Optimization
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) 21, 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 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.
Structured prediction via the extragradient method.
B. Taskar, S. Lacoste-Julien and M. I. Jordan.
To appear, Advances in Neural Information Processing Systems
(NIPS) 18, 2005.
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.
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, 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.
Convexity, classification, and risk bounds.
P. L. Bartlett, M. I. Jordan, and J. D. McAuliffe.
Technical Report 638, Department of Statistics,
University of California, Berkeley, 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.