publications

[71]	Kristjan Greenewald, Ambuj Tewari, Susan A. Murphy, and Predrag Klasnja. Action centered contextual bandits. In Advances in Neural Information Processing Systems 30, 2017. to appear. [ bib ]
[70]	Young Hun Jung, Jonathan Goetz, and Ambuj Tewari. Online multiclass boosting. In Advances in Neural Information Processing Systems 30, 2017. to appear. [ bib ]
[69]	Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep Ravikumar, and Ambuj Tewari. Cost-sensitive learning with noisy labels. Journal of Machine Learning Research, 2017. to appear. [ bib ]
[68]	Sougata Chaudhuri and Ambuj Tewari. Online learning to rank with top-k feedback. Journal of Machine Learning Research, 18(103):1-50, 2017. [ bib \| .html \| .pdf ]
[67]	Prateek Jain, Inderjit S. Dhillon, and Ambuj Tewari. Partial hard thresholding. IEEE Transactions on Information Theory, 63(5):3029-3038, 2017. [ bib \| http \| .pdf ]
[66]	Ambuj Tewari and Susan A. Murphy. From ads to interventions: Contextual bandits in mobile health. In Jim Rehg, Susan A. Murphy, and Santosh Kumar, editors, Mobile Health: Sensors, Analytic Methods, and Applications. Springer, 2017. [ bib \| http \| .pdf ]
[65]	Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, and George Michailidis. Optimality of fast matching algorithms for random networks with applications to structural controllability. IEEE Transactions on Control of Network Systems, 2016. to appear. [ bib \| http \| .pdf ]
[64]	Sougata Chaudhuri and Ambuj Tewari. Phased exploration with greedy exploitation in stochastic combinatorial partial monitoring games. In Advances in Neural Information Processing Systems 29, pages 2433-2441, 2016. [ bib \| http \| .pdf ]
[63]	Inbal Nahum-Shani, Shawna N. Smith, Bonnie J. Spring, Linda M. Collins, Katie Witkiewitz, Ambuj Tewari, and Susan A. Murphy. Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, 2016. to appear. [ bib \| http \| .pdf ]
[62]	Harish G. Ramaswamy, Clayton Scott, and Ambuj Tewari. Mixture proportion estimation via kernel embeddings of distributions. In Proceedings of the 33rd International Conference on Machine Learning, volume 48 of JMLR Workshop and Conference Proceedings, pages 2052-2060, 2016. [ bib \| .html \| .pdf ]
[61]	Nan Jiang, Satinder Singh, and Ambuj Tewari. On structural properties of MDPs that bound loss due to shallow planning. In Proceedings of the 25th International Joint Conference on Artificial Intelligence, pages 1640-1647. AAAI Press, 2016. [ bib \| http \| .pdf ]
[60]	Jacob Abernethy, Chansoo Lee, and Ambuj Tewari. Perturbation techniques in online learning and optimization. In Tamir Hazan, George Papandreou, and Daniel Tarlow, editors, Perturbations, Optimization, and Statistics, Neural Information Processing Series, chapter 8. MIT Press, 2016. [ bib \| http \| .pdf ]
[59]	Sougata Chaudhuri and Ambuj Tewari. Online learning to rank with feedback at the top. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, volume 51 of JMLR Workshop and Conference Proceedings, pages 277-285, 2016. [ bib \| .html \| .pdf ]
[58]	Peng Liao, Predrag Klasnja, Ambuj Tewari, and Susan A. Murphy. Sample size calculations for micro-randomized trials in mhealth. Statistics in Medicine, 35(12):1944-1971, 2016. [ bib \| http \| .pdf ]
[57]	Predrag Klasnja, Eric B. Hekler, Saul Shiffman, Audrey Boruvka, Daniel Almirall, Ambuj Tewari, and Susan A. Murphy. Microrandomized trials: An experimental design for developing just-in-time adaptive interventions. Health Psychology, 34(Suppl):1220-1228, Dec 2015. [ bib \| http \| .pdf ]
[56]	Prateek Jain, Nagarajan Natarajan, and Ambuj Tewari. Predtron: A family of online algorithms for general prediction problems. In Advances in Neural Information Processing Systems 28, pages 1009-1017, 2015. [ bib \| http \| .pdf ]
[55]	Prateek Jain and Ambuj Tewari. Alternating minimization for regression problems with vector-valued outputs. In Advances in Neural Information Processing Systems 28, pages 1126-1134, 2015. [ bib \| http \| .pdf ]
[54]	Jacob Abernethy, Chansoo Lee, and Ambuj Tewari. Fighting bandits with a new kind of smoothness. In Advances in Neural Information Processing Systems 28, pages 2188-2196, 2015. [ bib \| http \| .pdf ]
[53]	Harish G. Ramaswamy, Ambuj Tewari, and Shivani Agarwal. Convex calibrated surrogates for hierarchical classification. In Proceedings of the 32nd International Conference on Machine Learning, volume 37 of JMLR Workshop and Conference Proceedings, pages 1852-1860, 2015. [ bib \| .html \| .pdf ]
[52]	Ambuj Tewari and Sougata Chaudhuri. Generalization error bounds for learning to rank: Does the length of document lists matter? In Proceedings of the 32nd International Conference on Machine Learning, volume 37 of JMLR Workshop and Conference Proceedings, pages 315-323, 2015. [ bib \| .html \| .pdf ]
[51]	Sougata Chaudhuri and Ambuj Tewari. Online ranking with top-1 feedback. In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, volume 38 of JMLR Workshop and Conference Proceedings, pages 129-137, 2015. Honorable Mention, Best Student Paper Award. [ bib \| .html \| .pdf ]
[50]	Alexander Rakhlin, Karthik Sridharan, and Ambuj Tewari. Online learning via sequential complexities. Journal of Machine Learning Research, 16:155-186, Feb 2015. [ bib \| .html \| .pdf ]
[49]	Alexander Rakhlin, Karthik Sridharan, and Ambuj Tewari. Sequential complexities and uniform martingale laws of large numbers. Probability Theory and Related Fields, 161(1-2):111-153, 2015. [ bib \| http \| .pdf ]
[48]	Prateek Jain, Ambuj Tewari, and Purushottam Kar. On iterative hard thresholding methods for high-dimensional M-estimation. In Advances in Neural Information Processing Systems 27, pages 685-693, 2014. [ bib \| http \| .pdf ]
[47]	Jacob Abernethy, Chansoo Lee, Abhinav Sinha, and Ambuj Tewari. Online linear optimization via smoothing. In Proceedings of the 27th Annual Conference on Learning Theory, volume 35 of JMLR Workshop and Conference Proceedings, pages 807-823, 2014. [ bib \| .html \| .pdf ]
[46]	Kai-Yang Chiang, Cho-Jui Hsieh, Nagarajan Natarajan, Ambuj Tewari, and Inderjit S. Dhillon. Prediction and clustering in signed networks: A local to global perspective. Journal of Machine Learning Research, 15:1177-1213, March 2014. [ bib \| .html \| .pdf ]
[45]	Ambuj Tewari and Peter L. Bartlett. Learning theory. In Paulo S.R. Diniz, Johan A.K. Suykens, Rama Chellappa, and Sergios Theodoridis, editors, Academic Press Library in Signal Processing: Volume 1 Signal Processing Theory and Machine Learning, volume 1 of Academic Press Library in Signal Processing, chapter 14, pages 775-816. Elsevier, 2014. [ bib \| http \| .pdf ]
[44]	Harish G. Ramaswamy, Shivani Agarwal, and Ambuj Tewari. Convex calibrated surrogates for low-rank loss matrices with applications to subset ranking losses. In Advances in Neural Information Processing Systems 26, pages 1475-1483, 2013. [ bib \| http \| .pdf ]
[43]	Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep Ravikumar, and Ambuj Tewari. Learning with noisy labels. In Advances in Neural Information Processing Systems 26, pages 1196-1204, 2013. [ bib \| http \| .pdf ]
[42]	Eunho Yang, Ambuj Tewari, and Pradeep Ravikumar. On robust estimation of high dimensional generalized linear models. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence, pages 1834-1840. AAAI Press, 2013. [ bib \| http \| .pdf ]
[41]	U. Martin Singh-Blom, Nagarajan Natarajan, Ambuj Tewari, John O. Woods, Inderjit S. Dhillon, and Edward M. Marcotte. Prediction and validation of gene-disease associations using methods inspired by social network analyses. PLoS One, 8(5):e58977, 2013. [ bib \| http \| .pdf ]
[40]	Ankan Saha and Ambuj Tewari. On the non-asymptotic convergence of cyclic coordinate descent methods. SIAM Journal on Optimization, 23(1):576-601, 2013. [ bib \| http \| .pdf ]
[39]	Chad Scherrer, Ambuj Tewari, Mahantesh Halappanavar, and David Haglin. Feature clustering for accelerating parallel coordinate descent. In Advances in Neural Information Processing Systems 25, pages 28-36, 2012. [ bib \| .html \| .pdf ]
[38]	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, pages 93-101. AUAI Press, 2012. [ bib \| http \| .pdf ]
[37]	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, pages 1127-1128, 2012. [ bib \| http \| .pdf ]
[36]	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, pages 1503-1510. Omnipress, 2012. [ bib \| .html \| .pdf ]
[35]	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, pages 655-662. Omnipress, 2012. [ bib \| .html \| .pdf ]
[34]	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, pages 1407-1414. Omnipress, 2012. [ bib \| .html \| .pdf ]
[33]	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 ]
[32]	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 ]
[31]	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 ]
[30]	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 ]
[29]	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 ]
[28]	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 ]
[27]	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 ]
[26]	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 ]
[25]	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 ]
[24]	Alexander Rakhlin, Karthik Sridharan, and Ambuj Tewari. Online learning: Beyond regret. In Proceedings of the 24th 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 ]
[23]	Dean Foster, Alexander Rakhlin, Karthik Sridharan, and Ambuj Tewari. Complexity-based approach to calibration with checking rules. In Proceedings of the 24th Annual Conference on Learning Theory, volume 19 of JMLR Workshop and Conference Proceedings, pages 293-314, 2011. [ bib \| http \| .pdf ]
[22]	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 ]
[20]	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 ]
[19]	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 ]
[18]	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 ]
[17]	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 ]
[16]	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 ]
[15]	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, pages 35-42. AUAI Press, 2009. [ bib \| http \| .pdf ]
[14]	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 ]
[13]	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 ]
[12]	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 ]
[11]	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 ]
[10]	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 ]
[9]	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 ]
[8]	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 ]
[7]	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 ]
[6]	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 ]
[5]	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 ]
[4]	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 ]
[3]	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 ]
[2]	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 ]
[1]	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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