Optimization

  • On the convergence rate of decomposable submodular function minimization. R. Nishihara, S. Jegelka, and M. I. Jordan. In Z. Ghahramani, M. Welling, C. Cortes and N. Lawrence (Eds.), Advances in Neural Information Processing Systems (NIPS) 27, to appear.

  • 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, to appear.

  • 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, to appear.

  • Optimal rates for zero-order optimization: the power of two function evaluations. J. Duchi, M. I. Jordan, M. Wainwright, and A. Wibisono. arXiv:1312.2139, 2013.

  • Computational and statistical tradeoffs via convex relaxation. V. Chandrasekaran and M. I. Jordan. Proceedings of the National Academy of Sciences, 110, E1181-E1190, 2013.

  • The asymptotics of ranking algorithms. J. Duchi, L. Mackey, and M. I. Jordan. Annals of Statistics, 4, 2292-2323, 2013.

  • MAD-Bayes: MAP-based asymptotic derivations from Bayes. T. Broderick, B. Kulis, and M. I. Jordan. In S. Dasgupta and D. McAllester (Eds.), Proceedings of the 30th International Conference on Machine Learning (ICML), Atlanta, GA, 2013. [Supplementary information].

  • Finite sample convergence rates of zero-order stochastic optimization methods. J. Duchi, M. I. Jordan, M. Wainwright, and A. Wibisono. In P. Bartlett, F. Pereira, L. Bottou and C. Burges (Eds.), Advances in Neural Information Processing Systems (NIPS) 25, 2013.

  • Small-variance asymptotics for exponential family Dirichlet process mixture models. K. Jiang, B. Kulis, and M. I. Jordan. In P. Bartlett, F. Pereira, L. Bottou and C. Burges (Eds.), Advances in Neural Information Processing Systems (NIPS) 25, 2013.

  • Ergodic mirror descent. J. C. Duchi, A. Agarwal, M. Johansson, and M. I. Jordan. SIAM Journal of Optimization, 22, 1549-1578, 2012.

  • Variational inference over combinatorial spaces. A. Bouchard-Côté and M. I. Jordan. In J. Shawe-Taylor, R. Zemel, J. Lafferty, and C. Williams (Eds.) Advances in Neural Information Processing Systems (NIPS) 23, 2011. [Supplementary information].

  • Random conic pursuit for semidefinite programming. A. Kleiner, A. Rahimi, and M. I. Jordan. In J. Shawe-Taylor, R. Zemel, J. Lafferty, and C. Williams (Eds.) Advances in Neural Information Processing Systems (NIPS) 23, 2011.

  • Estimating divergence functionals and the likelihood ratio by convex risk minimization. X. Nguyen, M. J. Wainwright and M. I. Jordan. IEEE Transactions on Information Theory, 56, 5847-5861, 2010.

  • Feature selection methods for improving protein structure prediction with Rosetta. B. Blum, M. I. Jordan, D. Kim, R. Das, P. Bradley, and D. Baker. In J. Platt, D. Koller, Y. Singer and A. McCallum (Eds.), Advances in Neural Information Processing Systems (NIPS) 20, 2008.

  • A direct formulation for sparse PCA using semidefinite programming. A. d'Aspremont, L. El Ghaoui, M. I. Jordan, and G. R. G. Lanckriet. SIAM Review, 49, 434-448, 2007. [Winner of the 2008 SIAM Activity Group on Optimization Prize]. [Software].

  • Log-determinant relaxation for approximate inference in discrete Markov random fields. M. J. Wainwright and M. I. Jordan. IEEE Transactions on Signal Processing, 54, 2099-2109, 2006.

  • Structured prediction, dual extragradient and Bregman projections. B. Taskar, S. Lacoste-Julien and M. I. Jordan. Journal of Machine Learning Research, 7, 1627-1653, 2006.

  • Convexity, classification, and risk bounds. P. L. Bartlett, M. I. Jordan, and J. D. McAuliffe. Journal of the American Statistical Association, 101, 138-156, 2006.

  • Structured prediction via the extragradient method. B. Taskar, S. Lacoste-Julien and M. I. Jordan. Advances in Neural Information Processing Systems (NIPS) 18, 2006.

  • Treewidth-based conditions for exactness of the Sherali-Adams and Lasserre relaxations. M. J. Wainwright and M. I. Jordan. Technical Report 671, Department of Statistics, University of California, Berkeley, 2004.

  • Multiple kernel learning, conic duality, and the SMO algorithm. F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan. Proceedings of the 21st International Conference on Machine Learning (ICML), 2004. [Long version]. [Software].

  • Semidefinite relaxations for approximate inference on graphs with cycles. M. J. Wainwright and M. I. Jordan. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.), Advances in Neural Information Processing Systems (NIPS) 16, (long version), 2004.

  • Learning the kernel matrix with semidefinite programming. G. R. G. Lanckriet, N. Cristianini, L. El Ghaoui, P. L. Bartlett, and M. I. Jordan. Journal of Machine Learning Research, 5, 27-72, 2004.

  • Robust sparse hyperplane classifiers: application to uncertain molecular profiling data. C. Bhattacharyya, L. R. Grate, M. I. Jordan, L. El Ghaoui, and Mian, I. S. In press: Journal of Computational Biology, 2004.

  • Graphical models, exponential families, and variational inference. M. J. Wainwright and M. I. Jordan. Technical Report 649, Department of Statistics, University of California, Berkeley, 2003.

  • Variational inference in graphical models: The view from the marginal polytope. M. J. Wainwright and M. I. Jordan. Forty-first Annual Allerton Conference on Communication, Control, and Computing, Urbana-Champaign, IL, 2003.

  • Distance metric learning, with application to clustering with side-information. E. P. Xing, A. Y. Ng, M. I. Jordan and S. Russell. In S. Becker, S. Thrun, and K. Obermayer (Eds.), Advances in Neural Information Processing Systems (NIPS) 15, 2003.

  • A robust minimax approach to classification. G. R. G. Lanckriet, L. El Ghaoui, C. Bhattacharyya, and M. I. Jordan. Journal of Machine Learning Research, 3, 552-582, 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.

  • 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, 2002.

  • PEGASUS: A policy search method for large MDPs and POMDPs. A. Y. Ng and M. I. Jordan. Uncertainty in Artificial Intelligence (UAI), Proceedings of the Sixteenth Conference, 2000.

  • 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, 1997.