[1] Nagarajan Natarajan, U. Martin Blom, Ambuj Tewari, John O. Woods, Inderjit S. Dhillon, and Edward M. Marcotte. Predicting gene-disease associations using multiple species data. Technical Report TR-11-37, Department of Computer Science, University of Texas at Austin, October 2011. [ bib | http | .pdf ]
[2] Ankan Saha and Ambuj Tewari. On the non-asymptotic convergence of cyclic coordinate descent methods. 2012. accepted for publication in SIAM Journal on Optimization. [ bib | .pdf ]
[3] Chad Scherrer, Ambuj Tewari, Mahantesh Halappanavar, and David Haglin. Feature clustering for accelerating parallel coordinate descent. In Advances in Neural Information Processing Systems 25, 2012. to appear. [ bib | .pdf ]
[4] Raman Arora, Ofer Dekel, and Ambuj Tewari. Deterministic MDPs with adversarial rewards and bandit feedback. In Proceedings of the 28th Annual Conference on Uncertainty in Artificial Intelligence, 2012. [ bib | .pdf ]
[5] Shilpa Shukla, Matthew Lease, and Ambuj Tewari. Parallelizing ListNet training using Spark. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2012. [ bib | .pdf ]
[6] Raman Arora, Ofer Dekel, and Ambuj Tewari. Online bandit learning against an adaptive adversary: from regret to policy regret. In Proceedings of the 29th International Conference on Machine Learning, 2012. [ bib | .pdf ]
[7] Shivaram Kalyanakrishnan, Ambuj Tewari, Peter Auer, and Peter Stone. PAC subset selection in stochastic multi-armed bandits. In Proceedings of the 29th International Conference on Machine Learning, 2012. [ bib | .pdf ]
[8] Chad Scherrer, Mahantesh Halappanavar, Ambuj Tewari, and David Haglin. Scaling up coordinate descent algorithms for large l1 regularization problems. In Proceedings of the 29th International Conference on Machine Learning, 2012. [ bib | .pdf ]
[9] Sham M. Kakade, Shai Shalev-Shwartz, and Ambuj Tewari. Regularization techniques for learning with matrices. Journal of Machine Learning Research, 13:1865-1890, June 2012. [ bib | .html | .pdf ]
[10] Eunho Yang, Ambuj Tewari, and Pradeep Ravikumar. Perturbation based large margin approach for ranking. In Proceedings of the 15th International Conference on Artificial Intelligence and Statistics, volume 22 of JMLR Workshop and Conference Proceedings, pages 1358-1366, 2012. [ bib | http | .pdf ]
[11] Nathan Srebro, Karthik Sridharan, and Ambuj Tewari. On the universality of online mirror descent. In Advances in Neural Information Processing Systems 24, pages 2645-2653, 2011. longer version available as arXiv:1107.4080. [ bib | .html | .pdf ]
[12] Prateek Jain, Ambuj Tewari, and Inderjit S. Dhillon. Orthogonal matching pursuit with replacement. In Advances in Neural Information Processing Systems 24, pages 1215-1223, 2011. longer version available as arXiv:1106.2774. [ bib | .html | .pdf ]
[13] Ambuj Tewari, Pradeep Ravikumar, and Inderjit S. Dhillon. Greedy algorithms for structurally constrained high dimensional problems. In Advances in Neural Information Processing Systems 24, pages 882-890, 2011. [ bib | .html | .pdf ]
[14] Inderjit S. Dhillon, Pradeep Ravikumar, and Ambuj Tewari. Nearest neighbor based greedy coordinate descent. In Advances in Neural Information Processing Systems 24, pages 2160-2168, 2011. [ bib | .html | .pdf ]
[15] Alexander Rakhlin, Karthik Sridharan, and Ambuj Tewari. Online learning: Stochastic, constrained, and smoothed adversaries. In Advances in Neural Information Processing Systems 24, pages 1764-1772, 2011. longer (but older) version available as arXiv:1104.5070. [ bib | .html | .pdf ]
[16] Kai-Yang Chiang, Nagarajan Natarajan, Ambuj Tewari, and Inderjit S. Dhillon. Exploiting longer cycles for link prediction in signed networks. In Proceedings of the 20th ACM Conference on Information and Knowledge Management, pages 1157-1162, 2011. [ bib | http | .pdf ]
[17] Shai Shalev-Shwartz and Ambuj Tewari. Stochastic methods for l1 regularized loss minimization. Journal of Machine Learning Research, 12:1865-1892, June 2011. [ bib | .html | .pdf ]
[18] Alexander Rakhlin, Karthik Sridharan, and Ambuj Tewari. Online learning: Beyond regret. In Proceedings of the 24rd Annual Conference on Learning Theory, volume 19 of JMLR Workshop and Conference Proceedings, pages 559-594, 2011. Best Paper Award, longer version available as arXiv:1011.3168. [ bib | http | .pdf ]
[19] Dean Foster, Alexander Rakhlin, Karthik Sridharan, and Ambuj Tewari. Complexity-based approach to calibration with checking rules. In Proceedings of the 24rd Annual Conference on Learning Theory, volume 19 of JMLR Workshop and Conference Proceedings, pages 293-314, 2011. [ bib | http | .pdf ]
[20] Pradeep Ravikumar, Ambuj Tewari, and Eunho Yang. On NDCG consistency of listwise ranking methods. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, volume 15 of JMLR Workshop and Conference Proceedings, pages 618-626, 2011. [ bib | http | .pdf ]
[21] Ankan Saha and Ambuj Tewari. Improved regret guarantees for online smooth convex optimization with bandit feedback. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, volume 15 of JMLR Workshop and Conference Proceedings, pages 636-642, 2011. [ bib | http | .pdf ]
[22] Alexander Rakhlin, Karthik Sridharan, and Ambuj Tewari. Online learning: Random averages, combinatorial parameters, and learnability. In Advances in Neural Information Processing Systems 23, pages 1984-1992, 2010. [ bib | .html | .pdf ]
[23] Nathan Srebro, Karthik Sridharan, and Ambuj Tewari. Smoothness, low noise, and fast rates. In Advances in Neural Information Processing Systems 23, pages 2199-2207, 2010. [ bib | .html | .pdf ]
[24] John Duchi, Shai Shalev-Shwartz, Yoram Singer, and Ambuj Tewari. Composite objective mirror descent. In Proceedings of the 23rd Annual Conference on Learning Theory, pages 14-26. Omnipress, 2010. [ bib | .html | .pdf ]
[25] Karthik Sridharan and Ambuj Tewari. Convex games in Banach spaces. In Proceedings of the 23rd Annual Conference on Learning Theory, pages 1-13. Omnipress, 2010. [ bib | .html | .pdf ]
[26] 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. [ bib | http | .pdf ]
[27] 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. [ bib | .html | .pdf ]
[28] 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. [ bib | http | .pdf ]
[29] 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. [ bib | .html | .pdf ]
[30] 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. [ bib | .html | .pdf ]
[31] 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. [ bib | http | .pdf ]
[32] 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. [ bib | http | .pdf ]
[33] 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. [ bib | http | .pdf ]
[34] 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. [ bib | .html | .pdf ]
[35] 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. [ bib | http | .pdf ]
[36] 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). [ bib | .html | .pdf ]
[37] Peter L. Bartlett and Ambuj Tewari. Sparseness vs estimating conditional probabilities: Some asymptotic results. Journal of Machine Learning Research, 8:775-790, Apr 2007. [ bib | .html | .pdf ]
[38] 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. [ bib | .html | .pdf ]
[39] 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. [ bib | http | .pdf ]
[40] 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. [ bib | http | .pdf ]
[41] 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. [ bib | http | .pdf ]

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