37th International Conference on Machine Learning, 2020.
• Jiaming Song, Stefano Ermon
  Bridging the Gap Between f-GANs and Wasserstein GANs [**PDF**]
  ICML-20. In Proc. 37th International Conference on Machine Learning, 2020.
• Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon
  Domain Adaptive Imitation Learning [**PDF**]
  ICML-20. In Proc. 37th International Conference on Machine Learning, 2020.
• Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon
  Fair Generative Modeling via Weak Supervision [**PDF**]
  ICML-20. In Proc. 37th International Conference on Machine Learning, 2020.
• Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung Bui
  Predictive Coding for Locally-Linear Control [**PDF**]
  ICML-20. In Proc. 37th International Conference on Machine Learning, 2020.
• Christopher Yeh, Anthony Perez, Anne Driscoll, George Azzari, Zhongyi Tang, David Lobell, Stefano Ermon, Marshall Burke
  Using Publicly Available Satellite Imagery and Deep Learning to Understand Economic Well-Being in Africa [**PDF**]
  Nature Communications. In Nature Communications, 11, 2583, 2020.
• Chris Cundy, Stefano Ermon
  Flexible Approximate Inference via Stratified Normalizing Flows [**PDF**]
  UAI-20. In Proc. 36th Conference on Uncertainty in Artificial Intelligence, 2020.
• Kumar Ayush, Burak Uzkent, Marshall Burke, David Lobell, Stefano Ermon
  Generating Interpretable Poverty Maps using Object Detection in Satellite Images [**PDF**]
  IJCAI-20. In Proc. 29th International Joint Conference on Artificial Intelligence, 2020.
• Peter M. Attia, Aditya Grover, Norman Jin, Kristen A. Severson, Todor M. Markov, Yang-Hung Liao, Michael H. Chen, Bryan Cheong, Nicholas Perkins, Zi Yang, Patrick K. Herring, Muratahan Aykol, Stephen J. Harris, Richard D. Braatz, Stefano Ermon, William C. Chueh
  Closed-loop Optimization of Fast-Charging Protocols for Batteries with Machine Learning [**PDF**] [**News**]
  Nature. In Nature, 578, 397-402, 2020.
• Joseph Duris, Dylan Kennedy, Adi Hanuka, Jane Shtalenkova, Auralee Edelen, Panagiotis Baxevanis, Adam Egger, Tyler Cope, Mitchell McIntire, Stefano Ermon, Daniel Ratner
  Bayesian Optimization of a Free-Electron Laser [**PDF**]
  Physical Review Letters. In Physical Review Letters, 124, 124801, 2020.
• Burak Uzkent, Stefano Ermon
  Learning When and Where to Zoom with Deep Reinforcement Learning [**PDF**]
  CVPR-20. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2020.
• Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon
  Gaussianization Flows [**PDF**]
  AISTATS-20. In Proc. 23rd International Conference on Artificial Intelligence and Statistics, 2020.
• Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
  Permutation Invariant Graph Generation via Score-Based Generative Modeling [**PDF**] [**Code**]
  AISTATS-20. In Proc. 23rd International Conference on Artificial Intelligence and Statistics, 2020.
• Shengjia Zhao, Christopher Yeh, Stefano Ermon
  A Framework for Sample Efficient Interval Estimation with Control Variates [**PDF**]
  AISTATS-20. In Proc. 23rd International Conference on Artificial Intelligence and Statistics, 2020.
• Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon
  A Theory of Usable Information under Computational Constraints [**PDF**]
  ICLR-20. In Proc. 8th International Conference on Learning Representations, 2020.
• Jiaming Song, Stefano Ermon
  Understanding the Limitations of Variational Mutual Information Estimators [**PDF**]
  ICLR-20. In Proc. 8th International Conference on Learning Representations, 2020.
• Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole
  Weakly Supervised Disentanglement with Guarantees [**PDF**]
  ICLR-20. In Proc. 8th International Conference on Learning Representations, 2020.
• Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon
  AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows [**PDF**]
  AAAI-20. In Proc. 34th AAAI Conference on Artificial Intelligence, 2020.
• Mike Wu, Kristy Choi, Noah Goodman, Stefano Ermon
  Meta-Amortized Variational Inference and Learning [**PDF**]
  AAAI-20. In Proc. 34th AAAI Conference on Artificial Intelligence, 2020.

2019

• Chi-Sing Ho, Neal Jean, Catherine A. Hogan, Lena Blackmon, Stefanie S. Jeffrey, Mark Holodniy, Niaz Banaei, Amr A. E. Saleh, Stefano Ermon, Jennifer Dionne
  Rapid Identification of Pathogenic Bacteria using Raman Spectroscopy and Deep Learning [**PDF**]
  Nature Communications. In Nature Communications, 30 Oct 2019, Issue 10, Number 4927, DOI: 10.1038/s41467-019-12898-9.
• Yang Song, Stefano Ermon
  Generative Modeling by Estimating Gradients of the Data Distribution (Oral Presentation) [**PDF**] [**Code**]
  NeurIPS-19. In Proc. 33rd Annual Conference on Neural Information Processing Systems, 2019.
• Aditya Grover, Jiaming Song, Alekh Agarwal, Kenneth Tran, Ashish Kapoor, Eric Horvitz, Stefano Ermon
  Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting [**PDF**]
  NeurIPS-19. In Proc. 33rd Annual Conference on Neural Information Processing Systems, 2019.
• Yang Song, Chenlin Meng, Stefano Ermon
  MintNet: Building Invertible Neural Networks with Masked Convolutions [**PDF**] [**Code**]
  NeurIPS-19. In Proc. 33rd Annual Conference on Neural Information Processing Systems, 2019.
• Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon
  Meta-Inverse Reinforcement Learning with Probabilistic Context Variables [**PDF**] [**Code**]
  NeurIPS-19. In Proc. 33rd Annual Conference on Neural Information Processing Systems, 2019.
• Jonathan Kuck, Tri Dao, Hamid Rezatofighi, Ashish Sabharwal, Stefano Ermon
  Approximating the Permanent by Sampling from Adaptive Partitions [**PDF**]
  NeurIPS-19. In Proc. 33rd Annual Conference on Neural Information Processing Systems, 2019.
• Sawyer Birnbaum, Volodymyr Kuleshov, Zayd Enam, Pang Wei Koh, Stefano Ermon
  Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations [**PDF**]
  NeurIPS-19. In Proc. 33rd Annual Conference on Neural Information Processing Systems, 2019.
• Carla Gomes, Thomas Dietterich, Christopher Barrett, Jon Conrad, Bistra Dilkina, Stefano Ermon, Fei Fang, Andrew Farnsworth, Alan Fern, Xiaoli Fern, Daniel Fink, Douglas Fisher, Alexander Flecker, Daniel Freund, Angela Fuller, John Gregoire, John Hopcroft, Steve Kelling, Zico Kolter, Warren Powell, Nicole Sintov, John Selker, Bart Selman, Daniel Sheldon, David Shmoys, Milind Tambe, Weng-Keen Wong, Christopher Wood, Xiaojian Wu, Yexiang Xue, Amulya Yadav, Abdul-Aziz Yakubu, Mary Lou Zeeman
  Computational Sustainability: Computing for a Better World and a Sustainable Future [**PDF**]
  CACM. In Communications of the ACM, September 2019, Vol. 62 No. 9, Pages 56-65.
• Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon
  Sliced Score Matching: A Scalable Approach to Density and Score Estimation [**PDF**] [**Code**]
  UAI-19. In Proc. 35th Conference on Uncertainty in Artificial Intelligence, 2019.
• Jonathan Kuck, Tri Dao, Shengjia Zhao, Burak Bartan, Ashish Sabharwal, Stefano Ermon
  Adaptive Hashing for Model Counting [**PDF**] [**Code**]
  UAI-19. In Proc. 35th Conference on Uncertainty in Artificial Intelligence, 2019.
• Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, David Lobell, Marshall Burke, Stefano Ermon
  Learning to Interpret Satellite Images using Wikipedia [**PDF**] [**Code**]
  IJCAI-19. In Proc. 28th International Joint Conference on Artificial Intelligence, 2019.
• Michael Xie, Stefano Ermon
  Reparameterizable Subset Sampling via Continuous Relaxations [**PDF**] [**Code**]
  IJCAI-19. In Proc. 28th International Joint Conference on Artificial Intelligence, 2019.
• Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent, Neal Jean, David Lobell, Marshall Burke, Stefano Ermon
  Predicting Economic Development using Geolocated Wikipedia Articles [**PDF**] [**Code**]
  KDD-19. In Proc. 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2019.
• Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon
  Neural Joint-Source Channel Coding [**PDF**] [**Code**]
  ICML-19. In Proc. 36th International Conference on Machine Learning, 2019.
• Aditya Grover, Aaron Zweig, Stefano Ermon
  Iterative Deep Generative Modeling of Large Graphs [**PDF**] [**Code**]
  ICML-19. In Proc. 36th International Conference on Machine Learning, 2019.
• Lantao Yu, Jiaming Song, Stefano Ermon
  Multi-Agent Adversarial Inverse Reinforcement Learning [**PDF**] [**Code**]
  ICML-19. In Proc. 36th International Conference on Machine Learning, 2019.
• Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon
  Calibrated Model-Based Deep Reinforcement Learning [**PDF**] [**Code**]
  ICML-19. In Proc. 36th International Conference on Machine Learning, 2019.
• Hongyu Ren, Shengjia Zhao, Stefano Ermon
  Adaptive Antithetic Sampling for Variance Reduction [**PDF**]
  ICML-19. In Proc. 36th International Conference on Machine Learning, 2019.
• Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon
  Stochastic Optimization of Sorting Networks via Continuous Relaxations [**PDF**] [**Code**]
  ICLR-19. In Proc. 7th International Conference on Learning Representations, 2019.
• Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon
  Learning Neural PDE Solvers with Convergence Guarantees [**PDF**] [**Code**]
  ICLR-19. In Proc. 7th International Conference on Learning Representations, 2019.
• Mike Wu, Noah Goodman, Stefano Ermon
  Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference [**PDF**] [**Code**]
  AISTATS-19. In Proc. 22nd International Conference on Artificial Intelligence and Statistics, 2019.
• Aditya Grover, Stefano Ermon
  Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization [**PDF**] [**Code**]
  AISTATS-19. In Proc. 22nd International Conference on Artificial Intelligence and Statistics, 2019.
• Rui Shu, Hung Bui, Jay Whang, Stefano Ermon
  Training Variational Autoencoders with Buffered Stochastic Variational Inference [**PDF**]
  AISTATS-19. In Proc. 22nd International Conference on Artificial Intelligence and Statistics, 2019.
• Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon
  Learning Controllable Fair Representations [**PDF**] [**Code**]
  AISTATS-19. In Proc. 22nd International Conference on Artificial Intelligence and Statistics, 2019.
• Wenjie Hu, Jay Harshadbhai Patel, Zoe-Alanah Robert, Paul Novosad, Samuel Asher, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon
  Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery [**PDF**]
  AIES-19. In Proc. 1st AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, 2019.
• Yuwei Mao, Xuelong Wang, Sihao Xia, Kai Zhang, Chenxi Wei, Seongmin Bak, Zulipiya Shadike, Xuejun Liu, Yang Yang, Rong Xu, Piero Pianetta, Stefano Ermon, Eli Stavitski, Kejie Zhao, Zhengrui Xu, Feng Lin, Xiao-Qing Yang, Enyuan Hu, Yijin Liu
  High-Voltage Charging-Induced Strain, Heterogeneity, and Micro-Cracks in Secondary Particles of a Nickel-Rich Layered Cathode Material [**PDF**]
  Advanced Functional Materials. In Advanced Functional Materials, 2019, Vol. 29 No. 18, Pages 1900247.
• Jian Wei Khor, Neal Jean, Eric S Luxenberg, Stefano Ermon, Sindy K Y Tang
  Using Machine Learning to Discover Shape Descriptors for Predicting Emulsion Stability in a Microfluidic Channel [**PDF**]
  Soft Matter. In Soft Matter, 2019, Vol. 15 No. 6, Pages 1361-1372.
• Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David Lobell, Stefano Ermon
  Tile2Vec: Unsupervised representation learning for spatially distributed data [**PDF**] [**Code**]
  AAAI-19. In Proc. 33rd AAAI Conference on Artificial Intelligence, 2019.
• Shengjia Zhao, Jiaming Song, Stefano Ermon
  InfoVAE: Balancing Learning and Inference in Variational Autoencoders [**PDF**] [**Code**]
  AAAI-19. In Proc. 33rd AAAI Conference on Artificial Intelligence, 2019.

2018

• Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon
  Multi-Agent Generative Adversarial Imitation Learning [**PDF**] [**Code**]
  NeurIPS-18. In Proc. 32nd Annual Conference on Neural Information Processing Systems, 2018.
• Rui Shu, Hung Bui, Shengjia Zhao, Mykel Kochenderfer, Stefano Ermon
  Amortized Inference Regularization [**PDF**]
  NeurIPS-18. In Proc. 32nd Annual Conference on Neural Information Processing Systems, 2018.
• Yang Song, Rui Shu, Nate Kushman, Stefano Ermon
  Constructing Unrestricted Adversarial Examples with Generative Models [**PDF**] [**Code**]
  NeurIPS-18. In Proc. 32nd Annual Conference on Neural Information Processing Systems, 2018.
• Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman, Stefano Ermon
  Bias and Generalization in Deep Generative Models: An Empirical Study [**PDF**] [**Code**]
  NeurIPS-18. In Proc. 32nd Annual Conference on Neural Information Processing Systems, 2018.
• Neal Jean, Michael Xie, Stefano Ermon
  Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance [**PDF**] [**Code**]
  NeurIPS-18. In Proc. 32nd Annual Conference on Neural Information Processing Systems, 2018.
• Aditya Grover, Tudor Achim, Stefano Ermon
  Streamlining Variational Inference for Constraint Satisfaction Problems [**PDF**] [**Code**]
  NeurIPS-18. In Proc. 32nd Annual Conference on Neural Information Processing Systems, 2018.
• Shengjia Zhao, Jiaming Song, Stefano Ermon
  The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models [**PDF**] [**Code**]
  UAI-18. In Proc. 34th Conference on Uncertainty in Artificial Intelligence, 2018.
• Stephan Eissman, Daniel Levy, Rui Shu, Stefan Bartzsch, Stefano Ermon
  Bayesian Optimization and Attribute Adjustment [**PDF**]
  UAI-18. In Proc. 34th Conference on Uncertainty in Artificial Intelligence, 2018.
• Barak Oshri, Annie Hu, Peter Adelson, Xiao Chen, Pascaline Dupas, Jeremy Weinstein, Marshall Burke, David Lobell, Stefano Ermon
  Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning [**PDF**]
  KDD-18. In Proc. 24th ACM SIGKDD Conference, 2018.
• Yang Song, Jiaming Song, Stefano Ermon
  Accelerating Natural Gradient with Higher-Order Invariance [**PDF**] [**Code**]
  ICML-18. In Proc. 35th International Conference on Machine Learning, 2018.
• Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon
  Accurate Uncertainties for Deep Learning Using Calibrated Regression [**PDF**]
  ICML-18. In Proc. 35th International Conference on Machine Learning, 2018.
• Manik Dhar, Aditya Grover, Stefano Ermon
  Modeling Sparse Deviations for Compressed Sensing using Generative Models [**PDF**] [**Code**]
  ICML-18. In Proc. 35th International Conference on Machine Learning, 2018.
• Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon
  Adversarial Constraint Learning for Structured Prediction [**PDF**] [**Code**]
  IJCAI-18. In Proc. 27th International Joint Conference on Artificial Intelligence, 2018.
• Lijie Fan, Wenbing Huang, Chuang Gan, Stefano Ermon, Boqing Gong, Junzhou Huang
  End-to-End Motion Representations Learning for Video Understanding [**PDF**]
  CVPR-18. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2018.
• Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman
  PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples [**PDF**]
  ICLR-18. In Proc. 6th International Conference on Learning Representations, 2018.
• Rui Shu, Hirokazu Narui, Hung Bui, Stefano Ermon
  A DIRT-T Approach to Unsupervised Domain Adaptation [**PDF**] [**Code**]
  ICLR-18. In Proc. 6th International Conference on Learning Representations, 2018.
• Aditya Grover, Ramki Gummadi, Miguel Lazaro-Gredilla, Dale Schuurmans, Stefano Ermon
  Variational Rejection Sampling [**PDF**]
  AISTATS-18. In Proc. 21st International Conference on Artificial Intelligence and Statistics, 2018.
• Aditya Grover, Todor Markov, Norman Jin, Peter Attia, Nick Perkins, Bryan Cheong, Michael Chen, Zi Yang, Stephen Harris, William Chueh, Stefano Ermon
  Best arm identification in multi-armed bandits with delayed and partial feedback [**PDF**]
  AISTATS-18. In Proc. 21st International Conference on Artificial Intelligence and Statistics, 2018.
• Aditya Grover, Manik Dhar, Stefano Ermon
  Flow-GAN: Combining maximum likelihood and adversarial learning in generative models [**PDF**] [**Code**]
  AAAI-18. In Proc. 32nd AAAI Conference on Artificial Intelligence, February 2018.
• Aditya Grover, Stefano Ermon
  Boosted Generative Models [**PDF**] [**Code**]
  AAAI-18. In Proc. 32nd AAAI Conference on Artificial Intelligence, February 2018.
• Jonathan Kuck, Stefano Ermon
  Approximate Inference via Weighted Rademacher Complexity [**PDF**]
  AAAI-18. In Proc. 32nd AAAI Conference on Artificial Intelligence, February 2018.
• Daniel Levy, Stefano Ermon
  Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces [**PDF**]
  AAAI-18. In Proc. 32nd AAAI Conference on Artificial Intelligence, February 2018.
• Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon
  Learning with weak supervision from physics and data-driven constraints [**PDF**]
  AI Magazine. In AI Magazine, Spring 2018, Vol 39, No 1, pp. 27-38.

2017

• William Gent, Kipil Lim, Yufeng Liang, Qinghao Li, Taylor Barnes, Sung-Jin Ahn, Kevin Stone, Mitchell McIntire, Jihyun Hong, Jay Hyok Song, Yiyang Li, Apurva Mehta, Stefano Ermon, Tolek Tyliszczak, Arthur Kilcoyne, David Vine, Jin-Hwan Park, Seok-Gwang Doo, Michael Toney, Wanli Yang, David Prendergast, and William Chueh
  Coupling Between Oxygen Redox and Cation Migration Explains Unusual Electrochemistry in Lithium-Rich Layered Oxides [**PDF**]
  Nature Communications. In Nature Communications, DOI: 10.1038/s41467-017-02041-x, December 2017.
• Volodymyr Kuleshov, Stefano Ermon
  Neural Variational Inference and Learning in Undirected Graphical Models [**PDF**]
  NIPS-17. In Proc. 31st Annual Conference on Neural Information Processing Systems, December 2017.
• Jiaming Song, Shengjia Zhao, Stefano Ermon
  A-NICE-MC: Adversarial Training for MCMC [**PDF**] [**Code**]
  NIPS-17. In Proc. 31st Annual Conference on Neural Information Processing Systems, December 2017.
• Yunzhu Li, Jiaming Song, Stefano Ermon
  InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations [**PDF**] [**Code**]
  NIPS-17. In Proc. 31st Annual Conference on Neural Information Processing Systems, December 2017.
• Stephen Mussmann, Daniel Levy, Stefano Ermon
  Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search [**PDF**]
  UAI-17. In Proc. 33rd Conference on Uncertainty in Artificial Intelligence, August 2017.
• Volodymyr Kuleshov, Stefano Ermon
  Deep Hybrid Models: Bridging Discriminative and Generative Approaches [**PDF**]
  UAI-17. In Proc. 33rd Conference on Uncertainty in Artificial Intelligence, August 2017.
• Shengjia Zhao, Jiaming Song, Stefano Ermon
  Learning Hierarchical Features from Generative Models [**PDF**] [**Code**]
  ICML-17. In Proc. 34th International Conference on Machine Learning, August 2017.
• Russell Stewart, Stefano Ermon
  Supervising Neural Networks with Physics and other Domain Knowledge [**PDF**] [**Code**]
  AAAI-17. In Proc. 31st AAAI Conference on Artificial Intelligence, February 2017.
  AAAI Outstanding Paper Award
• Volodymyr Kuleshov, Stefano Ermon
  Online Uncertainty Estimation Against an Adversary [**PDF**] [**Code**]
  AAAI-17. In Proc. 31st AAAI Conference on Artificial Intelligence, February 2017.
• Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, Stefano Ermon
  Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data [**PDF**] [**Code**]
  AAAI-17. In Proc. 31st AAAI Conference on Artificial Intelligence, February 2017.
  Best Student Paper Award (CompSust Track)
• Colin Wei, Stefano Ermon
  General Bounds on Satisfiability Thresholds for Random CSPs via Fourier Analysis [**PDF**]
  AAAI-17. In Proc. 31st AAAI Conference on Artificial Intelligence, February 2017.
• Siamak Yousefi, Hirokazu Narui, Sankalp Dayal, Stefano Ermon, Shahrokh Valaee
  A Survey on Behavior Recognition Using WiFi Channel State Information [**PDF**] [**Code**]
  In IEEE Communications Magazine, 55 (10), 98-104, 2017.
• Biagio Cosenza, Juan Durillo, Stefano Ermon, Ben Juurlink
  Autotuning Stencil Computations with Structural Ordinal Regression Learning [**PDF**]
  IPDPS-17. In IEEE International Parallel and Distributed Processing Symposium, February 2017.

2016

• Xiaoyue Duan, Feifei Yang, Erin Antono, Wenge Yang, Piero Pianetta, Stefano Ermon, Apurva Mehta, Yijin Liu
  Unsupervised Data Mining in Nanoscale X-ray Spectro-Microscopic Study of NdFeB Magnet [**PDF**]
  Scientific Reports. In Scientific Reports, 6, 34406 (2016).
• Neal Jean, Marshall Burke, Michael Xie, Matthew Davis, David Lobell, Stefano Ermon
  Combining Satellite Imagery and Machine Learning to Predict Poverty [**PDF**] [**Project Website**] [**Commentary**] [**Nature Research Highlights**] [**Code**]
  Science. In Science, 353(6301), 790-794, 2016.
• Aditya Grover, Stefano Ermon
  Variational Bayes on Monte Carlo Steroids [**PDF**]
  NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
• Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon
  Adaptive Concentration Inequalities for Sequential Decision Problems [**PDF**] [**Code**]
  NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
• Jonathan Ho, Stefano Ermon
  Generative Adversarial Imitation Learning [**PDF**] [**Code**]
  NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
• Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla Gomes, Bart Selman
  Solving Marginal MAP Problems with NP Oracles and Parity Constraints [**PDF**]
  NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
• Mitchell McIntire, Daniel Ratner, Stefano Ermon
  Sparse Gaussian Processes for Bayesian Optimization [**PDF**] [**Code**]
  UAI-16. In Proc. 32nd Conference on Uncertainty in Artificial Intelligence, June 2016.
• Jonathan Ho, Jayesh Gupta, Stefano Ermon
  Model-Free Imitation Learning with Policy Optimization [**PDF**]
  ICML-16. In Proc. 33rd International Conference on Machine Learning, June 2016.
• Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla Gomes, Bart Selman
  Variable Elimination in the Fourier Domain [**PDF**]
  ICML-16. In Proc. 33rd International Conference on Machine Learning, June 2016.
• Steve Mussmann, Stefano Ermon
  Learning and Inference via Maximum Inner Product Search [**PDF**]
  ICML-16. In Proc. 33rd International Conference on Machine Learning, June 2016.
• Tudor Achim, Ashish Sabharwal, Stefano Ermon
  Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference [**PDF**]
  ICML-16. In Proc. 33rd International Conference on Machine Learning, June 2016.
• Lun-Kai Hsu, Tudor Achim, Stefano Ermon
  Tight Variational Bounds via Random Projections and I-Projections [**PDF**]
  AISTATS-16. In Proc. 19th International Conference on Artificial Intelligence and Statistics, May 2016.
• Michael Xie, Neal Jean, Marshall Burke, David Lobell, Stefano Ermon
  Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping [**PDF**] [**Stanford Report**] [**NYTimes**]
  AAAI-16. In Proc. 30th AAAI Conference on Artificial Intelligence, February 2016.
• Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon
  Closing the Gap Between Short and Long XORs for Model Counting [**PDF**] [**Code**]
  AAAI-16. In Proc. 30th AAAI Conference on Artificial Intelligence, February 2016.
• Carolyn Kim, Ashish Sabharwal, Stefano Ermon
  Exact Sampling with Integer Linear Programs and Random Perturbations [**PDF**] [**Code**]
  AAAI-16. In Proc. 30th AAAI Conference on Artificial Intelligence, February 2016.

2015

• Stefan Hadjis, Stefano Ermon
  Importance sampling over sets: a new probabilistic inference scheme. [**PDF**] [**Code**]
  UAI-15. In Proc. 31st Conference on Uncertainty in Artificial Intelligence, July 2015.
• Michael Zhu, Stefano Ermon
  A Hybrid Approach for Probabilistic Inference using Random Projections. [**PDF**]
  ICML-15. In Proc. 32nd International Conference on Machine Learning, July 2015.
• Yexiang Xue, Stefano Ermon, Carla Gomes, Bart Selman
  Uncovering Hidden Structure through Parallel Problem Decomposition for the Set Basis Problem with Application to Materials Discovery. [**PDF**]
  IJCAI-15. In Proc. International Joint Conference on Artificial Intelligence, July 2015.
• Stefano Ermon, Yexiang Xue, Russell Toth, Bistra Dilkina, Richard Bernstein, Theodoros Damoulas, Patrick Clark, Steve DeGloria, Andrew Mude, Christopher Barrett, and Carla Gomes
  Learning Large Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa. [**PDF**]
  AAAI-15. In Proc. 29th AAAI Conference on Artificial Intelligence, January 2015.
• Stefano Ermon, Ronan Le Bras, Santosh Suram, John M. Gregoire, Carla Gomes, Bart Selman, and Robert B. van Dover
  Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery. [**PDF**]
  AAAI-15. In Proc. 29th AAAI Conference on Artificial Intelligence, January 2015.

2014

• Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
  Designing Fast Absorbing Markov Chains [**PDF**]
  AAAI-14. In Proc. 28th AAAI Conference on Artificial Intelligence, July 2014.
• Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
  Low-density Parity Constraints for Hashing-Based Discrete Integration [**PDF**] [**Code**]
  ICML-14. In Proc. 31st International Conference on Machine Learning, June 2014.

2013

• Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
  Embed and Project: Discrete Sampling with Universal Hashing [**PDF**] [**Code**]
  NIPS-13. In Proc. 27th Annual Conference on Neural Information Processing Systems, December 2013.
• Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
  Optimization With Parity Constraints: From Binary Codes to Discrete Integration [**PDF**] [**Slides**] [**Poster**] [**Code**]
  UAI-13. In Proc. 29th Conference on Uncertainty in Artificial Intelligence, July 2013.
  Best Student Paper Award. Best Paper Award Runner-up.
• Stefano Ermon, Yexiang Xue, Carla Gomes, and Bart Selman.
  Learning Policies For Battery Usage Optimization in Electric Vehicles.
  Machine Learning. In Machine Learning: Volume 92, Issue 1, Page 177-194, 2013.
• Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
  Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization [**PDF**] [**Slides**] [**Code**]
  ICML-13. In Proc. 30th International Conference on Machine Learning, June 2013.

2012

• Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
  Density Propagation and Improved Bounds on the Partition Function. [**PDF**] [**[Poster](papers/NIPS2012%5FPoster - v3.pdf)**]
  NIPS-12. In Proc. 26th Annual Conference on Neural Information Processing Systems, December 2012.
• Stefano Ermon, Carla Gomes, and Bart Selman
  Uniform Solution Sampling Using a Constraint Solver As an Oracle [**PDF**] [**Slides**] [**Code**]
  UAI-12. In Proc. 28th Conference on Uncertainty in Artificial Intelligence, August 2012.
• Liaoruo Wang, Stefano Ermon, and John Hopcroft
  Feature-Enhanced Probabilistic Models for Diffusion Network Inference. [**PDF**] [**Slides**] [**Code**]
  ECML-PKDD-12. In Proc. of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2012.
• Stefano Ermon, Yexiang Xue, Carla Gomes, and Bart Selman
  Learning Policies For Battery Usage Optimization in Electric Vehicles [**PDF**] [**Slides**] [**Dataset**]
  ECML-PKDD-12. In Proc. of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2012.
• Stefano Ermon, Ronan Le Bras, Carla Gomes, Bart Selman, and Bruce van Dover
  SMT-Aided Combinatorial Materials Discovery [**PDF**] [**Code**]
  SAT-12. In Proc. 15th International Conference on Theory and Applications of Satisfiability Testing, June 2012.
• Stefano Ermon, Carla Gomes, Bart Selman, and Alexander Vladimirsky
  Probabilistic Planning With Non-linear Utility Functions and Worst Case Guarantees [**PDF**]
  AAMAS-12. In Proc. 11th International Conference on Autonomous Agents and Multiagent Systems, June 2012.

2011

• Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
  Accelerated Adaptive Markov Chain for Partition Function Computation [**PDF**] [**Code**] [**Data**]
  NIPS-11. In Proc. 25th Annual Conference on Neural Information Processing Systems, December 2011.
• Stefano Ermon, Carla Gomes, and Bart Selman
  A Flat Histogram Method for Computing the Density of States of Combinatorial Problems [**PDF**]
  IJCAI-11. In Proc. 22nd International Joint Conference on Artificial Intelligence, July 2011. .
• Stefano Ermon, Jon Conrad, Carla Gomes, and Bart Selman
  Risk-Sensitive Policies for Sustainable Renewable Resource Allocation [**PDF**]
  IJCAI-11. In Proc. 22nd International Joint Conference on Artificial Intelligence, July 2011.
• Stefano Ermon, Carla Gomes, and Bart Selman
  A Message Passing Approach to Multiagent Gaussian Inference for Dynamic Processes (Short Paper) [**PDF**]
  AAMAS-11. In Proc. 10th International Conference on Autonomous Agents and Multiagent Systems, May 2011.

2010

• Stefano Ermon, Carla Gomes, and Bart Selman
  Computing the Density of States of Boolean Formulas [**PDF**] [**Slides**] [**Code**] [**Data**]
  CP-10. In Proc. 16th International Conference on Principles and Practice of Constraint Programming, September 2010.
  Best Student Paper Award
• Stefano Ermon, Jon Conrad, Carla Gomes, and Bart Selman
  Playing Games against Nature: Optimal Policies for Renewable Resource Allocation [**PDF**]
  UAI-10. In Proc. 26th Conference on Uncertainty in Artificial Intelligence, July 2010.
• Stefano Ermon, Carla Gomes, and Bart Selman
  Collaborative Multiagent Gaussian Inference in a Dynamic Environment Using Belief Propagation (Short Paper) [**PDF**]
  AAMAS-10. In Proc. 9th International Conference on Autonomous Agents and Multiagent Systems, May 2010.

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