| 2026 | COLT | A Characterization of List Language Identification in the Limit. | Moses Charikar, Chirag Pabbaraju, Ambuj Tewari |
| 2025 | AISTATS | Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span. | Woojin Chae, Kihyuk Hong, Yufan Zhang, Ambuj Tewari, Dabeen Lee |
| 2025 | AISTATS | Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs via Approximation by Discounted-Reward MDPs. | Kihyuk Hong, Woojin Chae, Yufan Zhang, Dabeen Lee, Ambuj Tewari |
| 2025 | ALT | A Unified Theory of Supervised Online Learnability. | Vinod Raman, Unique Subedi, Ambuj Tewari |
| 2025 | COLT | Generation through the lens of learning theory. | Vinod Raman, Jiaxun Li, Ambuj Tewari |
| 2025 | ICLR | A Theoretical Framework for Partially-Observed Reward States in RLHF. | Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari |
| 2025 | ICML | A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs. | Kihyuk Hong, Ambuj Tewari |
| 2025 | ICML | Leveraging Offline Data in Linear Latent Contextual Bandits. | Chinmaya Kausik, Kevin Tan, Ambuj Tewari |
| 2025 | ICML | On the Benefits of Active Data Collection in Operator Learning. | Unique Subedi, Ambuj Tewari |
| 2024 | AISTATS | A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning. | Kihyuk Hong, Yuhang Li, Ambuj Tewari |
| 2024 | AISTATS | Offline Policy Evaluation and Optimization Under Confounding. | Chinmaya Kausik, Yangyi Lu, Kevin Tan, Maggie Makar, Yixin Wang, Ambuj Tewari |
| 2024 | AISTATS | Conformal Contextual Robust Optimization. | Yash P. Patel, Sahana Rayan, Ambuj Tewari |
| 2024 | AISTATS | Sequence Length Independent Norm-Based Generalization Bounds for Transformers. | Jacob Trauger, Ambuj Tewari |
| 2024 | ALT | Multiclass Online Learnability under Bandit Feedback. | Ananth Raman, Vinod Raman, Unique Subedi, Idan Mehalel, Ambuj Tewari |
| 2024 | ALT | Online Infinite-Dimensional Regression: Learning Linear Operators. | Unique Subedi, Vinod Raman, Ambuj Tewari |
| 2024 | COLT | Apple Tasting: Combinatorial Dimensions and Minimax Rates. | Vinod Raman, Unique Subedi, Ananth Raman, Ambuj Tewari |
| 2024 | COLT | Online Learning with Set-valued Feedback. | Vinod Raman, Unique Subedi, Ambuj Tewari |
| 2024 | ICLR | Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks. | Ziping Xu, Zifan Xu, Runxuan Jiang, Peter Stone, Ambuj Tewari |
| 2024 | ICML | A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs. | Kihyuk Hong, Ambuj Tewari |
| 2024 | ICML | Variational Inference with Coverage Guarantees in Simulation-Based Inference. | Yash P. Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari |
| 2023 | AISTATS | An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge. | Kihyuk Hong, Yuhang Li, Ambuj Tewari |
| 2023 | COLT | Multiclass Online Learning and Uniform Convergence. | Steve Hanneke, Shay Moran, Vinod Raman, Unique Subedi, Ambuj Tewari |
| 2023 | ICML | Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits. | Sunrit Chakraborty, Saptarshi Roy, Ambuj Tewari |
| 2023 | ICML | Learning Mixtures of Markov Chains and MDPs. | Chinmaya Kausik, Kevin Tan, Ambuj Tewari |
| 2023 | UAI | Learning in online MDPs: is there a price for handling the communicating case? | Gautam Chandrasekaran, Ambuj Tewari |
| 2022 | AISTATS | Weighted Gaussian Process Bandits for Non-stationary Environments. | Yuntian Deng, Xingyu Zhou, Baekjin Kim, Ambuj Tewari, Abhishek Gupta, Ness B. Shroff |
| 2022 | ICML | On the Statistical Benefits of Curriculum Learning. | Ziping Xu, Ambuj Tewari |
| 2022 | UAI | Balancing adaptability and non-exploitability in repeated games. | Anthony DiGiovanni, Ambuj Tewari |
| 2021 | AISTATS | Low-Rank Generalized Linear Bandit Problems. | Yangyi Lu, Amirhossein Meisami, Ambuj Tewari |
| 2021 | AISTATS | Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns. | Ziping Xu, Amirhossein Meisami, Ambuj Tewari |
| 2021 | UAI | Thompson sampling for Markov games with piecewise stationary opponent policies. | Anthony DiGiovanni, Ambuj Tewari |
| 2020 | AISTATS | Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles. | Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder Singh |
| 2020 | UAI | No-regret Exploration in Contextual Reinforcement Learning. | Aditya Modi, Ambuj Tewari |
| 2020 | UAI | Randomized Exploration for Non-Stationary Stochastic Linear Bandits. | Baekjin Kim, Ambuj Tewari |
| 2020 | UAI | Regret Analysis of Bandit Problems with Causal Background Knowledge. | Yangyi Lu, Amirhossein Meisami, Ambuj Tewari, William Yan |
| 2020 | UAI | What You See May Not Be What You Get: UCB Bandit Algorithms Robust to ε-Contamination. | Laura Niss, Ambuj Tewari |
| 2019 | AISTATS | Online Multiclass Boosting with Bandit Feedback. | Daniel T. Zhang, Young Hun Jung, Ambuj Tewari |
| 2018 | AISTATS | Online Boosting Algorithms for Multi-label Ranking. | Young Hun Jung, Ambuj Tewari |
| 2018 | ALT | Markov Decision Processes with Continuous Side Information. | Aditya Modi, Nan Jiang, Satinder Singh, Ambuj Tewari |
| 2016 | AAAI | Handling Class Imbalance in Link Prediction Using Learning to Rank Techniques. | Bopeng Li, Sougata Chaudhuri, Ambuj Tewari |
| 2016 | AISTATS | Online Learning to Rank with Feedback at the Top. | Sougata Chaudhuri, Ambuj Tewari |
| 2016 | ICML | Mixture Proportion Estimation via Kernel Embeddings of Distributions. | Harish G. Ramaswamy, Clayton Scott, Ambuj Tewari |
| 2016 | IJCAI | On Structural Properties of MDPs that Bound Loss Due to Shallow Planning. | Nan Jiang, Satinder Singh, Ambuj Tewari |
| 2015 | AISTATS | Online Ranking with Top-1 Feedback. | Sougata Chaudhuri, Ambuj Tewari |
| 2015 | ICML | Convex Calibrated Surrogates for Hierarchical Classification. | Harish G. Ramaswamy, Ambuj Tewari, Shivani Agarwal |
| 2015 | ICML | Generalization error bounds for learning to rank: Does the length of document lists matter? | Ambuj Tewari, Sougata Chaudhuri |
| 2014 | COLT | Online Linear Optimization via Smoothing. | Jacob D. Abernethy, Chansoo Lee, Abhinav Sinha, Ambuj Tewari |
| 2013 | IJCAI | On Robust Estimation of High Dimensional Generalized Linear Models. | Eunho Yang, Ambuj Tewari, Pradeep Ravikumar |
| 2012 | ICML | Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret. | Ofer Dekel, Ambuj Tewari, Raman Arora |
| 2012 | ICML | PAC Subset Selection in Stochastic Multi-armed Bandits. | Shivaram Kalyanakrishnan, Ambuj Tewari, Peter Auer, Peter Stone |
| 2012 | ICML | Scaling Up Coordinate Descent Algorithms for Large | Chad Scherrer, Mahantesh Halappanavar, Ambuj Tewari, David Haglin |
| 2012 | SIGIR | Parallelizing ListNet training using spark. | Shilpa Shukla, Matthew Lease, Ambuj Tewari |
| 2012 | UAI | Deterministic MDPs with Adversarial Rewards and Bandit Feedback. | Raman Arora, Ofer Dekel, Ambuj Tewari |
| 2011 | CIKM | Exploiting longer cycles for link prediction in signed networks. | Kai-Yang Chiang, Nagarajan Natarajan, Ambuj Tewari, Inderjit S. Dhillon |
| 2010 | COLT | Composite Objective Mirror Descent. | John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Ambuj Tewari |
| 2010 | COLT | Convex Games in Banach Spaces. | Karthik Sridharan, Ambuj Tewari |
| 2009 | ICML | Stochastic methods for | Shai Shalev-Shwartz, Ambuj Tewari |
| 2009 | UAI | REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs. | Peter L. Bartlett, Ambuj Tewari |
| 2008 | COLT | Optimal Stragies and Minimax Lower Bounds for Online Convex Games. | Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari |
| 2008 | COLT | High-Probability Regret Bounds for Bandit Online Linear Optimization. | Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham M. Kakade, Alexander Rakhlin, Ambuj Tewari |
| 2008 | ICML | Efficient bandit algorithms for online multiclass prediction. | Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari |
| 2007 | COLT | Bounded Parameter Markov Decision Processes with Average Reward Criterion. | Ambuj Tewari, Peter L. Bartlett |
| 2005 | COLT | On the Consistency of Multiclass Classification Methods. | Ambuj Tewari, Peter L. Bartlett |
| 2004 | COLT | Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results. | Peter L. Bartlett, Ambuj Tewari |
| 2002 | HiPC | A Parallel DFA Minimization Algorithm. | Ambuj Tewari, Utkarsh Srivastava, P. Gupta |