Prediction-aware learning in multi-agent systems. A. Capitaine, E. Boursier, E. Moulines, M. I. Jordan, and A. Durmus. In M. Fazel, D. Hsu, S. Lacoste-Julien, and V. Smith (Eds.),International Conference on Machine Learning (ICML), 2025.
AutoEval done right: Using synthetic data for model evaluation. P. Boyeau, A. N. Angelopoulos, N. Yosef, J. Malik, and M. I. Jordan. In M. Fazel, D. Hsu, S. Lacoste-Julien, and V. Smith (Eds.),International Conference on Machine Learning (ICML), 2025.
Automatically adaptive conformal risk control. V. Blot, A. Angelopoulos, M. I. Jordan, and N. Brunel.Proceedings of the Twenty-Eighth Conference on Artificial Intelligence and Statistics (AISTATS), 2025.
Unravelling in collaborative learning. A. Capitaine, E. Boursier, A. Scheid, E. Moulines, M. I. Jordan, E.-M. El Mhamdi, and A. Durmus. In A. Fan, C. Zhang, D. Belgrave, J. Tomczak, and U. Paquet (Eds),Advances in Neural Information Processing Systems (NeurIPS) 37, 2024.
Fairness-aware meta-learning via Nash bargaining. Y. Zeng, X. Yang, L. Chen, C. C. Ferrer, M. Jin, M. I. Jordan, and R. Jia. In A. Fan, C. Zhang, D. Belgrave, J. Tomczak, and U. Paquet (Eds),Advances in Neural Information Processing Systems (NeurIPS) 37, 2024.
Fair allocation in dynamic mechanism design. A. Fallah, M. I. Jordan, and A. Ulichney. In A. Fan, C. Zhang, D. Belgrave, J. Tomczak, and U. Paquet (Eds),Advances in Neural Information Processing Systems (NeurIPS) 37, 2024.
Dimension-free private mean estimation for anisotropic distributions. Y. Dagan, M. I. Jordan, X. Yang, L. Zakynthinou, and N. Zhivotovskiy. In A. Fan, C. Zhang, D. Belgrave, J. Tomczak, and U. Paquet (Eds),Advances in Neural Information Processing Systems (NeurIPS) 37, 2024.
Chatbot Arena: An open platform for evaluating LLMs by human preference. W.-L. Chiang, L. Zheng, Y. Sheng, A. N. Angelopoulos, T. Li, D. Li, H. Zhang, B. Zhu, M. I. Jordan, J. Gonzalez, and I. Stoica. In A. Weller, K. Heller, N. Oliver and Z. Kolter (Eds.),International Conference on Machine Learning (ICML), 2024.
Incentivized learning in principal-agent bandit games. A. Scheid, D. Tiapkin, E. Boursier, A. Capitaine, E.-M. El Mhamdi, E. Moulines, M. I. Jordan, and A. Durmus. In A. Weller, K. Heller, N. Oliver and Z. Kolter (Eds.),International Conference on Machine Learning (ICML), 2024.
Conformal triage for medical imaging AI deployment. A. Angelopoulos, S. Pomerantz, S. Do, S. Bates, C. Bridge, D. Elton, M. Lev, R. G. Gonzalez, M. I. Jordan, and J. Malik.medrxiv.org/content/10.1101/2024.02.09.24302543v1, 2024.
Class-conditional conformal prediction with many classes. T. Ding, A. Angelopoulos, S. Bates, M. I. Jordan, and R. Tibshirani. In A. Globerson, K. Saenko, M. Hardt, and S. Levine (Eds),Advances in Neural Information Processing Systems (NeurIPS) 36, 2023.
On learning necessary and sufficient causal graphs. H. Cai, Y. Wang, M. I. Jordan, and R. Song. In A. Globerson, K. Saenko, M. Hardt, and S. Levine (Eds),Advances in Neural Information Processing Systems (NeurIPS) 36, 2023.
Doubly robust self-training. B. Zhu, M. Ding, P. Jacobson, M. Wu, W. Zhan, M. I. Jordan, and J. Jiao. In A. Globerson, K. Saenko, M. Hardt, and S. Levine (Eds),Advances in Neural Information Processing Systems (NeurIPS) 36, 2023.
Deterministic nonsmooth nonconvex optimization. M. I. Jordan, G. Kornowski, T. Lin, O. Shamir, and E. Zampetakis. In G. Neu and L. Rosasco (Eds.),Proceedings of the Thirty-Sixth Conference on Learning Theory (COLT), Bengalaru, India, 2023.
The sample complexity of online contract design. B. Zhu, S. Bates, Z. Yang, Y. Wang, J. Jiao, and M. I. Jordan. In J. Hartline and L. Samuelson (Eds.),ACM Conference on Economics and Computation (EC), London, UK, 2023.
Bayesian robustness: A nonasymptotic viewpoint. K. Bhatia, Y-A. Ma, A. Dragan, P. Bartlett, and M. I. Jordan.Journal of the American Statistical Association, doi.org/10.1080/01621459.2023.2174121, 2023.
Recommendation systems with distribution-free reliability guarantees. A. Angelopoulos, K. Krauth, S. Bates, Y. Wang, and M. I. Jordan. In H. Papadopoulos and K. An (Eds.),12th Symposium on Conformal and Probabilistic Prediction with Applications (COPA), Limassol, Cyprus, 2023. [_Alexey Chervonenkis Best Paper Award_].
Local exchangeability. T. Campbell, S. Syed, C.-Y. Yang, M. I. Jordan, and T. Broderick.Bernoulli, 29, 2084-2100, 2023.
2022
Empirical Gateaux derivatives for causal inference. M. I. Jordan, Y. Wang, and A. Zhou. In A. Agarwal, A. Oh, D. Belgrave, and and K. Cho (Eds),Advances in Neural Information Processing Systems (NeurIPS) 35. 2022.
Robust calibration with multi-domain temperature scaling. Y. Yu, S. Bates, Y. Ma, and M. I. Jordan. In A. Agarwal, A. Oh, D. Belgrave, and and K. Cho (Eds),Advances in Neural Information Processing Systems (NeurIPS) 35. 2022.
Rank diminishing in deep neural networks. R. Feng, K. Zheng, Y. Huang, D. Zhao, M. I. Jordan, and Z.-J. Zhao. In A. Agarwal, A. Oh, D. Belgrave, and and K. Cho (Eds),Advances in Neural Information Processing Systems (NeurIPS) 35. 2022.
The sky above the clouds. S. Chasins, A. Cheung, N. Crooks, A. Ghodsi, K. Goldberg, J. E. Gonzalez, J. M. Hellerstein, M. I. Jordan, A. D. Joseph, M. Mahoney, A. Parameswaran, D. Patterson, R. A. Popa, K. Sen, S. Shenker, D. Song, and I. Stoica.arxiv.org/abs/2205.07147, 2022.
Optimal mean estimation without a variance. Y. Cherapanamjeri, N. Tripuraneni, P. Bartlett, and M. I. Jordan.Proceedings of the Thirty-Fifth Conference on Learning Theory (COLT), 2022.
No-regret learning in partially-informed auctions. W. Guo, M. I. Jordan, and E. Vitercik. In In C. Szepesvari, L. Song and S. Jegelka (Eds.),International Conference on Machine Learning (ICML), 2022.
Scvi-tools: A library for deep probabilistic analysis of single-cell omics data. A. Gayoso, R. Lopez, G. Xing, P. Boyeau, K. Wu, M. Jayasuriya, E. Melhman, M. Langevin, Y. Liu, J. Samaran, J., G. Misrachi, A. Nazaret, O. Clivio, C. Xu, T. Ashuach, M. Lotfollahi, V. Svensson, E. Da Veiga Beltrame, C. Talavera-López, L. Pachter, F. Theis, A. Streets, M. I. Jordan, J. Regier, and N. Yosef.Nature Biotechnology, 40, 163-166, 2022.
Who leads and who follows in strategic classification? T. Zrnic, E. Mazumdar, S. S. Sastry, and M. I. Jordan. In M. Ranzato, A. Beygelzimer, P. Liang, J. Wortman Vaughan, and Y. Dauphin (Eds),Advances in Neural Information Processing Systems (NeurIPS) 34. 2021.
Learning equilibria in matching markets from bandit feedback. M. Jagadeesan, A. Wei, M. I. Jordan, and J. Steinhardt. In M. Ranzato, A. Beygelzimer, P. Liang, J. Wortman Vaughan, and Y. Dauphin (Eds),Advances in Neural Information Processing Systems (NeurIPS) 34. 2021.
On the theory of reinforcement learning with once-per-episode feedback. N. Chatterji, A. Pacchiano, P. Bartlett, and M. I. Jordan. In M. Ranzato, A. Beygelzimer, P. Liang, J. Wortman Vaughan, and Y. Dauphin (Eds),Advances in Neural Information Processing Systems (NeurIPS) 34. 2021.
Robust learning of optimal auctions. W. Guo, M. Jordan, and E. Zampetakis. In M. Ranzato, A. Beygelzimer, P. Liang, J. Wortman Vaughan, and Y. Dauphin (Eds),Advances in Neural Information Processing Systems (NeurIPS) 34. 2021.
On component interactions in two-stage recommender systems. J. Hron, K. Krauth, M. I. Jordan, and N. Kilbertus. In M. Ranzato, A. Beygelzimer, P. Liang, J. Wortman Vaughan, and Y. Dauphin (Eds),Advances in Neural Information Processing Systems (NeurIPS) 34. 2021.
Test-time collective prediction. C. Mendler-Dünner, W. Guo, S. Bates, and M. I. Jordan. In M. Ranzato, A. Beygelzimer, P. Liang, J. Wortman Vaughan, and Y. Dauphin (Eds),Advances in Neural Information Processing Systems (NeurIPS) 34. 2021.
Learning in multi-stage decentralized matching markets. X. Dai and M. I. Jordan. In M. Ranzato, A. Beygelzimer, P. Liang, J. Wortman Vaughan, and Y. Dauphin (Eds),Advances in Neural Information Processing Systems (NeurIPS) 34. 2021.
Tactical optimism and pessimism for deep reinforcement learningT. Moskovitz, J. Parker-Holder, A. Pacchiano, M. Arbel, and M. I. Jordan. In M. Ranzato, A. Beygelzimer, P. Liang, J. Wortman Vaughan, and Y. Dauphin (Eds),Advances in Neural Information Processing Systems (NeurIPS) 34. 2021.
Elastic hyperparameter tuning on the cloud. L. Dunlap, K. Kandasamy, U. Misra, R. Liaw, J. Gonzalez, I. Stoica, and M. I. Jordan.ACM Symposium on Cloud Computing (SoCC), Seattle, WA, 2021.
Learning from eXtreme bandit feedback. R. Lopez, I. Dhillon, and M. I. Jordan.Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 2021. [_Best Paper Award Honorable Mention_].
Robust optimization for fairness with noisy protected groups. S. Wang, W. Guo, H. Narasimhan, A. Cotter, M. Gupta, and M. I. Jordan. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H.-T. Lin (Eds),Advances in Neural Information Processing Systems (NeurIPS) 33. 2020.
Decision-making with auto-encoding variational Bayes. R. Lopez, P. Boyeau, N. Yosef, M. I. Jordan, and J. Regier. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H.-T. Lin (Eds),Advances in Neural Information Processing Systems (NeurIPS) 33. 2020.
Projection robust Wasserstein distance and Riemannian optimization. T. Lin, C. Fan, N. Ho, M. Cuturi, and M. I. Jordan. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H.-T. Lin (Eds),Advances in Neural Information Processing Systems (NeurIPS) 33. 2020.
On Thompson sampling with Langevin algorithms. E. Mazumdar, A. Pacchiano, Y.-A. Ma, P. Bartlett, and M. I. Jordan. In H. Daumé III and A. Singh (Eds.),International Conference on Machine Learning (ICML), 2020.
Learning to score behaviors for guided policy optimization. A. Pacchiano, J. Parker-Holder, Y. Tang, K. Choromanski, A. Choromanska, and M. I. Jordan. In H. Daumé III and A. Singh (Eds.),International Conference on Machine Learning (ICML), 2020.
Stochastic gradient and Langevin processes. X. Cheng, Yin, D., P. Bartlett, and M. I. Jordan. In H. Daumé III and A. Singh (Eds.),International Conference on Machine Learning (ICML), 2020.
Competing bandits in matching markets. L. Liu, H. Mania, and M. I. Jordan. In R. Calandra and S. Chiappa (Eds.),Proceedings of the Twenty-Third Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Italy, 2020.
Langevin Monte Carlo without smoothness. N. Chatterji, J. Diakonikolas, M. I. Jordan, and P. Bartlett. In R. Calandra and S. Chiappa (Eds.),Proceedings of the Twenty-Third Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Italy, 2020.
The power of batching in multiple hypothesis testing. T. Zrnic, D. Jiang, Ramdas, A., and M. I. Jordan. In R. Calandra and S. Chiappa (Eds.),Proceedings of the Twenty-Third Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Italy, 2020.
Sharp analysis of expectation-maximization for weakly identifiable models. R. Dwivedi, N. Ho, K. Khamaru, M. Wainwright, M. I. Jordan, and B. Yu. In R. Calandra and S. Chiappa (Eds.),Proceedings of the Twenty-Third Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Italy, 2020.
Variance reduction with sparse gradients. M. Elibol, L. Lei, and M. I. Jordan.International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020.
Wasserstein reinforcement learning. A. Pacchiano, J. Parker-Holder, Y. Tang, A. Choromanska, K. Choromanski, and M. I. Jordan.arxiv.org/abs/1906.04349, 2019.
A Swiss army infinitesimal jackknife. R. Giordano, W. Stephenson, R. Liu, M. I. Jordan, and T. Broderick. In K. Chaudhuri and M. Sugiyama (Eds.),Proceedings of the Twenty-Second Conference on Artificial Intelligence and Statistics (AISTATS), Okinawa, Japan, 2019. [_Notable Paper Award_].
Ray: A distributed framework for emerging AI applications. P. Moritz, R. Nishihara, S. Wang, A. Tumanov, R. Liaw, E. Liang, W. Paul, M. I. Jordan, and I. Stoica.13th USENIX Symposium on Operating Systems Design and Implementation (OSDI), Carlsbad, CA, 2018.
Ray RLlib: A framework for distributed reinforcement learning. E. Liang, R. Liaw, P. Moritz, R. Nishihara, R. Fox, K. Goldberg, J. Gonzalez, M. I. Jordan, and I. Stoica. Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018.
Is Q-learning provably efficient?. C. Jin, Z. Allen-Zhu, S. Bubeck, and M. I. Jordan. In S. Vishwanathan, H. Wallach, Larochelle, H., Grauman, K., and Cesa-Bianchi, N. (Eds),Advances in Neural Information Processing Systems (NeurIPS) 31, 2018.
On the local minima of the empirical risk. C. Jin, L. Liu, R. Ge, and M. I. Jordan. In S. Vishwanathan, H. Wallach, Larochelle, H., Grauman, K., and Cesa-Bianchi, N. (Eds),Advances in Neural Information Processing Systems (NeurIPS) 31, 2018.
Theoretical guarantees for EM under misspecified Gaussian mixture models. R. Dwivedi, N. Ho, K. Khamaru, M. Wainwright, and M. I. Jordan. In S. Vishwanathan, H. Wallach, Larochelle, H., Grauman, K., and Cesa-Bianchi, N. (Eds),Advances in Neural Information Processing Systems (NeurIPS) 31, 2018.
Generalized zero-shot learning with deep calibration network. S. Liu, M. Long, J. Wang, and M. I. Jordan. In S. Vishwanathan, H. Wallach, Larochelle, H., Grauman, K., and Cesa-Bianchi, N. (Eds),Advances in Neural Information Processing Systems (NeurIPS) 31, 2018.
Conditional adversarial domain adaptation. M. Long, Z. Cao, J. Wang, and M. I. Jordan. In S. Vishwanathan, H. Wallach, Larochelle, H., Grauman, K., and Cesa-Bianchi, N. (Eds),Advances in Neural Information Processing Systems (NeurIPS) 31, 2018.
Information constraints on auto-encoding variational Bayes. R. Lopez, J. Regier, M. I. Jordan, and N. Yosef. In S. Vishwanathan, H. Wallach, Larochelle, H., Grauman, K., and Cesa-Bianchi, N. (Eds),Advances in Neural Information Processing Systems (NeurIPS) 31, 2018.
A Berkeley view of systems challenges for AI. I. Stoica, D. Song, R. A. Popa, D. Patterson, M. Mahoney, R Katz, A. Joseph, M. I. Jordan, J. M. Hellerstein, J. Gonzalez, K Goldberg, A. Ghodsi, D. Culler, and P. Abbeel.arxiv.org/abs/1712.05855, 2017.
Real-time machine learning: The missing pieces. R. Nishihara, P. Moritz, S. Wang, A. Tumanov, W. Paul, J. Schleier-Smith, R. Liaw, M. I. Jordan and I. Stoica.16th Workshop on Hot Topics in Operating Systems (HotOS XVI), Whistler, Canada, 2017.
How to escape saddle points efficiently. C. Jin, R. Ge, P. Netrapalli, S. Kakade, and M. I. Jordan. In D. Precup and Y. W. Teh (Eds), Proceedings of the 34th International Conference on Machine Learning (ICML), Sydney, Australia, 2017.
Breaking locality accelerates block Gauss-Seidel. S. Tu, S. Venkataraman, A. Wilson, A. Gittens, M. I. Jordan, and B. Recht. In D. Precup and Y. W. Teh (Eds), Proceedings of the 34th International Conference on Machine Learning (ICML), Sydney, Australia, NY, 2017.
On the learnability of fully-connected neural networks. Y. Zhang, J. Lee, M. Wainwright, and M. I. Jordan. In A. Singh and J. Zhu (Eds.), Proceedings of the Twentieth Conference on Artificial Intelligence and Statistics (AISTATS), 2017.
Nonconvex finite-sum optimization via SCSG methods. L. Lei, C. Ju, J. Chen, and M. I. Jordan. In S. Bengio, R. Fergus, S. Vishwanathan, and H. Wallach (Eds),Advances in Neural Information Processing Systems (NIPS) 30, 2017.
Distributed optimization with arbitrary local solvers. C. Ma, J. Konecny, M. Jaggi, V. Smith, M. I. Jordan, P Richtarik, and M. Takac.Optimization Methods and Software, 4, 813-848, 2017. [_Most Read Paper Award_].
Gradient descent converges to minimizers. J. Lee, M. Simchowitz, M. I. Jordan, and B. Recht.Proceedings of the Conference on Learning Theory (COLT), New York, NY, 2016.
A linearly-convergent stochastic L-BFGS algorithm. P. Moritz, R. Nishihara, and M. I. Jordan.Proceedings of the Eighteenth Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain, 2016.
SparkNet: Training deep networks in Spark. P. Moritz, R. Nishihara, I. Stoica and M. I. Jordan.International Conference on Learning Representations (ICLR), Puerto Rico, 2016.
CYCLADES: Conflict-free asynchronous machine learning. X. Pan, M. Lam, S. Tu, D. Papailiopoulos, C. Zhang, M. I. Jordan, K. Ramchandran, C. Re, and B. Recht. In U. von Luxburg, I. Guyon, D. Lee, M. Sugiyama (Eds.),Advances in Neural Information Processing Systems (NIPS) 29, 2016.
Nested hierarchical Dirichlet processes. J. Paisley, C. Wang, D. Blei, and M. I. Jordan.IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 256-270, 2015.
Trust region policy optimization. J. Schulman, P. Moritz, S. Levine, M. I. Jordan, and P. Abbeel. In F. Bach and D. Blei (Eds.), Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France, 2015.[Long version]
A general analysis of the convergence of ADMM. R. Nishihara, L. Lessart, B. Recht, A. Packard, and M. I. Jordan. In F. Bach and D. Blei (Eds.), Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France, 2015.[Long version]
Parallel correlation clustering on big graphs. X. Pan, D. Papailiopoulos, S. Oymak, B. Recht, K. Ramchandran, and M. I. Jordan. In D. Lee, M. Sugiyama, C. Cortes and N. Lawrence (Eds.),Advances in Neural Information Processing Systems (NIPS) 28, 2015.
On the accuracy of self-normalized log-linear models. J. Andreas, M. Rabinovich, D. Klein, and M. I. Jordan. In D. Lee, M. Sugiyama, C. Cortes and N. Lawrence (Eds.),Advances in Neural Information Processing Systems (NIPS) 28, 2015.
Variational consensus Monte Carlo. M. Rabinovich, E. Angelino, and M. I. Jordan. In D. Lee, M. Sugiyama, C. Cortes and N. Lawrence (Eds.),Advances in Neural Information Processing Systems (NIPS) 28, 2015.
A scalable bootstrap for massive data. A. Kleiner, A. Talwalkar, P. Sarkar and M. I. Jordan.Journal of the Royal Statistical Society, Series B, 76, 795-816, 2014.
SMASH: A benchmarking toolkit for variant calling. A. Talwalkar, J. Liptrap, J. Newcomb, C. Hartl, J. Terhorst, K. Curtis, M Bresler, Y. Song, M. I. Jordan, and D. Patterson.Bioinformatics, DOI:10.1093/bioinformatics/btu345, 2014.
Scaling a crowd-sourced database. B. Mozafari, P. Sarkar, M. Franklin, M. I. Jordan, and S. Madden.Proceedings of the 41st International Conference on Very Large Data Bases (VLDB), Hawaii, USA, 2014.
Changepoint analysis for efficient variant calling. A. Bloniarz, A. Talwalkar, J. Terhorst, M. I. Jordan, D. Patterson, B. Yu, and Y. Song. International Conference on Research in Computational Molecular Biology (RECOMB), Pittsburgh, PA, 2014.
Mixed membership models for time series. E. Fox and M. I. Jordan. In E. Airoldi, D. Blei, E. A. Erosheva, and S. E. Fienberg (Eds.), Handbook of Mixed Membership Models and Their Applications, Chapman & Hall/CRC, 2014.
Mixed membership matrix factorization. L. Mackey, D. Weiss, and M. I. Jordan. In E. Airoldi, D. Blei, E. A. Erosheva, and S. E. Fienberg (Eds.), Handbook of Mixed Membership Models and Their Applications, Chapman & Hall/CRC, 2014.
Communication-efficient distributed dual coordinate ascent. M. Jaggi, V. Smith, M. Takac, J. Terhorst, T. Hofmann, and M. I. Jordan. In Z. Ghahramani, M. Welling, C. Cortes and N. Lawrence (Eds.),Advances in Neural Information Processing Systems (NIPS) 27, 2014.
Parallel double greedy submodular maximization. X. Pan, S. Jegelka, J. Gonzalez, J. Bradley, and M. I. Jordan. In Z. Ghahramani, M. Welling, C. Cortes and N. Lawrence (Eds.),Advances in Neural Information Processing Systems (NIPS) 27, 2014.
MLI: An API for distributed machine learning. E. Sparks, A. Talwalkar, V. Smith, J. Kottalam, X. Pan, J. Gonzalez, M. I. Jordan, M. Franklin, and T. Kraska. IEEE International Conference on Data Mining (ICDM), Dallas, TX, 2013.
Distributed low-rank subspace segmentation. L. Mackey, A. Talwalkar, Y. Mu, S-F. Chang, and M. I. Jordan. IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, 2013.
A general bootstrap performance diagnostic. A. Kleiner, A. Talwalkar, S. Agarwal, M. I. Jordan, and I. Stoica.ACM Conference on Knowledge Discovery and Data Mining (SIGKDD), Chicago, IL, 2013.
Streaming variational Bayes. T. Broderick, N. Boyd, A. Wibisono, A. Wilson and M. I. Jordan. In L. Bottou, C. Burges, Z. Ghahramani, and M. Welling (Eds.),Advances in Neural Information Processing Systems (NIPS) 26, 2013.
A million cancer genome warehouse. D. Haussler, D. A. Patterson, M. Diekhans, A. Fox, M. I. Jordan, A. D. Joseph, S. Ma, B. Paten, S. Shenker, T. Sittler and I. Stoica. Technical Report UCB/EECS-2012-211, Department of EECS, University of California, Berkeley, 2012.
The Big Data bootstrap. A. Kleiner, A. Talwalkar, P. Sarkar, and M. I. Jordan. In J. Langford and J. Pineau (Eds.), Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, UK, 2012.
Variational Bayesian inference with stochastic search. J. Paisley, D. Blei, and M. I. Jordan. In J. Langford and J. Pineau (Eds.), Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, UK, 2012.
Nonparametric link prediction in dynamic networks. P. Sarkar, D. Chakrabarti, and M. I. Jordan. In J. Langford and J. Pineau (Eds.), Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, UK, 2012.[Appendix].
Stick-breaking beta processes and the Poisson process. J. Paisley, D. Blei, and M. I. Jordan. In N. Lawrence and M. Girolami (Eds.),Proceedings of the Fifteenth Conference on Artificial Intelligence and Statistics (AISTATS), Canary Islands, Spain, 2012.
Ancestral sampling for particle Gibbs. F. Lindsten, M. I. Jordan, and T. Schön. In P. Bartlett, F. Pereira, L. Bottou and C. Burges (Eds.),Advances in Neural Information Processing Systems (NIPS) 25, 2012.
Privacy aware learning. J. Duchi, M. I. Jordan, and M. Wainwright. In P. Bartlett, F. Pereira, L. Bottou and C. Burges (Eds.),Advances in Neural Information Processing Systems (NIPS) 25, 2012.[Long version].
Learning low-dimensional signal models. L. Carin, R. G. Baraniuk, V. Cevher, D. Dunson, M. I. Jordan, G. Sapiro, and M. B. Wakin.IEEE Signal Processing Magazine, 28, 39-51, 2011.
Dimensionality reduction for spectral clustering. D. Niu, J. Dy, and M. I. Jordan. In G. Gordon and D. Dunson (Eds.),Proceedings of the Fourteenth Conference on Artificial Intelligence and Statistics (AISTATS), Ft. Lauderdale, FL, 2011.
Nonparametric combinatorial sequence models. F. Wauthier, M. I. Jordan, and N. Jojic.15th Annual International Conference on Research in Computational Molecular Biology (RECOMB), Vancouver, BC, 2011.
Visually relating gene expression and in vivo DNA binding data. M.-Y. Huang, L. Mackey, S. Keranen, G. Weber, M. I. Jordan, D. Knowles, M. Biggin, and B. Hamann._IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)_Atlanta, GA, 2011.
Ergodic subgradient descent. J. C. Duchi, A. Agarwal, M. Johansson, and M. I. Jordan. Forty-Ninth Annual Allerton Conference on Communication, Control, and Computing, Urbana-Champaign, IL, 2011.
Bayesian bias mitigation for crowdsourcing. F. L. Wauthier and M. I. Jordan. In J. Shawe-Taylor, R. Zemel, P. Bartlett and F. Pereira (Eds.),Advances in Neural Information Processing Systems (NIPS) 24, 2011.
Divide-and-conquer matrix factorization. L. Mackey, A. Talwalkar and M. I. Jordan. In J. Shawe-Taylor, R. Zemel, P. Bartlett and F. Pereira (Eds.),Advances in Neural Information Processing Systems (NIPS) 24, 2011.[Long version].
Hierarchical models, nested models and completely random measures. M. I. Jordan. In M.-H. Chen, D. Dey, P. Mueller, D. Sun, and K. Ye (Eds.), Frontiers of Statistical Decision Making and Bayesian Analysis: In Honor of James O. Berger, New York: Springer, 2010.
Leo Breiman. M. I. Jordan.Annals of Applied Statistics, 4, 1642-1643, 2010.
Hierarchical Bayesian nonparametric models with applications. Y. W. Teh and M. I. Jordan. In N. Hjort, C. Holmes, P. Mueller, and S. Walker (Eds.), Bayesian Nonparametrics: Principles and Practice, Cambridge, UK: Cambridge University Press, 2010.
On the consistency of ranking algorithms. J. Duchi, L. Mackey, and M. I. Jordan. Proceedings of the 27th International Conference on Machine Learning (ICML), Haifa, Israel, 2010. [_Best Student Paper Award_].
Matrix-variate Dirichlet process mixture models. Z. Zhang, G. Dai, and M. I. Jordan.Proceedings of the Thirteenth Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia, Italy, 2010.
Inference and learning in networks of queues. C. Sutton and M. I. Jordan.Proceedings of the Thirteenth Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia, Italy, 2010.
Bayesian generalized kernel models. Z. Zhang, G. Dai, D. Wang, and M. I. Jordan.Proceedings of the Thirteenth Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia, Italy, 2010.
Type-based MCMC. P. Liang, M. I. Jordan, and D. Klein.The 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), Los Angeles, CA, 2010.
Heavy-tailed process priors for selective shrinkage. F. L. Wauthier and M. I. Jordan. In J. Lafferty and C. Williams and J. Shawe-Taylor and R. Zemel and A. Culotta (Eds.),Advances in Neural Information Processing Systems (NIPS) 23, 2010.
Tree-structured stick breaking for hierarchical data. R. Adams, Z. Ghahramani, and M. I. Jordan. In J. Lafferty and C. Williams and J. Shawe-Taylor and R. Zemel and A. Culotta (Eds.),Advances in Neural Information Processing Systems (NIPS) 23, 2010.
Optimization of structured mean field objectives. A. Bouchard-Côté and M. I. Jordan. In Uncertainty in Artificial Intelligence (UAI), Proceedings of the Twenty-Fifth Conference, Montreal, Canada, 2009.
Automatic exploration of datacenter performance regimes. P. Bodik, R. Griffith, C. Sutton, A. Fox, M. I. Jordan, and D. Patterson. First Workshop on Automated Control for Datacenters and Clouds (ACDC), Barcelona, Spain, 2009.
Sharing features among dynamical systems with beta processes. E. Fox, E. Sudderth, M. I. Jordan, and A. S. Willsky. In Y. Bengio, D. Schuurmans, J. Lafferty and C. Williams (Eds.),Advances in Neural Information Processing Systems (NIPS) 22, 2009.
Nonparametric latent feature models for link prediction. K. Miller, T. Griffiths, and M. I. Jordan. In Y. Bengio, D. Schuurmans, J. Lafferty and C. Williams (Eds.),Advances in Neural Information Processing Systems (NIPS) 22, 2009.
An asymptotic analysis of smooth regularizers. P. Liang, F. Bach, G. Bouchard, and M. I. Jordan. In Y. Bengio, D. Schuurmans, J. Lafferty and C. Williams (Eds.),Advances in Neural Information Processing Systems (NIPS) 22, 2009.
An HDP-HMM for systems with state persistence. E. Fox, E. Sudderth, M. I. Jordan, and A. Willsky. Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, 2008.
Spectral clustering for speech separation. F. R. Bach and M. I. Jordan. In J. Keshet and S. Bengio (Eds.),Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods. New York: John Wiley, 2008.
Efficient inference in phylogenetic InDel trees. A. Bouchard-Côté, M. I. Jordan, and D. Klein. In D. Koller, Y. Bengio, D. Schuurmans and L. Bottou (Eds.),Advances in Neural Information Processing Systems (NIPS) 21, 2008.
Spectral clustering with perturbed data. L. Huang, D. Yan, M. I. Jordan, and N. Taft. In D. Koller, Y. Bengio, D. Schuurmans and L. Bottou (Eds.),Advances in Neural Information Processing Systems (NIPS) 21, 2008.[Long version].
Agreement-based learning. P. Liang, D. Klein and M. I. Jordan. In J. Platt, D. Koller, Y. Singer and A. McCallum (Eds.),Advances in Neural Information Processing Systems (NIPS) 20, 2007.
Word alignment via quadratic assignment. S. Lacoste-Julien, B. Taskar, D. Klein, and M. I. Jordan.Proceedings of the North American Chapter of the Association for Computational Linguistics Annual Meeting (HLT-NAACL), 2006.
Advanced tools for operators at Amazon.com. P. Bodik, A. Fox, M. I. Jordan, D. Patterson, A. Banerjee, R. Jagannathan, T. Su, S. Tenginakai, B. Turner, and J. Ingalls. First Workshop on Hot Topics in Autonomic Computing (HotAC), Dublin, Ireland, 2006.
In-network PCA and anomaly detection. L. Huang, X. Nguyen, M. Garofalakis, M. I. Jordan, A. Joseph, and N. Taft. In B. Schoelkopf, J. Platt and T. Hofmann (Eds.),Advances in Neural Information Processing Systems (NIPS) 19, 2006.[Long version].
The DLR hierarchy of approximate inference. M. Rosen-Zvi, M. I. Jordan, and A. Yuille. In Uncertainty in Artificial Intelligence (UAI), Proceedings of the Twenty-First Conference, 2005.
A variational principle for graphical models. M. J. Wainwright and M. I. Jordan.New Directions in Statistical Signal Processing: From Systems to Brain. Cambridge, MA: MIT Press, 2005.
Scalable statistical bug isolation. B. Liblit, M. Naik, A. X. Zheng, A. Aiken, and M. I. Jordan. ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 2005.[Software]
Extensions of the informative vector machine. N. D. Lawrence, J. C. Platt, & M. I. Jordan. In J. Winkler and N. D. Lawrence and M. Niranjan (Eds.),Proceedings of the Sheffield Machine Learning Workshop, Lecture Notes in Computer Science, New York: Springer, 2005.
Semiparametric latent factor models. Y. W. Teh, M. Seeger, and M. I. Jordan.Proceedings of the Eighth Conference on Artificial Intelligence and Statistics (AISTATS), 2005.
Robust design of biological experiments. P. Flaherty, M. I. Jordan and A. P. Arkin. In Y. Weiss and B. Schoelkopf and J. Platt (Eds.),Advances in Neural Information Processing Systems (NIPS) 18, 2005.
Kalman filtering with intermittent observations. B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. I. Jordan, and S. Sastry. IEEE Transactions on Automatic Control, 49, 1453-1464, 2004.
Failure diagnosis using decision trees. M. Chen, A. X. Zheng, J. Lloyd, M. I. Jordan, and E. Brewer. International Conference on Autonomic Computing (ICAC), 2004.
Semi-supervised learning via Gaussian processes. N. D. Lawrence and M. I. Jordan. In L. Saul, Y. Weiss, and L. Bottou (Eds.),Advances in Neural Information Processing Systems (NIPS) 17, 2004.
Matching words and pictures. K. Barnard, P. Duygulu, N. de Freitas, D. A. Forsyth, D. M. Blei, and M. I. Jordan.Journal of Machine Learning Research, 3, 1107-1135, 2003.
Modeling annotated data. D. M. Blei and M. I. Jordan.26th International Conference on Research and Development in Information Retrieval (SIGIR), New York: ACM Press, 2003. [_SIGIR Test of Time Honorable Mention_].
Bug isolation via remote program sampling. B. Liblit, A. Aiken, A. X. Zheng, and M. I. Jordan. ACM SIGPLAN 2003 Conference on Programming Language Design and Implementation (PLDI), San Diego, 2003.
Learning spectral clustering. F. R. Bach and M. I. Jordan. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.),Advances in Neural Information Processing Systems (NIPS) 16, (long version), 2003.
Statistical debugging of sampled programs. A. X. Zheng, M. I. Jordan, B. Liblit, and A. Aiken. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.),Advances in Neural Information Processing Systems (NIPS) 16, 2003.
Autonomous helicopter flight via reinforcement learning. A. Y. Ng, H. J. Kim, M. I. Jordan, and S. Sastry. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.),Advances in Neural Information Processing Systems (NIPS) 16, 2003.
Kalman filtering with intermittent observations. B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. I. Jordan, and S. Sastry.42nd IEEE Conference on Decision and Control (CDC), 2004.
Sampling user executions for bug isolation. B. Liblit, A. Aiken, A. X. Zheng, and M. I. Jordan.Workshop on Remote Analysis and Measurement of Software Systems (RAMSS), 2003.
Graphical models: Probabilistic inference. M. I. Jordan and Y. Weiss. In M. Arbib (Ed.),The Handbook of Brain Theory and Neural Networks, 2nd edition. Cambridge, MA: MIT Press, 2002.
Loopy belief propagation and Gibbs measures. S. Tatikonda and M. I. Jordan. In D. Koller and A. Darwiche (Eds)., Uncertainty in Artificial Intelligence (UAI), Proceedings of the Eighteenth Conference, 2002.
Tree-dependent component analysis. F. R. Bach and M. I. Jordan. In D. Koller and A. Darwiche (Eds)., Uncertainty in Artificial Intelligence (UAI), Proceedings of the Eighteenth Conference, 2002. [Matlab code]
Learning the kernel matrix with semidefinite programming. G. R. G. Lanckriet, P. L. Bartlett, N. Cristianini, L. El Ghaoui, and M. I. Jordan.Machine Learning: Proceedings of the Nineteenth International Conference (ICML), San Mateo, CA: Morgan Kaufmann, 2002.
Learning graphical models with Mercer kernels. F. R. Bach and M. I. Jordan. In S. Becker, S. Thrun, and K. Obermayer (Eds.), Advances in Neural Information Processing Systems (NIPS) 15, 2002.
Robust novelty detection with single-class MPM. G. R. G. Lanckriet, L. El Ghaoui, and M. I. Jordan. In S. Becker, S. Thrun, and K. Obermayer (Eds.), Advances in Neural Information Processing Systems (NIPS) 15, 2002.
Learning in modular and hierarchical systems. M. I. Jordan and R. A. Jacobs. In M. Arbib (Ed.),The Handbook of Brain Theory and Neural Networks, 2nd edition. Cambridge, MA: MIT Press, 2002.
2001
Stable algorithms for link analysis. A. Y. Ng, A. X. Zheng, and M. I. Jordan. Proceedings of the 24th International Conference on Research and Development in Information Retrieval (SIGIR), New York, NY: ACM Press, 2001.
Efficient stepwise selection in decomposable models. A. Deshpande, M. N. Garofalakis, and M. I. Jordan. In J. Breese and D. Koller (Ed)., Uncertainty in Artificial Intelligence (UAI), Proceedings of the Seventeenth Conference, 2001.
Variational MCMC. N. de Freitas, P. Højen-Sørensen, M. I. Jordan, and S. Russell. In J. Breese and D. Koller (Ed)., Uncertainty in Artificial Intelligence (UAI), Proceedings of the Seventeenth Conference, 2001.
Thin junction trees. F. R. Bach and M. I. Jordan. In T. Dietterich, S. Becker and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (NIPS) 14, 2001.
On spectral clustering: Analysis and an algorithm. A. Y. Ng, M. I. Jordan, and Y. Weiss. In T. Dietterich, S. Becker and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (NIPS) 14, 2001.
Minimax probability machine. G. R. G. Lanckriet, L. El Ghaoui, C. Bhattacharyya, and M. I. Jordan. In T. Dietterich, S. Becker and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (NIPS) 14, 2001.
Latent Dirichlet allocation. D. M. Blei, A. Y. Ng and M. I. Jordan. In T. Dietterich, S. Becker and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (NIPS) 14, 2001,[Long version],[software].
Are reaching movements planned to be straight and invariant in the extrinsic space? M. Desmurget, C. Prablanc, M. I. Jordan, and M. Jeannerod, M.Quarterly Journal of Experimental Psychology, 52, 981-1020, 1999.
Computational motor control. M. I. Jordan and D. M. Wolpert. In M. Gazzaniga (Ed.), The Cognitive Neurosciences, 2nd edition, Cambridge: MIT Press, 1999.
Recurrent networks. M. I. Jordan. In R. A. Wilson and F. C. Keil (Eds.),The MIT Encyclopedia of the Cognitive Sciences, Cambridge, MA: MIT Press, 1999.
Neural networks. M. I. Jordan. In R. A. Wilson and F. C. Keil (Eds.),The MIT Encyclopedia of the Cognitive Sciences, Cambridge, MA: MIT Press, 1999.
Computational intelligence. M. I. Jordan, and S. Russell In R. A. Wilson and F. C. Keil (Eds.),The MIT Encyclopedia of the Cognitive Sciences, Cambridge, MA: MIT Press, 1999.
Learning from dyadic data. T. Hofmann, J. Puzicha, and M. I. Jordan. In Kearns, M. S., Solla, S. A., and Cohn, D. (Eds.),Advances in Neural Information Processing Systems (NIPS) 11, Cambridge MA: MIT Press, 1998.
Viewing the hand prior to movement improves accuracy of pointing performed toward the unseen contralateral hand. M. Desmurget, Y. Rossetti, M. I. Jordan, C. Meckler, and C. Prablanc.Experimental Brain Research, 115, 180--186, 1997.
Constrained and unconstrained movements involve different control strategies. M. Desmurget, M. I. Jordan, C. Prablanc, and M. Jeannerod.Journal of Neurophysiology, 77, 1644--1650, 1997.
Approximating posterior distributions in belief networks using mixtures. C. M. Bishop, N. D. Lawrence, T. S. Jaakkola, and M. I. Jordan. In Jordan, M. I., Kearns, M. J. and Solla, S. A. (Eds.), Advances in Neural Information Processing Systems (NIPS) 10, Cambridge, MA: MIT Press, 1997.
Estimating dependency structure as a hidden variable. M. Meila and M. I. Jordan. In Jordan, M. I., Kearns, M. J. and Solla, S. A. (Eds.), Advances in Neural Information Processing Systems (NIPS) 10, Cambridge, MA: MIT Press, 1997.
Adaptation in speech motor control. J. F. Houde and M. I. Jordan. In Jordan, M. I., Kearns, M. J. and Solla, S. A. (Eds.), Advances in Neural Information Processing Systems (NIPS) 10, Cambridge, MA: MIT Press, 1997.
Neural networks. M. I. Jordan and C. Bishop. In Tucker, A. B. (Ed.), CRC Handbook of Computer Science, Boca Raton, FL: CRC Press, 1997.
Computational models of sensorimotor organization. Z. Ghahramani, D. M. Wolpert, and M. I. Jordan. In P. Morasso and V. Sanguineti (Eds.), Self-Organization Computational Maps and Motor Control, Amsterdam: North-Holland, 1997.
Mixture models for learning from incomplete data. Z. Ghahramani and M. I. Jordan. In Greiner, R., Petsche, T., and Hanson, S. J. (Eds.), Computational Learning Theory and Natural Learning Systems, Cambridge, MA: MIT Press, 1997.
Active learning with statistical models. D. Cohn, Z. Ghahramani, and M. I. Jordan. In Murray-Smith, R., and Johansen, T. A. (Eds.), Multiple Model Approaches to Modelling and Control, London: Taylor and Francis, 1997.
An objective function for belief net triangulation. M. Meila and M. I. Jordan. In D. Madigan and P. Smyth (Eds.), Proceedings of the 1997 Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL, 1997.
Markov mixtures of experts. M. Meila and M. I. Jordan. In Murray-Smith, R., and Johansen, T. A. (Eds.), Multiple Model Approaches to Modelling and Control, London: Taylor and Francis, 1997.
Serial order: A parallel, distributed processing approach. M. I. Jordan. In J. W. Donahoe and V. P. Dorsel, (Eds.). Neural-network Models of Cognition: Biobehavioral Foundations, Amsterdam: Elsevier Science Press, 1997.
Optimal triangulation with continuous cost functions. M. Meila and M. I. Jordan. In M. C. Mozer, M. I. Jordan, and T. Petsche (Eds.), Advances in Neural Information Processing Systems (NIPS) 9, Cambridge MA: MIT Press, 1996.
A variational principle for model-based interpolation. L. K. Saul and M. I. Jordan. In M. C. Mozer, M. I. Jordan, and T. Petsche (Eds.), Advances in Neural Information Processing Systems (NIPS) 9, Cambridge MA: MIT Press, 1996.
Hidden Markov decision trees. M. I. Jordan, Z. Ghahramani, and L. K. Saul. In M. C. Mozer, M. I. Jordan, and T. Petsche (Eds.), Advances in Neural Information Processing Systems (NIPS) 9, Cambridge MA: MIT Press, 1996.
The organization of action sequences: Evidence from a relearning task. M. I. Jordan.Journal of Motor Behavior, 27, 179--192, 1995.
Adaptation in speech production to transformed auditory feedback. J. Houde and M. I. Jordan. Journal of the Acoustical Society of America, 97, 3243.
Fast learning by bounding likelihoods in sigmoid belief networks. T. S. Jaakkola, L. K. Saul, and M. I. Jordan. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo (Eds.), Advances in Neural Information Processing Systems (NIPS) 8, Cambridge MA: MIT Press, 1995.
Reinforcement learning by probability matching. P. N. Sabes and M. I. Jordan. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo (Eds.), Advances in Neural Information Processing Systems (NIPS) 8, Cambridge MA: MIT Press, 1995.
Markov mixtures of experts. M. Meila and M. I. Jordan. In D. Touretzky, M. Mozer, and M. Hasselmo (Eds.), Advances in Neural Information Processing Systems (NIPS) 8, MIT Press, 1995.
Factorial Hidden Markov models. Z. Ghahramani and M. I. Jordan. In D. Touretzky, M. Mozer, and M. Hasselmo (Eds.), Advances in Neural Information Processing Systems (NIPS) 8, MIT Press, 1995.
Learning in modular and hierarchical systems. M. I. Jordan and R. A. Jacobs. In M. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks, Cambridge, MA: MIT Press, 1995.
The moving basin: Effective action-search in adaptive control. W. Fun and M. I. Jordan, M. I.Proceedings of the World Conference on Neural Networks, Washington, DC, 1995.
Goal-based speech motor control: A theoretical framework and some preliminary data. J. S. Perkell, M. L. Matthies, M. A. Svirsky, and M. I. Jordan. In D. A. Robin, K. M. Yorkston, and D. R. Beukelman (Eds.), Disorders of Motor Speech: Assessment, Treatment, and Clinical Characterization, Baltimore, MD: Brookes Publishing Co, 1993.
A model of the learning of arm trajectories from spatial targets. M. I. Jordan, T. Flash, and Y. Arnon. Journal of Cognitive Neuroscience, 6, 359--376, 1994.
A statistical approach to decision tree modeling. M. I. Jordan. In M. Warmuth (Ed.), Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, New York: ACM Press, 1994.
Learning from incomplete data. Z. Ghahramani and M. I. Jordan. MIT Center for Biological and Computational Learning Technical Report 108, 1994.
Theoretical and experimental studies of convergence properties of EM algorithm based on finite Gaussian mixtures. L. Xu and M. I. Jordan, M. I.Proceedings of the 1994 International Symposium on Artificial Neural Networks, Tainan, Taiwan, pp. 380--385, 1994.
A statistical approach to decision tree modeling. M. I. Jordan. In M. Warmuth (Ed.), Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, New York: ACM Press, 1994.
Boltzmann chains and hidden Markov Models. L. K. Saul and M. I. Jordan. In G. Tesauro, D. S. Touretzky and T. K. Leen, (Eds.), Advances in Neural Information Processing Systems (NIPS) 7, MIT Press, 1994.
Reinforcement learning with soft state aggregation. S. P. Singh, T. S. Jaakkola, and M. I. Jordan. In G. Tesauro, D. S. Touretzky and T. K. Leen, (Eds.),Advances in Neural Information Processing Systems (NIPS) 7, Cambridge, MA: MIT Press, 1994.
Computational structure of coordinate transformations: A generalization study. Z. Ghahramani, D. M. Wolpert, and M. I. Jordan. In G. Tesauro, D. S. Touretzky and T. K. Leen, (Eds.),Advances in Neural Information Processing Systems (NIPS) 8, Cambridge, MA: MIT Press, 1994.
Neural forward dynamic models in human motor control: Psychophysical evidence. D. M. Wolpert, Z. Ghahramani, and M. I. Jordan. In G. Tesauro, D. S. Touretzky and T. K. Leen, (Eds.),Advances in Neural Information Processing Systems (NIPS) 7, Cambridge, MA: MIT Press, 1994.
An alternative model for mixtures of experts. L. Xu, M. I. Jordan, and G. E. Hinton. In G. Tesauro, D. S. Touretzky and T. K. Leen, (Eds.),Advances in Neural Information Processing Systems (NIPS) 7, Cambridge, MA: MIT Press, 1994.
Active learning with statistical models. D. Cohn, Z. Ghahramani, and M. I. Jordan. In G. Tesauro, D. S. Touretzky and T. K. Leen, (Eds.),Advances in Neural Information Processing Systems (NIPS) 7, Cambridge, MA: MIT Press, 1994.
Learning piecewise control strategies in a modular neural network architecture. R. A. Jacobs and M. I. Jordan.IEEE Transactions on Systems, Man, and Cybernetics, 23, 337--345, 1993.
Trading relations between tongue-body raising and lip rounding in production of the vowel /u/: A pilot motor equivalence study. J. S. Perkell, M. L. Matthies, M. A. Svirsky, and M. I. Jordan.Journal of the Acoustical Society of America, 93, 2948--2961, 1993.
Supervised learning and divide-and-conquer: A statistical approach. M. I. Jordan, and R. A. Jacobs. In P. E. Utgoff, (Ed.), Machine Learning: Proceedings of the Tenth International Workshop, San Mateo, CA: Morgan Kaufmann, 1993.
A dynamical model of priming and repetition blindness. D. Bavelier and M. I. Jordan. In Hanson, S. J., Cowan, J. D., and Giles, C. L., (Eds.), Advances in Neural Information Processing Systems (NIPS) 5, San Mateo, CA: Morgan Kaufmann, 1992.
EM learning of a generalized finite mixture model for combining multiple classifiers. L. Xu and M. I. Jordan. Proceedings of the World Conference on Neural Networks, Portland, OR, pp. 431--434, 1993.
The cascade neural network model and a speed-accuracy tradeoff of arm movement. M. Hirayama, M. Kawato, and M. I. Jordan. Journal of Motor Behavior, 25, 162--175, 1993.
Constrained supervised learning. M. I. Jordan. Journal of Mathematical Psychology, 36, 396--425, 1992.
Computational consequences of a bias towards short connections. R. A. Jacobs and M. I. Jordan. Journal of Cognitive Neuroscience, 4, 331--344, 1992.
Hierarchies of adaptive experts. M. I. Jordan and R. A. Jacobs. In J. Moody, S. Hanson, and R. Lippmann (Eds.),Advances in Neural Information Processing Systems (NIPS) 4, San Mateo, CA: Morgan Kaufmann, 1991.
Forward dynamics modeling of speech motor control using physiological data. M. Hirayama, E. Vatikiotis-Bateson, M. Kawato, and M. I. Jordan. In J. Moody, S. Hanson, and R. Lippmann (Eds.),Advances in Neural Information Processing Systems (NIPS) 4, San Mateo, CA: Morgan Kaufmann, 1991.
Supervised learning and excess degrees of freedom. Jordan, M. I. In P. Mehra, and B. Wah, (Eds.), Artificial Neural Networks: Concepts and Theory, Los Alamitos, CA: IEEE Computer Society Press, 1992.
Optimal control: A foundation for intelligent control. D. A. White and M. I. Jordan. In D. A. White, and D. A. Sofge (Eds.), Handbook of Intelligent Control, Amsterdam: Van Nostrand, 1992.
Constraints on underspecified target trajectories. M. I. Jordan. In P. Dario, G. Sandini, and P. Aebischer, (Eds.),Robots and Biological Systems: Toward a New Bionics, Heidelberg: Springer-Verlag, 1992.
A more biologically plausible learning network model for neural networks. P. Mazzoni, R. Andersen, and M. I. Jordan.Proceedings of the National Academy of Sciences, 88, 4433--4437, 1991.
Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks. R. A. Jacobs, M. I. Jordan, and A. G. Barto.Cognitive Science, 15, 219--250, 1991.
Internal world models and supervised learning. M. I. Jordan, and D. E. Rumelhart. In L. Birnbaum and G. Collins, (Eds.),Machine Learning: Proceedings of the Eighth International Workshop, San Mateo, CA: Morgan Kaufmann, pp. 70--75, 1991.
A competitive modular connectionist architecture. R. A. Jacobs and M. I. Jordan. In D. Touretzky (Ed.), Advances in Neural Information Processing Systems (NIPS) 4, San Mateo, CA: Morgan Kaufmann, 1991.
Speech motor control model using electromyography. M. Hirayama, E. Vatikiotis-Bateson, M. Kawato, and M. I. Jordan.INCN Conference on Speech Communications, 39--46, 1991.
A modular connectionist architecture for learning piecewise control strategies. R. A. Jacobs and M. I. Jordan.Proceedings of the 1991 American Control Conference, Boston, MA, pp. 343--351, 1991. [_Best Paper Award_].
A more biologically plausible learning rule than backpropagation applied to a network model of cortical area 7a. P. Mazzoni, R. Andersen, and M. I. Jordan.Cerebral Cortex, 1, 293--307, 1991.
Modularity, supervised learning, and unsupervised learning. M. I. Jordan, and R. A. Jacobs. In S. Davis (Ed.), Connectionism: Theory and practice, Oxford: Oxford University Press, 1991.
A non-empiricist perspective on learning in layered networks. M. I. Jordan.Behavioral and Brain Sciences, 13, 497--498, 1990.
Simulation of vocalic gestures using an articulatory model driven by a sequential neural network. G. Bailly, M. I. Jordan, M. Mantakas, J-L. Schwartz, M. Bach, and O. Olesen. Journal of the Acoustical Society of America, 87:S105, 1990.
A competitive modular connectionist architecture. M. I. Jordan, and R. A. Jacobs. In R. Lippmann and J. Moody and D. Touretzky (Eds.), Advances in Neural Information Processing Systems (NIPS) 3, San Mateo, CA: Morgan Kaufmann, pp. 324--331, 1990.
AR-P learning applied to a network model of cortical area 7a. P. Mazzoni, R. Andersen, and M. I. Jordan.Proceedings of the International Joint Conference On Neural Networks, San Diego, CA, pp. 373--379, 1990.
Motor learning and the degrees of freedom problem. M. I. Jordan.Attention and Performance, XIII, 796--836, 1990.
Learning inverse mappings with forward models. M. I. Jordan. In K. S. Narendra (Ed.), Proceedings of the Sixth Yale Workshop on Adaptive and Learning Systems, New York: Plenum Press, 1990.
Action. M. I. Jordan, and D. A. Rosenbaum. In M. I. Posner (Ed.), Foundations of Cognitive Science, Cambridge, MA: MIT Press, 1989.
Learning to control an unstable system with forward modeling. M. I. Jordan, and R. A. Jacobs. In D. Touretzky (Ed.), Advances in Neural Information Processing Systems (NIPS) 2, San Mateo, CA: Morgan Kaufmann, pp. 324--331, 1989.
Gradient following without backpropagation in layered networks. A. G. Barto and M. I. Jordan. Proceedings of the IEEE First Annual International Conference on Neural Networks, New York: IEEE Publishing Services, 1987.
An introduction to linear algebra in parallel, distributed processing. M. I. Jordan. In D. E. Rumelhart and J. L. McClelland, (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Cambridge, MA: MIT Press, 1986.
Attractor dynamics and parallelism in a connectionist sequential machine. M. I. Jordan.Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Englewood Cliffs, NJ: Erlbaum, pp. 531--546. [Reprinted in IEEE Tutorials Series, New York: IEEE Publishing Services, 1990], 1986.