@article{subedi26operator,
author = {Unique Subedi and Ambuj Tewari},
title = {Operator Learning: A Statistical Perspective},
journal = {Annual Reviews of Statistics and Its Application},
year = {2026},
pdf = {research/subedi26operator.pdf}
}
@article{shim25prospective,
title = {Prospective active transfer learning on the formal coupling of amines and carboxylic acids to form secondary alkyl bonds},
author = {Shim, Eunjae and Tewari, Ambuj and Zimmerman, Paul and Cernak, Tim},
journal = {Digital Discovery},
year = {2025},
url = {https://doi.org/10.1039/D5DD00309A},
pdf = {research/shim25prospective.pdf}
}
@inproceedings{brooks25generator-mediated,
author = {Marc Brooks and Gabriel Durham and Kihyuk Hong and Ambuj Tewari},
title = {Generator-Mediated Bandits: {T}hompson Sampling for {GenAI}-Powered Adaptive Interventions},
booktitle = {Advances in Neural Information Processing Systems 38},
year = {2025},
pdf = {research/brooks25generator-mediated.pdf}
}
@inproceedings{patel25conformal,
author = {Yash Patel and Eduardo Ochoa Rivera and Ambuj Tewari},
title = {Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation},
booktitle = {Advances in Neural Information Processing Systems 38},
year = {2025},
pdf = {research/patel25conformal.pdf}
}
@article{qiao25asymptotically,
title = {An Asymptotically Optimal Algorithm for the Convex Hull Membership Problem},
author = {Gang Qiao and Ambuj Tewari},
journal = {Transactions on Machine Learning Research},
year = {2025},
pdf = {research/qiao25asymptotically.pdf},
url = {https://openreview.net/pdf?id=r8eAwBMtlN}
}
@article{parham25identifying,
author = {Parham, Rebecca and Ayala, Abbygail and Meagher, Lauren and Clough, Madeline and Ochoa Rivera, Eduardo and Shi, Jia and Tewari, Ambuj and McNeil, Anne and Ault, Andrew},
title = {Identifying Microplastics in Laboratory and Atmospheric Aerosol Mixtures via Optical Photothermal Infrared and {R}aman Microspectroscopy},
journal = {Analytical Chemistry},
year = {2025},
volume = {97},
number = {33},
pages = {18136--18143},
url = {https://doi.org/10.1021/acs.analchem.5c02968},
pdf = {research/parham25identifying.pdf}
}
@article{subedi25controlling,
title = {Controlling Statistical, Discretization, and Truncation Errors in Learning {F}ourier Linear Operators},
author = {Unique Subedi and Ambuj Tewari},
journal = {Transactions on Machine Learning Research},
year = {2025},
pdf = {research/subedi25controlling.pdf},
url = {https://openreview.net/pdf?id=A2sHNGcjLO}
}
@article{mohan25quantum,
title = {Quantum Learning Theory Beyond Batch Binary Classification},
author = {Preetham Mohan and Ambuj Tewari},
journal = {Quantum},
volume = {9},
pages = {1813},
year = {2025},
url = {https://doi.org/10.22331/q-2025-07-29-1813},
pdf = {research/mohan25quantum.pdf}
}
@inproceedings{li25generation,
author = {Jiaxun Li and Vinod Raman and Ambuj Tewari},
title = {Generation through the lens of learning theory},
booktitle = {Proceedings of the 38th Annual Conference on Learning Theory},
pages = {4740--4776},
volume = {291},
series = {Proceedings of Machine Learning Research},
year = {2025},
pdf = {research/li25generation.pdf},
url = {https://proceedings.mlr.press/v291/raman25a.html}
}
@inproceedings{kausik25leveraging,
author = {Chinmaya Kausik and Kevin Tan and Ambuj Tewari},
title = {Leveraging Offline Data in Linear Latent Contextual Bandits},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
year = {2025},
pdf = {research/kausik25leveraging.pdf},
note = {accepted}
}
@inproceedings{subedi25benefits,
author = {Unique Subedi and Ambuj Tewari},
title = {On the Benefits of Active Data Collection in Operator Learning},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
year = {2025},
pdf = {research/subedi25benefits.pdf},
note = {accepted}
}
@inproceedings{hong25computationally,
author = {Kihyuk Hong and Ambuj Tewari},
title = {A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear {MDP}s},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
year = {2025},
pdf = {research/hong25computationally.pdf},
note = {accepted}
}
@article{roy25understanding,
title = {Understanding Best Subset Selection: A Tale of Two C(omplex)ities},
author = {Saptarshi Roy and Ambuj Tewari and Ziwei Zhu},
journal = {Electronic Journal of Statistics},
volume = {19},
number = {1},
pages = {2320--2342},
year = {2025},
url = {https://doi.org/10.1214/25-EJS2388},
pdf = {research/roy25understanding.pdf}
}
@inproceedings{raman25complexity,
author = {Vinod Raman and Unique Subedi and Ambuj Tewari},
title = {The Complexity of Sequential Prediction in Dynamical Systems},
booktitle = {Proceedings of the 7th Annual Learning for Dynamics and Control Conference},
year = {2025},
series = {Proceedings of Machine Learning Research},
volume = {283},
pages = {124--138},
pdf = {research/raman25complexity.pdf},
url = {https://proceedings.mlr.press/v283/raman25a.html}
}
@article{roy25high-dimensional,
title = {High-dimensional Variable Selection with Heterogeneous Signals: A Precise Asymptotic Perspective},
author = {Saptarshi Roy and Ambuj Tewari and Ziwei Zhu},
journal = {Bernoulli},
year = {2025},
volume = {31},
number = {2},
pages = {1206-1229},
url = {http://doi.org/10.3150/24-BEJ1767},
pdf = {research/roy25high-dimensional.pdf}
}
@article{shim25recommending,
title = {Recommending reaction conditions with label ranking},
author = {Eunjae Shim and Ambuj Tewari and Tim Cernak and Paul M. Zimmerman},
journal = {Chemical Science},
year = {2025},
volume = {16},
number = {9},
pages = {4109--4118},
pdf = {research/shim25recommending.pdf},
url = {http://dx.doi.org/10.1039/D4SC06728B}
}
@inproceedings{kausik25theoretical,
author = {Chinmaya Kausik and Mirco Mutti and Aldo Pacchiano and Ambuj Tewari},
title = {A Theoretical Framework for Partially-Observed Reward States in {RLHF}},
booktitle = {Proceedings of the Thirteenth International Conference on Learning Representations},
year = {2025},
pdf = {research/kausik25theoretical.pdf},
url = {https://openreview.net/forum?id=OjAU0LLDbe}
}
@inproceedings{hong25reinforcement,
author = {Kihyuk Hong and Woojin Chae and Yufan Zhang and Dabeen Lee and Ambuj Tewari},
title = {Reinforcement Learning for Infinite-Horizon Average-Reward Linear {MDP}s via Approximation by Discounted-Reward {MDP}s},
booktitle = {Proceedings of the 28th International Conference on Artificial Intelligence and Statistics},
year = {2025},
series = {Proceedings of Machine Learning Research},
volume = {258},
pages = {2989--2997},
pdf = {research/hong25reinforcement.pdf},
url = {https://proceedings.mlr.press/v258/hong25a.html}
}
@inproceedings{chae25learning,
author = {Woojin Chae and Kihyuk Hong and Yufan Zhang and Ambuj Tewari and Dabeen Lee},
title = {Learning Infinite-Horizon Average-Reward Linear Mixture {MDP}s of Bounded Span},
booktitle = {Proceedings of the 28th International Conference on Artificial Intelligence and Statistics},
year = {2025},
series = {Proceedings of Machine Learning Research},
volume = {258},
pages = {2737--2745},
pdf = {research/chae25learning.pdf},
url = {https://proceedings.mlr.press/v258/chae25a.html}
}
@inproceedings{raman25unified,
author = {Vinod Raman and Unique Subedi and Ambuj Tewari},
title = {A Unified Theory of Supervised Online Learnability},
booktitle = {Proceedings of the 36th International Conference on Algorithmic Learning Theory},
pages = {985--1007},
volume = {272},
series = {Proceedings of Machine Learning Research},
year = {2025},
url = {https://proceedings.mlr.press/v272/raman25a.html},
pdf = {research/raman25unified.pdf},
note = {{\bf Outstanding Paper Award}}
}
@article{khanna25examining,
title = {Examining the Impact of Local Constraint Violations on Energy Computations in {DFT}},
author = {Vaibhav Khanna and Bikash Kanungo and Vikram Gavini and Ambuj Tewari and Paul M. Zimmerman},
journal = {Journal of Computational Chemistry},
year = {2025},
volume = {46},
number = {1},
pages = {e70005},
url = {https://doi.org/10.1002/jcc.70005},
pdf = {research/khanna25examining.pdf}
}
@article{golob24microbiome,
title = {Microbiome preterm birth {DREAM} challenge: Crowdsourcing machine learning approaches to advance preterm birth research},
author = {Golob, Jonathan L. and Oskotsky, Tomiko T. and Tang, Alice S. and Roldan, Alennie and Chung, Verena and Ha, Connie W.Y. and Wong, Ronald J. and Flynn, Kaitlin J. and Chai, Rong and Dubin, Claire and Parraga-Leo, Antonio and Wibrand, Camilla and Minot, Samuel S. and Oskotsky, Boris and Andreoletti, Gaia and Kosti, Idit and Bletz, Julie and Nelson, Amber and Gao, Jifan and Wei, Zhoujingpeng and Chen, Guanhua and Tang, Zheng-Zheng and Novielli, Pierfrancesco and Romano, Donato and Pantaleo, Ester and Amoroso, Nicola and Monaco, Alfonso and Vacca, Mirco and De Angelis, Maria and Bellotti, Roberto and Tangaro, Sabina and Wang, Zehua and Yao, Jiaming and Goel, Akhil and Mao, Jiangyue and Wang, Huiqian and Zhang, Yuci and Tewari, Ambuj and Kuntzleman, Abigail and Bigcraft, Isaac and Techtmann, Stephen and Bae, Daehun and Kim, Eunyoung and Jeon, Jongbum and Joe, Soobok and Theis, Kevin R. and Ng, Sherrianne and Lee, Yun S. and Diaz-Gimeno, Patricia and Bennett, Phillip R. and MacIntyre, David A. and Stolovitzky, Gustavo and Lynch, Susan V. and Albrecht, Jake and Gomez-Lopez, Nardhy and Romero, Roberto and Stevenson, David K. and Aghaeepour, Nima and Tarca, Adi L. and Costello, James C. and Sirota, Marina},
year = {2024},
journal = {Cell Reports Medicine},
volume = {5},
number = {1},
url = {https://doi.org/10.1016/j.xcrm.2023.101350},
pdf = {research/golob24microbiome.pdf}
}
@article{clough24enhancing,
title = {Enhancing confidence in microplastic spectral identification via conformal prediction},
author = {Clough, Madeline E. and Ochoa Rivera, Eduardo and Parham, Rebecca L. and Ault, Andrew P. and Zimmerman, Paul M. and McNeil, Anne J. and Tewari, Ambuj},
journal = {Environmental Science \& Technology},
year = {2024},
volume = {58},
number = {49},
pages = {21740--21749},
url = {https://doi.org/10.1021/acs.est.4c05167},
pdf = {research/clough24enhancing.pdf}
}
@article{raman24characterization,
title = {A Characterization of Multioutput Learnability},
author = {Vinod Raman and Unique Subedi and Ambuj Tewari},
journal = {Journal of Machine Learning Research},
year = {2024},
volume = {25},
number = {342},
pages = {1--54},
url = {http://jmlr.org/papers/v25/23-078.html},
pdf = {research/raman24characterization.pdf}
}
@incollection{lu24bandit,
author = {Yangyi Lu and Ziping Xu and Ambuj Tewari},
title = {Bandit Algorithms for Precision Medicine},
booktitle = { Handbook of Statistical Methods for Precision Medicine},
publisher = {Chapman \& Hall/CRC},
year = {2024},
editor = {Eric Laber and Bibhas Chakraborty and Erica E. M. Moodie and Tianxi Cai and Mark van der Laan},
url = {https://www.routledge.com/Handbook-of-Statistical-Methods-for-Precision-Medicine/Laber-Chakraborty-Moodie-Cai-Laan/p/book/9781032106151},
pdf = {research/lu24bandit.pdf}
}
@inproceedings{raman24online-classification,
author = {Vinod Raman and Ambuj Tewari},
title = {Online Classification with Predictions},
booktitle = {Advances in Neural Information Processing Systems 37},
year = {2024},
url = {https://openreview.net/forum?id=MB0DD5qAz8},
pdf = {research/raman24online-classification.pdf}
}
@inproceedings{raman24smoothed,
author = {Vinod Raman and Unique Subedi and Ambuj Tewari},
title = {Smoothed Online Classification can be Harder than Batch Classification},
booktitle = {Advances in Neural Information Processing Systems 37},
year = {2024},
url = {https://openreview.net/forum?id=NO9MSeZs6g},
pdf = {research/raman24smoothed.pdf}
}
@inproceedings{roy24computational,
author = {Saptarshi Roy and Zehua Wang and Ambuj Tewari},
title = {On the Computational Complexity of Private High-dimensional Model Selection},
booktitle = {Advances in Neural Information Processing Systems 37},
year = {2024},
url = {https://openreview.net/forum?id=PzG7xVlYqm},
pdf = {research/roy24computational.pdf}
}
@inproceedings{raman24set,
author = {Vinod Raman and Unique Subedi and Ambuj Tewari},
title = {Online Learning with Set-valued Feedback},
booktitle = {Proceedings of the 37th Annual Conference on Learning Theory},
year = {2024},
pdf = {research/raman24set.pdf},
url = {https://proceedings.mlr.press/v247/raman24b.html}
}
@inproceedings{raman24apple,
author = {Vinod Raman and Unique Subedi and Ananth Raman and Ambuj Tewari},
title = {Apple Tasting: Combinatorial Dimensions and Minimax Rates},
booktitle = {Proceedings of the 37th Annual Conference on Learning Theory},
year = {2024},
pdf = {research/raman24apple.pdf},
url = {https://proceedings.mlr.press/v247/raman24a.html}
}
@inproceedings{patel24variational,
author = {Yash Patel and Declan McNamara and Jackson Loper and Jeffrey Regier and Ambuj Tewari},
title = {Variational Inference with Coverage Guarantees in Simulation-Based Inference},
booktitle = {Proceedings of the 41st International Conference on Machine Learning},
year = {2024},
pdf = {research/patel24variational.pdf},
url = {https://proceedings.mlr.press/v235/patel24a.html}
}
@inproceedings{hong24primal-dual,
author = {Kihyuk Hong and Ambuj Tewari},
title = {A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear {MDPs}},
booktitle = {Proceedings of the 41st International Conference on Machine Learning},
year = {2024},
pdf = {research/hong24primal-dual.pdf},
url = {https://proceedings.mlr.press/v235/hong24e.html}
}
@inproceedings{xu24sample,
author = {Ziping Xu and Zifan Xu and Runxuan Jiang and Peter Stone and Ambuj Tewari},
title = {Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks},
booktitle = {Proceedings of the 12th International Conference on Learning Representations},
year = {2024},
url = {https://openreview.net/forum?id=YZrg56G0JV},
pdf = {research/xu24sample.pdf}
}
@inproceedings{hong24primal,
author = {Kihyuk Hong and Yuhang Li and Ambuj Tewari},
title = {A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning},
booktitle = {Proceedings of the 27th International Conference on Artificial Intelligence and Statistics},
year = {2024},
url = {https://proceedings.mlr.press/v238/hong24a.html},
pdf = {research/hong24primal.pdf}
}
@inproceedings{trauger24sequence,
author = {Jacob Trauger and Ambuj Tewari},
title = {Sequence Length Independent Norm-Based Generalization Bounds for Transformers},
booktitle = {Proceedings of the 27th International Conference on Artificial Intelligence and Statistics},
year = {2024},
url = {https://proceedings.mlr.press/v238/trauger24a.html},
pdf = {research/trauger24sequence.pdf}
}
@inproceedings{kausik24offline,
author = {Chinmaya Kausik and Yangyi Lu and Kevin Tan and Maggie Makar and Yixin Wang and Ambuj Tewari},
title = {Offline Policy Evaluation and Optimization Under Confounding},
booktitle = {Proceedings of the 27th International Conference on Artificial Intelligence and Statistics},
year = {2024},
url = {https://proceedings.mlr.press/v238/kausik24a.html},
pdf = {research/kausik24offline.pdf}
}
@inproceedings{patel24conformal,
author = {Yash Patel and Sahana Rayan and Ambuj Tewari},
title = {Conformal Contextual Robust Optimization},
booktitle = {Proceedings of the 27th International Conference on Artificial Intelligence and Statistics},
year = {2024},
url = {https://proceedings.mlr.press/v238/patel24a.html},
pdf = {research/patel24conformal.pdf}
}
@article{modi24joint,
title = {Joint Learning of Linear Time-Invariant Dynamical Systems},
author = {Aditya Modi and Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
journal = {Automatica},
volume = {164},
pages = {111635},
year = {2024},
url = {https://doi.org/10.1016/j.automatica.2024.111635},
pdf = {research/modi24joint.pdf}
}
@inproceedings{raman24multiclass,
author = {Ananth Raman and Vinod Raman and Unique Subedi and Idan Mehalel and Ambuj Tewari},
title = {Multiclass Online Learnability under Bandit Feedback},
booktitle = {Proceedings of 35th International Conference on Algorithmic Learning Theory},
year = {2024},
url = {https://proceedings.mlr.press/v237/raman24a.html},
pdf = {research/raman24multiclass.pdf}
}
@inproceedings{raman24online,
author = {Vinod Raman and Unique Subedi and Ambuj Tewari},
title = {Online Infinite-Dimensional Regression: Learning Linear Operators},
booktitle = {Proceedings of 35th International Conference on Algorithmic Learning Theory},
year = {2024},
url = {https://proceedings.mlr.press/v237/subedi24a.html},
pdf = {research/raman24online.pdf}
}
@inproceedings{raman23learnability,
author = {Vinod Raman and Unique Subedi and Ambuj Tewari},
title = {On the Learnability of Multilabel Ranking},
booktitle = {Advances in Neural Information Processing Systems 36},
year = {2023},
url = {https://openreview.net/forum?id=cwBeRBe9hq},
pdf = {research/raman23learnability.pdf}
}
@inproceedings{raman23proper,
author = {Vinod Raman and Unique Subedi and Ambuj Tewari},
title = {On Proper Learnability between Average- and Worst-case Robustness},
booktitle = {Advances in Neural Information Processing Systems 36},
year = {2023},
url = {https://openreview.net/forum?id=4yXnnCK3r9},
pdf = {research/raman23proper.pdf}
}
@article{shim23machine,
title = {Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit},
author = {Eunjae Shim and Ambuj Tewari and Tim Cernak and Paul M. Zimmerman},
journal = {Journal of Chemical Information and Modeling},
year = {2023},
volume = {63},
number = {12},
pages = {3659--3668},
pdf = {research/shim23machine.pdf},
url = {https://doi.org/10.1021/acs.jcim.3c00577}
}
@inproceedings{hanneke23multiclass,
author = {Steve Hanneke and Shay Moran and Vinod Raman and Unique Subedi and Ambuj Tewari},
title = {Multiclass Online Learning and Uniform Convergence},
booktitle = {Proceedings of the 36th Annual Conference on Learning Theory},
year = {2023},
pdf = {research/hanneke23multiclass.pdf},
url = {https://proceedings.mlr.press/v195/hanneke23b.html}
}
@inproceedings{chandrasekaran23learning,
author = {Gautam Chandrasekaran and Ambuj Tewari},
title = {Learning in Online {MDPs}: Is there a Price for Handling the Communicating Case?},
booktitle = {Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence},
year = {2023},
series = {Proceedings of Machine Learning Research},
volume = {216},
pages = {293--302},
pdf = {research/chandrasekaran23learning.pdf},
url = {https://proceedings.mlr.press/v216/chandrasekaran23a.html}
}
@inproceedings{roy23thompson,
author = {Saptarshi Roy and Sunrit Chakraborty and Ambuj Tewari},
title = {{T}hompson Sampling for High-Dimensional Sparse Linear Contextual Bandits},
booktitle = {Proceedings of the 40th International Conference on Machine Learning},
year = {2023},
pdf = {research/roy23thompson.pdf},
url = {https://proceedings.mlr.press/v202/chakraborty23b.html}
}
@inproceedings{kausik23learning,
author = {Chinmaya Kausik and Kevin Tan and Ambuj Tewari},
title = {Learning Mixtures of {M}arkov Chains and {MDP}s},
booktitle = {Proceedings of the 40th International Conference on Machine Learning},
year = {2023},
pdf = {research/kausik23learning.pdf},
url = {https://proceedings.mlr.press/v202/kausik23a.html}
}
@article{tewari23mhealth,
title = {mHealth Systems Need a Privacy-by-Design Approach: Commentary on "{F}ederated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review"},
author = {Ambuj Tewari},
journal = {Journal of Medical Internet Research},
year = {2023},
volume = {25},
pages = {e46700},
url = {https://doi.org/10.2196/46700},
pdf = {research/tewari23mhealth.pdf},
note = {Invited Commentary}
}
@article{shen23exploring,
title = {Exploring the Relationship Between Privacy and Utility in Mobile Health: A Simulation of Federated Learning, Differential Privacy, and External Attacks},
author = {Alexander Shen and Luke Francisco and Srijan Sen and Ambuj Tewari},
journal = {Journal of Medical Internet Research},
year = {2023},
volume = {25},
pages = {e43664},
url = {https://doi.org/10.2196/43664},
pdf = {research/shen23exploring.pdf}
}
@article{bairakdar23meta-analysis,
title = {A Meta-Analysis of {RNA}-Seq Studies to Identify Novel Genes that Regulate Aging},
author = {Mohamad D. Bairakdar and Ambuj Tewari and Matthias C. Truttman},
journal = {Experimental Gerontology},
volume = {173},
pages = {112107},
year = {2023},
url = {https://doi.org/10.1016/j.exger.2023.112107},
pdf = {research/bairakdar23meta-analysis.pdf}
}
@inproceedings{hong23optimization,
author = {Kihyuk Hong and Yuhang Li and Ambuj Tewari},
title = {An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge},
booktitle = {Proceedings of the 26th International Conference on Artificial Intelligence and Statistics},
year = {2023},
series = {Proceedings of Machine Learning Research},
volume = {206},
pages = {3048--3085},
pdf = {research/hong23optimization.pdf},
url = {https://proceedings.mlr.press/v206/hong23b.html}
}
@article{wu22effectiveness,
title = {Effectiveness of gamified team competition as mHealth intervention for medical interns: a cluster micro-randomized trial},
author = {Jitao Wang and Yu Fang and Elena Frank and Maureen Walton and Margit Burmeister and Ambuj Tewari and Walter Dempsey and Timothy NeCamp and Srijan Sen and Zhenke Wu},
journal = {npj Digital Medicine},
volume = {6},
number = {4},
year = {2022},
url = {https://doi.org/10.1038/s41746-022-00746-y},
pdf = {research/wang22effectiveness.pdf}
}
@article{elbalghiti22generalization,
author = {Othman El Balghiti and Adam N. Elmachtoub and Paul Grigas and Ambuj Tewari},
title = {Generalization Bounds in the Predict-then-Optimize Framework},
journal = {Mathematics of Operations Research},
year = {2022},
volume = {48},
number = {4},
pages = {2043--2065},
url = {https://doi.org/10.1287/moor.2022.1330},
pdf = {research/elbalghiti22generalization.pdf}
}
@inproceedings{xu22adaptive,
author = {Ziping Xu and Eunjae Shim and Ambuj Tewari and Paul Zimmerman},
title = {Adaptive Sampling for Discovery},
booktitle = {Advances in Neural Information Processing Systems 35},
year = {2022},
pages = {1114--1126},
url = {https://papers.nips.cc/paper_files/paper/2022/hash/07bc8125400bf4b140c332010756bd9b-Abstract-Conference.html},
pdf = {research/xu22adaptive.pdf}
}
@inproceedings{raman22online,
author = {Vinod Raman and Ambuj Tewari},
title = {Online Agnostic Multiclass Boosting},
booktitle = {Advances in Neural Information Processing Systems 35},
year = {2022},
pages = {25908--25920},
url = {https://papers.nips.cc/paper_files/paper/2022/hash/a6acb2d482de9c708f5b03d5a70465d2-Abstract-Conference.html},
pdf = {research/raman22online.pdf}
}
@article{jiang22conformer-rl,
author = {Runxuan Jiang and Tarun Gogineni and Joshua Kammeraad and Yifei He and Ambuj Tewari and Paul M. Zimmerman},
title = {Conformer-{RL}: A deep reinforcement learning library for conformer generation},
journal = {Journal of Computational Chemistry},
year = {2022},
volume = {43},
issue = {27},
pages = {1880--1886},
url = {https://doi.org/10.1002/jcc.26984},
pdf = {research/jiang22conformer-rl.pdf}
}
@article{dawid22learnability,
author = {Philip Dawid and Ambuj Tewari},
title = {On Learnability under General Stochastic Processes},
journal = {Harvard Data Science Review},
volume = {4},
number = {4},
year = {2022},
url = {https://doi.org/10.1162/99608f92.dec7d780},
pdf = {research/dawid22learnability.pdf}
}
@inproceedings{digiovanni22balancing,
author = {Anthony DiGiovanni and Ambuj Tewari},
title = {Balancing Adaptability and Non-exploitability in Repeated Games},
booktitle = {Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence},
series = {Proceedings of Machine Learning Research},
volume = {180},
pages = {559--568},
year = {2022},
url = {https://proceedings.mlr.press/v180/digiovanni22a.html},
pdf = {research/digiovanni22balancing.pdf}
}
@inproceedings{xu22statistical,
author = {Ziping Xu and Ambuj Tewari},
title = {On the Statistical Benefits of Curriculum Learning},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
series = {Proceedings of Machine Learning Research},
volume = {162},
pages = {24663--24682},
year = {2022},
url = {https://proceedings.mlr.press/v162/xu22i.html},
pdf = {research/xu22statistical.pdf}
}
@article{kim22predicting,
author = {Eunjae Shim and Joshua A. Kammeraad and Ziping Xu and Ambuj Tewari and Tim Cernak and Paul M. Zimmerman},
title = {Predicting Reaction Conditions from Limited Data through Active Transfer Learning},
journal = {Chemical Science},
year = {2022},
volume = {13},
issue = {22},
pages = {6655--6668},
url = {http://dx.doi.org/10.1039/D1SC06932B},
pdf = {research/kim22predicting.pdf}
}
@inproceedings{deng22weighted,
author = {Yuntian Deng and Xingyu Zhou and Baekjin Kim and Ambuj Tewari and Abhishek Gupta and Ness Shroff},
title = {Weighted Gaussian Process Bandits for Non-stationary Environments},
booktitle = {Proceedings of the 25th International Conference on Artificial Intelligence and Statistics},
year = {2022},
series = {Proceedings of Machine Learning Research},
volume = {151},
pages = {6909--6932},
pdf = {research/deng22weighted.pdf},
url = {https://proceedings.mlr.press/v151/deng22b.html}
}
@inproceedings{lu22causal,
author = {Yangyi Lu and Amirhossein Meisami and Ambuj Tewari},
title = {Efficient Reinforcement Learning with Prior Causal Knowledge},
booktitle = {Proceedings of the First Conference on Causal Learning and Reasoning},
year = {2022},
pdf = {research/lu22causal.pdf},
url = {https://www.cclear.cc/2022/AcceptedPapers}
}
@inproceedings{lu21causal,
author = {Yangyi Lu and Amirhossein Meisami and Ambuj Tewari},
title = {Causal Bandits with Unknown Graph Structure},
booktitle = {Advances in Neural Information Processing Systems 34},
year = {2021},
pages = {24817--24828},
pdf = {research/lu21causal.pdf},
url = {https://proceedings.neurips.cc/paper/2021/hash/d010396ca8abf6ead8cacc2c2f2f26c7-Abstract.html}
}
@inproceedings{xu21representation,
author = {Ziping Xu and Ambuj Tewari},
title = {Representation Learning Beyond Linear Prediction Functions},
booktitle = {Advances in Neural Information Processing Systems 34},
year = {2021},
pages = {4792--4804},
pdf = {research/xu21representation.pdf},
url = {https://proceedings.neurips.cc/paper/2021/hash/258be18e31c8188555c2ff05b4d542c3-Abstract.html}
}
@inproceedings{digiovanni21thompson,
author = {Anthony DiGiovanni and Ambuj Tewari},
title = {{T}hompson Sampling for {M}arkov Games with Piecewise Stationary Opponent Policies},
booktitle = {Proceedings of the 37th Annual Conference on Uncertainty in Artificial Intelligence},
year = {2021},
pdf = {research/digiovanni21thompson.pdf},
series = {Proceeding of Maching Learning Research},
volume = {161},
pages = {738--748},
url = {https://proceedings.mlr.press/v161/digiovanni21a.html}
}
@article{liu21learning,
author = {Jessica Chia Liu and Jack Goetz and Srijan Sen and Ambuj Tewari},
title = {Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data},
journal = {JMIR mHealth and uHealth},
year = {2021},
month = {Mar},
volume = {9},
number = {3},
pages = {e23728},
url = {https://doi.org/10.2196/23728},
pdf = {research/liu21learning.pdf}
}
@inproceedings{lu21low-rank,
author = {Yangyi Lu and Amirhossein Meisami and Ambuj Tewari },
title = {Low-Rank Generalized Linear Bandit Problems},
booktitle = {Proceedings of the 24th International Conference on Artificial Intelligence and Statistics},
pages = {460--468},
year = {2021},
volume = {130},
series = {Proceedings of Machine Learning Research},
pdf = {research/lu21low-rank.pdf},
url = {http://proceedings.mlr.press/v130/lu21a.html}
}
@inproceedings{xu21decision,
author = {Ziping Xu and Amirhossein Meisami and Ambuj Tewari },
title = {Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns},
booktitle = {Proceedings of the 24th International Conference on Artificial Intelligence and Statistics},
year = {2021},
pages = {127--135},
volume = {130},
series = {Proceedings of Machine Learning Research},
pdf = {research/xu21decision.pdf},
url = {http://proceedings.mlr.press/v130/xu21a.html}
}
@inproceedings{xu20reinforcement,
author = {Ziping Xu and Ambuj Tewari},
title = {Reinforcement Learning in Factored {MDPs}: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting},
booktitle = {Advances in Neural Information Processing Systems 33},
year = {2020},
pdf = {research/xu20reinforcement.pdf},
url = {https://proceedings.neurips.cc/paper/2020}
}
@inproceedings{jung20equivalence,
author = {Young Hun Jung and Baekjin Kim and Ambuj Tewari},
title = {On the Equivalence between Online and Private Learnability beyond Binary Classification},
booktitle = {Advances in Neural Information Processing Systems 33},
year = {2020},
pdf = {research/jung20equivalence.pdf},
url = {https://proceedings.neurips.cc/paper/2020}
}
@inproceedings{gogineni20torsionnet,
author = {Tarun Gogineni and Ziping Xu and Exequiel Punzalan and Runxuan Jiang and Joshua Kammeraad and Ambuj Tewari and Paul Zimmerman},
title = {{TorsionNet}: A Reinforcement Learning Approach to Sequential Conformer Search},
booktitle = {Advances in Neural Information Processing Systems 33},
year = {2020},
pdf = {research/gogineni20torsionnet.pdf},
url = {https://proceedings.neurips.cc/paper/2020}
}
@article{faradonbeh20optimism,
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
title = {Optimism-Based Adaptive Regulation of Linear-Quadratic Systems},
journal = {IEEE Transactions on Automatic Control},
volume = {66},
number = {4},
pages = {1802--1808},
month = {April},
year = {2020},
pdf = {research/faradonbeh20optimism.pdf},
url = {https://doi.org/10.1109/TAC.2020.2998952}
}
@article{wong20lasso,
author = {Kam Chung Wong and Zifan Li and Ambuj Tewari},
title = {Lasso Guarantees for $\beta$-Mixing Heavy Tailed Time Series},
journal = {Annals of Statistics},
year = {2020},
volume = {48},
number = {2},
pages = {1124--1142},
url = {http://dx.doi.org/10.1214/19-AOS1840},
pdf = {research/wong20lasso.pdf}
}
@inproceedings{modi20no-regret,
author = {Aditya Modi and Ambuj Tewari},
title = {No-regret Exploration in Contextual Reinforcement Learning},
booktitle = {Proceedings of the 36th Annual Conference on Uncertainty in Artificial Intelligence},
year = {2020},
pdf = {research/modi20no-regret.pdf},
url = {http://www.auai.org/uai2020/proceedings/355_main_paper.pdf}
}
@inproceedings{niss20what,
author = {Laura Niss and Ambuj Tewari},
title = {What You See May Not Be What You Get: {UCB} Bandit Algorithms Robust to ε-Contamination},
booktitle = {Proceedings of the 36th Annual Conference on Uncertainty in Artificial Intelligence},
year = {2020},
pdf = {research/niss20what.pdf},
url = {http://www.auai.org/uai2020/proceedings/198_main_paper.pdf}
}
@inproceedings{lu20regret,
author = {Yangyi Lu and Amirhossein Meisami and Ambuj Tewari and Zhenyu Yan},
title = {Regret Analysis of Bandit Problems with Causal Background Knowledge},
booktitle = {Proceedings of the 36th Annual Conference on Uncertainty in Artificial Intelligence},
year = {2020},
pdf = {research/lu20regret.pdf},
url = {http://www.auai.org/uai2020/proceedings/77_main_paper.pdf}
}
@inproceedings{kim20randomized,
author = {Baekjin Kim and Ambuj Tewari},
title = {Randomized Exploration for Non-Stationary Stochastic Linear Bandits},
booktitle = {Proceedings of the 36th Annual Conference on Uncertainty in Artificial Intelligence},
year = {2020},
pdf = {research/kim20randomized.pdf},
url = {http://www.auai.org/uai2020/proceedings/49_main_paper.pdf}
}
@article{faradonbeh20adaptive,
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
title = {On Adaptive Linear-Quadratic Regulators},
journal = {Automatica},
year = {2020},
volume = {117},
month = {July},
url = {https://doi.org/10.1016/j.automatica.2020.108982},
pdf = {research/faradonbeh20adaptive.pdf}
}
@article{kammeraad20machine,
title = {What Does the Machine Learn? {K}nowledge Representations of Chemical Reactivity},
author = {Joshua Andrew Kammeraad and Jack Goetz and Eric A. Walker and Ambuj Tewari and Paul M. Zimmerman},
journal = {Journal of Chemical Information and Modeling},
year = {2020},
volume = {60},
number = {3},
pages = {1290-1301},
url = {https://dx.doi.org/10.1021/acs.jcim.9b00721},
pdf = {research/kammeraad20machine.pdf}
}
@article{faradonbeh20input,
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
title = {Input Perturbations for Adaptive Control and Learning},
journal = {Automatica},
year = {2020},
volume = {117},
month = {July},
url = {https://doi.org/10.1016/j.automatica.2020.108950},
pdf = {research/faradonbeh20input.pdf}
}
@inproceedings{modi20sample,
author = {Aditya Modi and Nan Jiang and Ambuj Tewari and Satinder Singh},
title = {Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles},
booktitle = {The 23rd International Conference on Artificial Intelligence and Statistics},
year = {2020},
pdf = {research/modi20sample.pdf},
url = {http://proceedings.mlr.press/v108/modi20a.html}
}
@article{necamp20assessing,
title = {Assessing Real-Time Moderation for Developing Adaptive Mobile Health Interventions for Medical Interns: Micro-randomized Trial},
author = {Timothy NeCamp and Srijan Sen and Elena Frank and Maureen A. Walton and Edward L. Ionides and Yu Fang and Ambuj Tewari and Zhenke Wu},
journal = {Journal of Medical Internet Research},
volume = {22},
number = {3},
pages = {e15033},
year = {2020},
url = {https://doi.org/10.2196/15033},
pdf = {research/necamp20assessing.pdf}
}
@inproceedings{balghiti19generalization,
author = {Othman El Balghiti and Adam Elmachtoub and Paul Grigas and Ambuj Tewari},
title = {Generalization Bounds in the Predict-then-Optimize Framework},
booktitle = {Advances in Neural Information Processing Systems 32},
year = {2019},
pages = {14389--14398},
url = {https://proceedings.neurips.cc/paper/2019},
pdf = {research/balghiti19generalization.pdf}
}
@inproceedings{jung19regret,
author = {Young Hun Jung and Ambuj Tewari},
title = {Regret Bounds for {T}hompson Sampling in Restless Bandit Problems},
booktitle = {Advances in Neural Information Processing Systems 32},
year = {2019},
pages = {9005--9014},
url = {https://proceedings.neurips.cc/paper/2019},
pdf = {research/jung19regret.pdf}
}
@inproceedings{abernethy19online,
author = {Jacob Abernethy and Young Hun Jung and Chansoo Lee and Audra McMillan and Ambuj Tewari},
title = {Online Learning via the Differential Privacy Lens},
booktitle = {Advances in Neural Information Processing Systems 32},
year = {2019},
pages = {8892--8902},
url = {https://proceedings.neurips.cc/paper/2019},
pdf = {research/abernethy19online.pdf}
}
@inproceedings{kim19optimality,
author = {Baekjin Kim and Ambuj Tewari},
title = {On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {2691--2700},
year = {2019},
url = {https://proceedings.neurips.cc/paper/2019},
pdf = {research/kim19optimality.pdf}
}
@article{walker2019learning,
title = {Learning to Predict Reaction Conditions: Relationships between Solvent, Molecular Structure, and Catalyst},
author = {Eric Walker and Joshua Andrew Kammeraad and Jack Goetz and Michael Robo and Ambuj Tewari and Paul M. Zimmerman},
journal = {Journal of Chemical Information and Modeling},
volume = {59},
number = {9},
pages = {3645--3654},
year = {2019},
url = {https://doi.org/10.1021/acs.jcim.9b00313},
pdf = {research/walker2019learning.pdf}
}
@inproceedings{faradonbeh19applications,
title = {On Applications of Bootstrap in Continuous Space Reinforcement Learning},
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
booktitle = {Proceedings of the 2019 IEEE 58th Conference on Decision and Control},
year = {2019},
pages = {1977--1984},
pdf = {research/faradonbeh19applications.pdf},
url = {https://doi.org/10.1109/CDC40024.2019.9029975}
}
@incollection{rabbi19optimizing,
author = {Mashfiqui Rabbi and Predrag Klasnja and Tanzeem Choudhury and Ambuj Tewari and Susan Murphy},
title = {Optimizing mHealth Interventions with a Bandit},
booktitle = {Mobile Sensing and Digital Phenotyping: New Developments in Psychoinformatics},
publisher = {Springer},
year = {2019},
editor = {Harald Baumeister and Christian Montag},
url = {https://doi.org/10.1007/978-3-030-31620-4_18},
pdf = {research/rabbi19optimizing.pdf}
}
@inproceedings{faradonbeh19randomized,
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
title = {Randomized Algorithms for Data-Driven Stabilization of Stochastic Linear Systems},
booktitle = {Proceedings of the 2019 IEEE Data Science Workshop},
pages = {170--174},
year = {2019},
url = {https://doi.org/10.1109/DSW.2019.8755578},
pdf = {research/faradonbeh19randomized.pdf}
}
@article{faradonbeh19finite,
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
title = {Finite Time Adaptive Stabilization of {LQ} Systems},
journal = {IEEE Transactions on Automatic Control},
volume = {64},
number = {8},
month = aug,
pages = {3498--3505},
year = {2019},
url = {https://doi.org/10.1109/TAC.2018.2883241},
pdf = {research/faradonbeh19finite.pdf}
}
@inproceedings{zhang19online,
author = {Daniel T. Zhang and Young Hun Jung and Ambuj Tewari},
title = {Online Multiclass Boosting with Bandit Feedback},
booktitle = {Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics},
series = {Proceedings of Machine Learning Research},
volume = {89},
year = {2019},
pages = {1148--1156},
pdf = {research/zhang19online.pdf},
url = {http://proceedings.mlr.press/v89/zhang19e.html}
}
@inproceedings{sun18linear,
author = {Yitong Sun and Anna C. Gilbert and Ambuj Tewari},
title = {But How Does It Work in Theory? {L}inear {SVM} with Random Features},
booktitle = {Advances in Neural Information Processing Systems 31},
year = {2018},
pages = {3383--3392},
url = {https://proceedings.neurips.cc/paper/2018},
pdf = {research/sun18linear.pdf}
}
@inproceedings{goetz18active,
author = {Jack Goetz and Ambuj Tewari and Paul Zimmerman},
title = {Active Learning for Non-Parametric Regression Using Purely Random Trees},
booktitle = {Advances in Neural Information Processing Systems 31},
year = {2018},
pages = {2542--2551},
url = {https://proceedings.neurips.cc/paper/2018},
pdf = {research/goetz18active.pdf}
}
@article{nahum-shani18just-in-time,
author = {Inbal Nahum-Shani and Shawna N. Smith and Bonnie J. Spring and Linda M. Collins and Katie Witkiewitz and Ambuj Tewari and Susan A. Murphy},
title = {Just-in-Time Adaptive Interventions {(JITAIs)} in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support},
journal = {Annals of Behavioral Medicine},
month = {May},
volume = {52},
number = {6},
year = {2018},
pages = {446--462},
url = {http://dx.doi.org/10.1007/s12160-016-9830-8},
pdf = {research/nahum-shani18just-in-time.pdf}
}
@article{faradonbeh18finite,
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
title = {Finite Time Identification in Unstable Linear Systems},
journal = {Automatica},
year = {2018},
volume = {96},
month = {October},
pages = {342--353},
url = {https://doi.org/10.1016/j.automatica.2018.07.008},
pdf = {research/faradonbeh18finite.pdf}
}
@inproceedings{modi18markov,
author = {Aditya Modi and Nan Jiang and Satinder Singh and Ambuj Tewari},
title = {{M}arkov Decision Processes with Continuous Side Information},
booktitle = {Proceedings of the 29th International Conference on Algorithmic Learning Theory},
series = {Proceedings of Machine Learning Research},
volume = {83},
pages = {597--618},
year = {2018},
url = {http://proceedings.mlr.press/v83/modi18a.html},
pdf = {research/modi18markov.pdf}
}
@inproceedings{jung18online,
author = {Young Hun Jung and Ambuj Tewari},
title = {Online Boosting Algorithms for Multi-label Ranking},
booktitle = {Proceedings of the 21st International Conference on Artificial Intelligence and Statistics},
series = {Proceedings of Machine Learning Research},
volume = {84},
pages = {279--287},
year = {2018},
url = {http://proceedings.mlr.press/v84/jung18a.html},
pdf = {research/jung18online.pdf}
}
@article{li18beyond,
author = {Zifan Li and Ambuj Tewari},
title = {Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits},
journal = {Journal of Machine Learning Research},
year = {2018},
volume = {18},
number = {183},
pages = {1--24},
url = {http://jmlr.org/papers/v18/17-364.html},
pdf = {research/li18beyond.pdf}
}
@article{natarajan18cost,
author = {Nagarajan Natarajan and Inderjit S. Dhillon and Pradeep Ravikumar and Ambuj Tewari},
title = {Cost-Sensitive Learning with Noisy Labels},
journal = {Journal of Machine Learning Research},
volume = {18},
number = {155},
pages = {1--33},
year = {2018},
url = {http://jmlr.org/papers/v18/15-226.html},
pdf = {research/natarajan18cost.pdf}
}
@article{li18sampled,
author = {Zifan Li and Ambuj Tewari},
title = {Sampled Fictitious Play is {H}annan Consistent},
journal = {Games and Economic Behavior},
year = {2018},
volume = {109},
pages = {401--412},
url = {https://doi.org/10.1016/j.geb.2018.01.005},
pdf = {research/li18sampled.pdf}
}
@article{ramaswamy18consistent,
author = {Harish G. Ramaswamy and Ambuj Tewari and Shivani Agarwal},
title = {Consistent Algorithms for Multiclass Classification with an Abstain Option},
journal = {Electronic Journal of Statistics},
volume = {12},
number = {1},
year = {2018},
pages = {530--554},
url = {https://doi.org/10.1214/17-EJS1388},
pdf = {research/ramaswamy18consistent.pdf}
}
@article{faradonbeh17optimality,
author = {Mohamad Kazem Shirani Faradonbeh and Ambuj Tewari and George Michailidis},
title = {Optimality of Fast Matching Algorithms for Random Networks with Applications to Structural Controllability},
journal = {IEEE Transactions on Control of Network Systems},
year = {2017},
volume = {4},
number = {4},
pages = {770--780},
url = {https://doi.org/10.1109/TCNS.2016.2553366},
pdf = {research/faradonbeh17optimality.pdf}
}
@inproceedings{greenewald17action,
author = {Kristjan Greenewald and Ambuj Tewari and Predrag Klasnja and Susan A. Murphy},
title = {Action Centered Contextual Bandits},
booktitle = {Advances in Neural Information Processing Systems 30},
year = {2017},
pages = {5979--5987},
url = {https://proceedings.neurips.cc/paper/2017},
pdf = {research/greenewald17action.pdf}
}
@inproceedings{jung17online,
author = {Young Hun Jung and Jack Goetz and Ambuj Tewari},
title = {Online Multiclass Boosting},
booktitle = {Advances in Neural Information Processing Systems 30},
year = {2017},
pages = {920--929},
url = {https://proceedings.neurips.cc/paper/2017},
pdf = {research/jung17online.pdf}
}
@article{chaudhuri17online,
author = {Sougata Chaudhuri and Ambuj Tewari},
title = {Online Learning to Rank with Top-k Feedback},
journal = {Journal of Machine Learning Research},
volume = {18},
number = {103},
pages = {1--50},
year = {2017},
pdf = {research/chaudhuri17online.pdf},
url = {http://jmlr.org/papers/v18/16-285.html}
}
@article{jain17partial,
author = {Prateek Jain and Inderjit S. Dhillon and Ambuj Tewari},
title = {Partial Hard Thresholding},
journal = {IEEE Transactions on Information Theory},
volume = {63},
number = {5},
year = {2017},
pages = {3029--3038},
url = {https://doi.org/10.1109/TIT.2017.2686880},
pdf = {research/jain17partial.pdf}
}
@incollection{tewari17ads,
author = {Ambuj Tewari and Susan A. Murphy},
title = {From Ads to Interventions: Contextual Bandits in Mobile Health},
booktitle = {Mobile Health: Sensors, Analytic Methods, and Applications},
publisher = {Springer},
year = {2017},
editor = {Jim Rehg and Susan A. Murphy and Santosh Kumar},
url = {https://www.springer.com/us/book/9783319513935},
pdf = {research/tewari17ads.pdf}
}
@inproceedings{chaudhuri16phased,
author = {Sougata Chaudhuri and Ambuj Tewari},
title = {Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games},
booktitle = {Advances in Neural Information Processing Systems 29},
year = {2016},
pages = {2433--2441},
pdf = {research/chaudhuri16phased.pdf},
url = {https://proceedings.neurips.cc/paper/2016}
}
@inproceedings{ramaswamy16mixture,
author = {Harish G. Ramaswamy and Clayton Scott and Ambuj Tewari},
title = {Mixture Proportion Estimation via Kernel Embeddings of Distributions},
booktitle = {Proceedings of the 33rd International Conference on Machine Learning},
series = {JMLR Workshop and Conference Proceedings},
volume = {48},
pages = {2052--2060},
year = {2016},
url = {http://jmlr.org/proceedings/papers/v48/ramaswamy16.html},
pdf = {research/ramaswamy16mixture.pdf}
}
@inproceedings{jiang16structural,
author = {Nan Jiang and Satinder Singh and Ambuj Tewari},
title = {On Structural Properties of {MDPs} that Bound Loss due to Shallow Planning},
booktitle = {Proceedings of the 25th International Joint Conference on Artificial Intelligence},
year = {2016},
pages = {1640--1647},
publisher = {AAAI Press},
url = {http://www.ijcai.org/Abstract/16/235},
pdf = {research/jiang16structural.pdf}
}
@incollection{abernethy16perturbation,
author = {Jacob Abernethy and Chansoo Lee and Ambuj Tewari},
title = {Perturbation Techniques in Online Learning and Optimization},
booktitle = {Perturbations, Optimization, and Statistics},
series = {Neural Information Processing Series},
publisher = {MIT Press},
year = {2016},
editor = {Tamir Hazan and George Papandreou and Daniel Tarlow},
chapter = {8},
pdf = {research/abernethy16perturbation.pdf},
url = {https://mitpress.mit.edu/books/perturbations-optimization-and-statistics}
}
@inproceedings{chaudhuri16online,
author = {Sougata Chaudhuri and Ambuj Tewari},
title = {Online Learning to Rank with Feedback at the Top},
booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
pages = {277--285},
series = {JMLR Workshop and Conference Proceedings},
volume = {51},
year = {2016},
pdf = {research/chaudhuri16online.pdf},
url = {http://jmlr.org/proceedings/papers/v51/chaudhuri16.html}
}
@article{liao16sample,
author = {Peng Liao and Predrag Klasnja and Ambuj Tewari and Susan A. Murphy},
title = {Sample Size Calculations for Micro-randomized Trials in mHealth},
journal = {Statistics in Medicine},
volume = {35},
number = {12},
pages = {1944--1971},
year = {2016},
url = {http://dx.doi.org/10.1002/sim.6847},
pdf = {research/liao16sample.pdf}
}
@article{klasnja15microrandomized,
author = {Predrag Klasnja and Eric B. Hekler and Saul Shiffman and Audrey Boruvka and Daniel Almirall and Ambuj Tewari and Susan A. Murphy},
title = {Microrandomized trials: An experimental design for developing just-in-time adaptive interventions},
journal = {Health Psychology},
volume = {34},
number = {Suppl},
month = {Dec},
pages = {1220--1228},
year = {2015},
url = {http://dx.doi.org/10.1037/hea0000305},
pdf = {research/klasnja15microrandomized.pdf}
}
@inproceedings{jain15predtron,
author = {Prateek Jain and Nagarajan Natarajan and Ambuj Tewari},
title = {Predtron: A Family of Online Algorithms for General Prediction Problems},
booktitle = {Advances in Neural Information Processing Systems 28},
pages = {1009--1017},
year = {2015},
url = {https://proceedings.neurips.cc/paper/2015},
pdf = {research/jain15predtron.pdf}
}
@inproceedings{jain15alternating,
author = {Prateek Jain and Ambuj Tewari},
title = {Alternating Minimization for Regression Problems with Vector-valued Outputs},
booktitle = {Advances in Neural Information Processing Systems 28},
pages = {1126--1134},
year = {2015},
url = {https://proceedings.neurips.cc/paper/2015},
pdf = {research/jain15alternating.pdf}
}
@inproceedings{abernethy15fighting,
author = {Jacob Abernethy and Chansoo Lee and Ambuj Tewari},
title = {Fighting Bandits with a New Kind of Smoothness},
booktitle = {Advances in Neural Information Processing Systems 28},
pages = {2188--2196},
year = {2015},
url = {https://proceedings.neurips.cc/paper/2015},
pdf = {research/abernethy15fighting.pdf}
}
@inproceedings{ramaswamy15convex,
author = {Harish G. Ramaswamy and Ambuj Tewari and Shivani Agarwal},
title = {Convex Calibrated Surrogates for Hierarchical Classification},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning},
series = {JMLR Workshop and Conference Proceedings},
volume = {37},
pages = {1852--1860},
year = {2015},
url = {http://jmlr.org/proceedings/papers/v37/ramaswamy15.html},
pdf = {research/ramaswamy15convex.pdf}
}
@inproceedings{tewari15generalization,
author = {Ambuj Tewari and Sougata Chaudhuri},
title = {Generalization error bounds for learning to rank: Does the length of document lists matter?},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning},
series = {JMLR Workshop and Conference Proceedings},
volume = {37},
pages = {315--323},
year = {2015},
url = {http://jmlr.org/proceedings/papers/v37/tewari15.html},
pdf = {research/tewari15generalization.pdf}
}
@inproceedings{chaudhuri15online,
author = {Sougata Chaudhuri and Ambuj Tewari},
title = {Online Ranking with Top-1 Feedback},
booktitle = {Proceedings of the 18th International Conference on Artificial Intelligence and Statistics},
series = {JMLR Workshop and Conference Proceedings},
volume = {38},
pages = {129--137},
year = {2015},
note = {{\bf Honorable Mention, Best Student Paper Award}},
pdf = {research/chaudhuri15online.pdf},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v38/chaudhuri15.html}
}
@article{rakhlin15online,
author = {Alexander Rakhlin and Karthik Sridharan and Ambuj Tewari},
title = {Online Learning via Sequential Complexities},
year = {2015},
journal = {Journal of Machine Learning Research},
volume = {16},
month = {Feb},
pages = {155--186},
pdf = {research/rakhlin15online.pdf},
url = {http://jmlr.org/papers/v16/rakhlin15a.html}
}
@article{rakhlin15sequential,
author = {Alexander Rakhlin and Karthik Sridharan and Ambuj Tewari},
title = {Sequential Complexities and Uniform Martingale Laws of Large Numbers},
journal = {Probability Theory and Related Fields},
year = {2015},
volume = {161},
number = {1--2},
pages = {111--153},
pdf = {research/rakhlin15sequential.pdf},
url = {http://dx.doi.org/10.1007/s00440-013-0545-5}
}
@inproceedings{jain14iterative,
author = {Prateek Jain and Ambuj Tewari and Purushottam Kar},
title = {On Iterative Hard Thresholding Methods for High-dimensional {M}-Estimation},
booktitle = {Advances in Neural Information Processing Systems 27},
pages = {685--693},
year = {2014},
pdf = {research/jain14iterative.pdf},
url = {https://proceedings.neurips.cc/paper/2014}
}
@inproceedings{abernethy14online,
author = {Jacob Abernethy and Chansoo Lee and Abhinav Sinha and Ambuj Tewari},
title = {Online Linear Optimization via Smoothing},
series = {JMLR Workshop and Conference Proceedings},
volume = {35},
year = {2014},
pages = {807--823},
booktitle = {Proceedings of the 27th Annual Conference on Learning Theory},
pdf = {research/abernethy14online.pdf},
url = {http://jmlr.org/proceedings/papers/v35/abernethy14.html}
}
@article{chiang14prediction,
author = {Kai-Yang Chiang and Cho-Jui Hsieh and Nagarajan Natarajan and Ambuj Tewari and Inderjit S. Dhillon},
title = {Prediction and Clustering in Signed Networks: A Local to Global Perspective},
journal = {Journal of Machine Learning Research},
year = {2014},
volume = {15},
pages = {1177--1213},
month = {March},
pdf = {research/chiang14prediction.pdf},
url = {http://jmlr.org/papers/v15/chiang14a.html}
}
@incollection{tewari13learning,
author = {Ambuj Tewari and Peter L. Bartlett},
title = {Learning Theory},
booktitle = {Academic Press Library in Signal Processing: Volume 1 Signal Processing Theory and Machine Learning},
series = {Academic Press Library in Signal Processing},
volume = {1},
publisher = {Elsevier},
year = {2014},
editor = {Paulo S.R. Diniz and Johan A.K. Suykens and Rama Chellappa and Sergios Theodoridis},
chapter = {14},
pages = {775--816},
pdf = {research/tewari13learning.pdf},
url = {http://dx.doi.org/10.1016/B978-0-12-396502-8.00014-0}
}
@inproceedings{ramaswamy13convex,
author = {Harish G. Ramaswamy and Shivani Agarwal and Ambuj Tewari},
title = {Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses},
booktitle = {Advances in Neural Information Processing Systems 26},
pages = {1475--1483},
year = {2013},
pdf = {research/ramaswamy13convex.pdf},
url = {https://proceedings.neurips.cc/paper/2013}
}
@inproceedings{natarajan13learning,
author = {Nagarajan Natarajan and Inderjit S. Dhillon and Pradeep Ravikumar and Ambuj Tewari},
title = {Learning with Noisy Labels},
booktitle = {Advances in Neural Information Processing Systems 26},
pages = {1196--1204},
year = {2013},
pdf = {research/natarajan13learning.pdf},
url = {https://proceedings.neurips.cc/paper/2013}
}
@inproceedings{yang13robust,
author = {Eunho Yang and Ambuj Tewari and Pradeep Ravikumar},
title = {On Robust Estimation of High Dimensional Generalized Linear Models},
booktitle = {Proceedings of the 23rd International Joint Conference on Artificial Intelligence},
year = {2013},
pages = {1834--1840},
publisher = {AAAI Press},
url = {http://ijcai.org/Abstract/13/271},
pdf = {research/yang13robust.pdf}
}
@article{singh-blom13prediction,
author = {U. Martin Singh-Blom and Nagarajan Natarajan and Ambuj Tewari and John O. Woods and Inderjit S. Dhillon and Edward M. Marcotte},
title = {Prediction and validation of gene-disease associations using methods inspired by social network analyses},
year = {2013},
journal = {PLoS One},
volume = {8},
number = {5},
pages = {e58977},
url = {http://dx.doi.org/10.1371/journal.pone.0058977},
pdf = {research/singh-blom13prediction.pdf}
}
@article{saha13non-asymptotic,
author = {Ankan Saha and Ambuj Tewari},
title = {On the Non-asymptotic Convergence of Cyclic Coordinate Descent Methods},
year = {2013},
volume = {23},
number = {1},
journal = {SIAM Journal on Optimization},
pages = {576--601},
url = {http://dx.doi.org/10.1137/110840054},
pdf = {research/saha13non-asymptotic.pdf}
}
@inproceedings{scherrer12feature,
author = {Chad Scherrer and Ambuj Tewari and Mahantesh Halappanavar and David Haglin},
title = {Feature Clustering for Accelerating Parallel Coordinate Descent},
year = {2012},
booktitle = {Advances in Neural Information Processing Systems 25},
pages = {28--36},
pdf = {research/scherrer12feature.pdf},
url = {https://proceedings.neurips.cc/paper/2012}
}
@inproceedings{arora12deterministic,
author = {Raman Arora and Ofer Dekel and Ambuj Tewari},
title = {Deterministic {MDPs} with Adversarial Rewards and Bandit Feedback},
booktitle = {Proceedings of the 28th Annual Conference on Uncertainty in Artificial Intelligence},
year = {2012},
pages = {93--101},
publisher = {AUAI Press},
url = {https://arxiv.org/abs/1210.4843},
pdf = {research/arora12deterministic.pdf}
}
@inproceedings{shukla12parallelizing,
author = {Shilpa Shukla and Matthew Lease and Ambuj Tewari},
title = {Parallelizing {L}ist{N}et Training using {S}park},
booktitle = {Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {1127--1128},
year = {2012},
url = {http://dx.doi.org/10.1145/2348283.2348502},
pdf = {research/shukla12parallelizing.pdf}
}
@inproceedings{arora12online,
author = {Raman Arora and Ofer Dekel and Ambuj Tewari},
title = {Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret},
booktitle = {Proceedings of the 29th International Conference on Machine Learning},
year = {2012},
pages = {1503--1510},
publisher = {Omnipress},
url = {https://dl.acm.org/doi/10.5555/3042573.3042796},
pdf = {research/arora12online.pdf}
}
@inproceedings{kalyanakrishnan12pac,
author = {Shivaram Kalyanakrishnan and Ambuj Tewari and Peter Auer and Peter Stone},
title = {{PAC} Subset Selection in Stochastic Multi-armed Bandits},
booktitle = {Proceedings of the 29th International Conference on Machine Learning},
year = {2012},
pages = {655--662},
publisher = {Omnipress},
url = {https://dl.acm.org/doi/10.5555/3042573.3042606},
pdf = {research/kalyanakrishnan12pac.pdf}
}
@inproceedings{scherrer12scaling,
author = {Chad Scherrer and Mahantesh Halappanavar and Ambuj Tewari and David Haglin},
title = {Scaling Up Coordinate Descent Algorithms for Large $l_1$ Regularization Problems},
booktitle = {Proceedings of the 29th International Conference on Machine Learning},
year = {2012},
pages = {1407--1414},
publisher = {Omnipress},
url = {https://dl.acm.org/doi/10.5555/3042573.3042622},
pdf = {research/scherrer12scaling.pdf}
}
@article{kakade12regularization,
author = {Sham M. Kakade and Shai Shalev-Shwartz and Ambuj Tewari},
title = {Regularization Techniques for Learning with Matrices},
month = {June},
year = {2012},
journal = {Journal of Machine Learning Research},
volume = {13},
pages = {1865--1890},
pdf = {research/kakade12regularization.pdf},
url = {http://jmlr.csail.mit.edu/papers/v13/kakade12a.html}
}
@inproceedings{yang12perturbation,
author = {Eunho Yang and Ambuj Tewari and Pradeep Ravikumar},
title = {Perturbation based Large Margin Approach for Ranking},
booktitle = {Proceedings of the 15th International Conference on Artificial Intelligence and Statistics},
series = {JMLR Workshop and Conference Proceedings},
volume = {22},
year = {2012},
pages = {1358--1366},
pdf = {research/yang12perturbation.pdf},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v22/}
}
@inproceedings{srebro11universality,
author = {Nathan Srebro and Karthik Sridharan and Ambuj Tewari},
title = {On the Universality of Online Mirror Descent},
year = {2011},
booktitle = {Advances in Neural Information Processing Systems 24},
pages = {2645--2653},
note = {longer version available as arXiv:1107.4080},
pdf = {research/srebro11universality.pdf},
url = {https://proceedings.neurips.cc/paper/2011}
}
@inproceedings{jain11orthogonal,
author = {Prateek Jain and Ambuj Tewari and Inderjit S. Dhillon},
title = {Orthogonal Matching Pursuit with Replacement},
year = {2011},
booktitle = {Advances in Neural Information Processing Systems 24},
pages = {1215--1223},
note = {longer version available as arXiv:1106.2774},
pdf = {research/jain11orthogonal.pdf},
url = {https://proceedings.neurips.cc/paper/2011}
}
@inproceedings{tewari11greedy,
author = {Ambuj Tewari and Pradeep Ravikumar and Inderjit S. Dhillon},
title = {Greedy Algorithms for Structurally Constrained High Dimensional Problems},
year = {2011},
booktitle = {Advances in Neural Information Processing Systems 24},
pages = {882--890},
pdf = {research/tewari11greedy.pdf},
url = {https://proceedings.neurips.cc/paper/2011}
}
@inproceedings{dhillon11nearest,
author = {Inderjit S. Dhillon and Pradeep Ravikumar and Ambuj Tewari},
title = {Nearest Neighbor based Greedy Coordinate Descent},
year = {2011},
booktitle = {Advances in Neural Information Processing Systems 24},
pages = {2160--2168},
pdf = {research/dhillon11nearest.pdf},
url = {https://proceedings.neurips.cc/paper/2011}
}
@inproceedings{rakhlin11stochastic,
author = {Alexander Rakhlin and Karthik Sridharan and Ambuj Tewari},
title = {Online Learning: Stochastic, Constrained, and Smoothed Adversaries},
year = {2011},
booktitle = {Advances in Neural Information Processing Systems 24},
pages = {1764--1772},
note = {longer (but older) version available as arXiv:1104.5070},
pdf = {research/rakhlin11stochastic.pdf},
url = {https://proceedings.neurips.cc/paper/2011}
}
@inproceedings{chiang11exploiting,
author = {Kai-Yang Chiang and Nagarajan Natarajan and Ambuj Tewari and Inderjit S. Dhillon},
title = {Exploiting Longer Cycles for Link Prediction in Signed Networks},
booktitle = {Proceedings of the 20th ACM Conference on Information and Knowledge Management},
year = {2011},
pages = {1157--1162},
pdf = {research/chiang11exploiting.pdf},
url = {http://dx.doi.org/10.1145/2063576.2063742}
}
@article{shalev-shwartz11stochastic,
author = {Shai Shalev-Shwartz and Ambuj Tewari},
title = {Stochastic Methods for $l_1$ Regularized Loss Minimization},
journal = {Journal of Machine Learning Research},
volume = {12},
month = {June},
pages = {1865--1892},
year = {2011},
pdf = {research/shalev-shwartz11stochastic.pdf},
url = {http://jmlr.csail.mit.edu/papers/v12/shalev-shwartz11a.html}
}
@inproceedings{rakhlin11online,
author = {Alexander Rakhlin and Karthik Sridharan and Ambuj Tewari},
title = {Online Learning: Beyond Regret},
year = {2011},
booktitle = {Proceedings of the 24th Annual Conference on Learning Theory},
series = {JMLR Workshop and Conference Proceedings},
volume = {19},
pages = {559--594},
note = {{\bf Best Paper Award}},
pdf = {research/rakhlin11online.pdf},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v19/}
}
@inproceedings{foster11complexity-based,
author = {Dean Foster and Alexander Rakhlin and Karthik Sridharan and Ambuj Tewari},
title = {Complexity-Based Approach to Calibration with Checking Rules},
year = {2011},
booktitle = {Proceedings of the 24th Annual Conference on Learning Theory},
series = {JMLR Workshop and Conference Proceedings},
volume = {19},
pages = {293--314},
pdf = {research/foster11complexity-based.pdf},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v19/}
}
@inproceedings{ravikumar11ndcg,
author = {Pradeep Ravikumar and Ambuj Tewari and Eunho Yang},
title = {On {NDCG} Consistency of Listwise Ranking Methods},
booktitle = {Proceedings of the 14th International Conference on Artificial Intelligence and Statistics},
series = {JMLR Workshop and Conference Proceedings},
volume = {15},
year = {2011},
pages = {618--626},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v15/},
pdf = {research/ravikumar11ndcg.pdf}
}
@inproceedings{saha11improved,
author = {Ankan Saha and Ambuj Tewari},
title = {Improved Regret Guarantees for Online Smooth Convex Optimization with Bandit Feedback},
booktitle = {Proceedings of the 14th International Conference on Artificial Intelligence and Statistics},
series = {JMLR Workshop and Conference Proceedings},
volume = {15},
year = {2011},
pages = {636-642},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v15/},
pdf = {research/saha11improved.pdf}
}
@inproceedings{rakhlin10online,
author = {Alexander Rakhlin and Karthik Sridharan and Ambuj Tewari},
title = {Online Learning: Random Averages, Combinatorial Parameters, and Learnability},
year = {2010},
booktitle = {Advances in Neural Information Processing Systems 23},
pages = {1984--1992},
pdf = {research/rakhlin10online.pdf},
url = {https://proceedings.neurips.cc/paper/2010}
}
@inproceedings{srebro10smoothness,
author = {Nathan Srebro and Karthik Sridharan and Ambuj Tewari},
title = {Smoothness, Low Noise, and Fast Rates},
year = {2010},
booktitle = {Advances in Neural Information Processing Systems 23},
pages = {2199--2207},
pdf = {research/srebro10smoothness.pdf},
url = {https://proceedings.neurips.cc/paper/2010}
}
@inproceedings{duchi10composite,
author = {John Duchi and Shai Shalev-Shwartz and Yoram Singer and Ambuj Tewari},
title = {Composite Objective Mirror Descent},
year = {2010},
booktitle = {Proceedings of the 23rd Annual Conference on Learning Theory},
pages = {14--26},
publisher = {Omnipress},
url = {http://www.learningtheory.org/colt2010/conference-website/proceedings.html},
pdf = {research/duchi10composite.pdf}
}
@inproceedings{sridharan10convex,
author = {Karthik Sridharan and Ambuj Tewari},
title = {Convex Games in {B}anach Spaces},
year = {2010},
booktitle = {Proceedings of the 23rd Annual Conference on Learning Theory},
pages = {1--13},
publisher = {Omnipress},
url = {http://www.learningtheory.org/colt2010/conference-website/proceedings.html},
pdf = {research/sridharan10convex.pdf}
}
@inproceedings{kakade10learning,
author = {Sham M. Kakade and Ohad Shamir and Karthik Sridharan and Ambuj Tewari},
title = {Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity},
booktitle = {Proceedings of the 13th International Conference on Artificial Intelligence and Statistics},
series = {JMLR Workshop and Conference Proceedings},
volume = {9},
year = {2010},
pages = {381--388},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v9/},
pdf = {research/kakade10learning.pdf}
}
@inproceedings{bartlett09regal,
author = {Peter L. Bartlett and Ambuj Tewari},
title = {{REGAL}: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating {MDP}s},
booktitle = {Proceedings of the 25th Annual Conference on Uncertainty in Artificial Intelligence},
year = {2009},
pages = {35--42},
publisher = {AUAI Press},
url = {http://www.auai.org/uai2009/papers/UAI2009_0115_adc2f3cd89e91f1bb9535973523e7dae.pdf},
pdf = {research/bartlett09regal.pdf}
}
@inproceedings{shalev-shwartz09stochastic,
author = {Shai Shalev-Shwartz and Ambuj Tewari},
title = {Stochastic Methods for $l_1$ Regularized Loss Minimization},
booktitle = {Proceedings of the 26th International Conference on Machine Learning},
pages = {929--936},
year = {2009},
publisher = {ACM Press},
url = {http://doi.acm.org/10.1145/1553374.1553493},
pdf = {research/shalev-shwartz09stochastic.pdf}
}
@inproceedings{kakade09generalization,
author = {Sham M. Kakade and Ambuj Tewari},
title = {On the Generalization Ability of Online Strongly Convex Programming Algorithms},
booktitle = {Advances in Neural Information Processing Systems 21},
pages = {801--808},
year = {2009},
publisher = {MIT Press},
url = {https://proceedings.neurips.cc/paper/2009},
pdf = {research/kakade09generalization.pdf}
}
@inproceedings{kakade09complexity,
author = {Sham M. Kakade and Karthik Sridharan and Ambuj Tewari},
title = {On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization},
booktitle = {Advances in Neural Information Processing Systems 21},
pages = {793--800},
year = {2009},
publisher = {MIT Press},
url = {https://proceedings.neurips.cc/paper/2009},
pdf = {research/kakade09complexity.pdf}
}
@inproceedings{bartlett08high-probability,
author = {Peter L. Bartlett and Varsha Dani and Thomas P. Hayes and Sham M. Kakade and Alexander Rakhlin and Ambuj Tewari},
title = {High-probability Regret Bounds for Bandit Online Linear Optimization},
booktitle = {Proceedings of the 21st Annual Conference on Learning Theory},
pages = {335--342},
year = {2008},
publisher = {Omnipress},
url = {http://colt2008.cs.helsinki.fi/programme.shtml},
pdf = {research/bartlett08high-probability.pdf}
}
@inproceedings{abernethy08optimal,
author = {Jacob Abernethy and Peter L. Bartlett and Alexander Rakhlin and Ambuj Tewari},
title = {Optimal Strategies and Minimax Lower Bounds for Online Convex Games},
booktitle = {Proceedings of the 21st Annual Conference on Learning Theory},
pages = {414--424},
year = {2008},
publisher = {Omnipress},
url = {http://colt2008.cs.helsinki.fi/programme.shtml},
pdf = {research/abernethy08optimal.pdf}
}
@inproceedings{kakade08efficient,
author = {Sham M. Kakade and Shai Shalev-Shwartz and Ambuj Tewari},
title = {Efficient Bandit Algorithms for Online Multiclass Prediction},
booktitle = {Proceedings of the 25th International Conference on Machine Learning},
year = {2008},
pages = {440--447},
publisher = {ACM Press},
url = {http://doi.acm.org/10.1145/1390156.1390212},
pdf = {research/kakade08efficient.pdf}
}
@inproceedings{tewari08optimistic,
author = {Ambuj Tewari and Peter L. Bartlett},
title = {Optimistic Linear Programming gives Logarithmic Regret for Irreducible {MDPs}},
booktitle = {Advances in Neural Information Processing Systems 20},
year = {2008},
publisher = {MIT Press},
pages = {1505--1512},
url = {https://proceedings.neurips.cc/paper/2008},
pdf = {research/tewari08optimistic.pdf}
}
@inproceedings{tewari07bounded,
author = {Ambuj Tewari and Peter L. Bartlett},
title = {Bounded Parameter {M}arkov Decision Processes with Average Reward Criterion},
booktitle = {Proceedings of the 20th Annual Conference on Learning Theory},
year = {2007},
pages = {263--277},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {4539},
pdf = {research/tewari07bounded.pdf},
url = {http://dx.doi.org/10.1007/978-3-540-72927-3_20}
}
@article{tewari07consistency,
author = {Ambuj Tewari and Peter L. Bartlett},
title = {On the Consistency of Multiclass CLassification Methods},
journal = {Journal of Machine Learning Research},
year = {2007},
volume = {8},
month = {May},
pages = {1007--1025},
note = {(Invited paper)},
pdf = {research/tewari07consistency.pdf},
url = {http://jmlr.csail.mit.edu/papers/v8/tewari07a.html}
}
@article{bartlett07sparseness,
author = {Peter L. Bartlett and Ambuj Tewari},
title = {Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results},
journal = {Journal of Machine Learning Research},
year = {2007},
volume = {8},
month = {Apr},
pages = {775--790},
pdf = {research/bartlett07sparseness.pdf},
url = {http://jmlr.csail.mit.edu/papers/v8/bartlett07a.html}
}
@inproceedings{bartlett07sample,
author = {Peter L. Bartlett and Ambuj Tewari},
title = {Sample Complexity of Policy Search with Known Dynamics},
year = {2007},
pages = {97--104},
booktitle = {Advances in Neural Information Processing Systems 19},
publisher = {MIT Press},
pdf = {research/bartlett07sample.pdf},
url = {https://proceedings.neurips.cc/paper/2007}
}
@inproceedings{tewari05consistency,
author = {Ambuj Tewari and Peter L. Bartlett},
title = {On the Consistency of Multiclass Classification Methods},
booktitle = {Proceedings of the 18th Annual Conference on Learning Theory},
year = {2005},
pages = {147--153},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {3559},
note = {{\bf Student Paper Award}},
pdf = {research/tewari05consistency.pdf},
url = {http://dx.doi.org/10.1007/11503415_10}
}
@inproceedings{bartlett04sparseness,
author = {Peter L. Bartlett and Ambuj Tewari},
title = {Sparseness versus Estimating Conditional Probabilities: Some Asymptotic Results},
booktitle = {Proceedings of the 17th Annual Conference on Learning Theory},
year = {2004},
pages = {564--578},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {3120},
pdf = {research/bartlett04sparseness.pdf},
url = {https://doi.org/10.1007/978-3-540-27819-1_39}
}
@inproceedings{tewari02parallel,
author = {Ambuj Tewari and Utkarsh Srivastava and Phalguni Gupta},
title = {A Parallel {DFA} Minimization Algorithm},
booktitle = {Proceedings of the 9th International Conference on High Performance Computing},
year = {2002},
pages = {34--40},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {2552},
pdf = {research/tewari02parallel.pdf},
url = {https://doi.org/10.1007/3-540-36265-7_4}
}
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