[1] Sham M. Kakade, Shai Shalev-Shwartz, and Ambuj Tewari. Regularization techniques for learning with matrices, 2010. preprint.
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[2] Ankan Saha and Ambuj Tewari. On the finite time convergence of cyclic coordinate descent methods, 2010. preprint.
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[3] John Duchi, Shai Shalev-Shwartz, Yoram Singer, and Ambuj Tewari. Composite objective mirror descent. In Proceedings of the 23rd Annual Conference on Learning Theory, 2010.
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[4] Karthik Sridharan and Ambuj Tewari. Convex games in Banach spaces. In Proceedings of the 23rd Annual Conference on Learning Theory, 2010.
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[5] Sham M. Kakade, Ohad Shamir, Karthik Sridharan, and Ambuj Tewari. Learning exponential families in high-dimensions: Strong convexity and sparsity. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, volume 9 of JMLR Workshop and Conference Proceedings, pages 381-388, 2010.
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[6] Peter L. Bartlett and Ambuj Tewari. REGAL: A regularization based algorithm for reinforcement learning in weakly communicating MDPs. In Proceedings of the 25th Annual Conference on Uncertainty in Artificial Intelligence, 2009.
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[7] Shai Shalev-Shwartz and Ambuj Tewari. Stochastic methods for l1 regularized loss minimization. In Proceedings of the 26th International Conference on Machine Learning, pages 929-936. ACM Press, 2009.
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[8] Sham M. Kakade and Ambuj Tewari. On the generalization ability of online strongly convex programming algorithms. In Advances in Neural Information Processing Systems 21, pages 801-808. MIT Press, 2009.
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[9] Sham M. Kakade, Karthik Sridharan, and Ambuj Tewari. On the complexity of linear prediction: Risk bounds, margin bounds, and regularization. In Advances in Neural Information Processing Systems 21, pages 793-800. MIT Press, 2009.
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[10] Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham M. Kakade, Alexander Rakhlin, and Ambuj Tewari. High-probability regret bounds for bandit online linear optimization. In Proceedings of the 21st Annual Conference on Learning Theory, pages 335-342. Omnipress, 2008.
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[11] Jacob Abernethy, Peter L. Bartlett, Alexander Rakhlin, and Ambuj Tewari. Optimal strategies and minimax lower bounds for online convex games. In Proceedings of the 21st Annual Conference on Learning Theory, pages 414-424. Omnipress, 2008.
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[12] Sham M. Kakade, Shai Shalev-Shwartz, and Ambuj Tewari. Efficient bandit algorithms for online multiclass prediction. In Proceedings of the 25th International Conference on Machine Learning, pages 440-447. ACM Press, 2008.
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[13] Ambuj Tewari and Peter L. Bartlett. Optimistic linear programming gives logarithmic regret for irreducible MDPs. In Advances in Neural Information Processing Systems 20, pages 1505-1512. MIT Press, 2008.
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[14] Ambuj Tewari and Peter L. Bartlett. Bounded parameter Markov decision processes with average reward criterion. In Proceedings of the 20th Annual Conference on Learning Theory, volume 4539 of Lecture Notes in Computer Science, pages 263-277. Springer, 2007.
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[15] Ambuj Tewari and Peter L. Bartlett. On the consistency of multiclass classification methods. Journal of Machine Learning Research, 8:1007-1025, May 2007. (Invited paper).
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[16] Peter L. Bartlett and Ambuj Tewari. Sparseness vs estimating conditional probabilities: Some asymptotic results. Journal of Machine Learning Research, 8:775-790, Apr 2007.
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[17] Peter L. Bartlett and Ambuj Tewari. Sample complexity of policy search with known dynamics. In Advances in Neural Information Processing Systems 19, pages 97-104. MIT Press, 2007.
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[18] Ambuj Tewari and Peter L. Bartlett. On the consistency of multiclass classification methods. In Proceedings of the 18th Annual Conference on Learning Theory, volume 3559 of Lecture Notes in Computer Science, pages 147-153. Springer, 2005. Student Paper Award.
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[19] Peter L. Bartlett and Ambuj Tewari. Sparseness versus estimating conditional probabilities: Some asymptotic results. In Proceedings of the 17th Annual Conference on Learning Theory, volume 3120 of Lecture Notes in Computer Science, pages 564-578. Springer, 2004.
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[20] Ambuj Tewari, Utkarsh Srivastava, and Phalguni Gupta. A parallel DFA minimization algorithm. In Proceedings of the 9th International Conference on High Performance Computing, volume 2552 of Lecture Notes in Computer Science, pages 34-40. Springer, 2002.
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