Tewari, Ambuj

127 publications

ICML 2025 A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs Kihyuk Hong, Ambuj Tewari

ICLR 2025 A Theoretical Framework for Partially-Observed Reward States in RLHF Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari

ALT 2025 A Unified Theory of Supervised Online Learnability Vinod Raman, Unique Subedi, Ambuj Tewari

TMLR 2025 An Asymptotically Optimal Algorithm for the Convex Hull Membership Problem Gang Qiao, Ambuj Tewari

NeurIPS 2025 Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation Yash Patel, Eduardo Ochoa Rivera, Ambuj Tewari

TMLR 2025 Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators Unique Subedi, Ambuj Tewari

COLT 2025 Generation Through the Lens of Learning Theory Vinod Raman, Jiaxun Li, Ambuj Tewari

NeurIPS 2025 Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions Marc Brooks, Gabriel Durham, Kihyuk Hong, Ambuj Tewari

ICLRW 2025 In-Context Fine-Tuning for Neural Operators Yash Patel, Abhiti Mishra, Ambuj Tewari

AISTATS 2025 Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span Woojin Chae, Kihyuk Hong, Yufan Zhang, Ambuj Tewari, Dabeen Lee

ICML 2025 Leveraging Offline Data in Linear Latent Contextual Bandits Chinmaya Kausik, Kevin Tan, Ambuj Tewari

ICML 2025 On the Benefits of Active Data Collection in Operator Learning Unique Subedi, Ambuj Tewari

AISTATS 2025 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

L4DC 2025 The Complexity of Sequential Prediction in Dynamical Systems Vinod Raman, Unique Subedi, Ambuj Tewari

JMLR 2024 A Characterization of Multioutput Learnability Vinod Raman, Unique Subedi, Ambuj Tewari

ICML 2024 A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs Kihyuk Hong, Ambuj Tewari

AISTATS 2024 A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning Kihyuk Hong, Yuhang Li, Ambuj Tewari

ICMLW 2024 A Theoretical Framework for Partially Observed Reward-States in RLHF Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari

ICMLW 2024 A Theoretical Framework for Partially-Observed Reward States in RLHF Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari

COLT 2024 Apple Tasting: Combinatorial Dimensions and Minimax Rates Vinod Raman, Unique Subedi, Ananth Raman, Ambuj Tewari

AISTATS 2024 Conformal Contextual Robust Optimization Yash P. Patel, Sahana Rayan, Ambuj Tewari

ALT 2024 Multiclass Online Learnability Under Bandit Feedback Ananth Raman, Vinod Raman, Unique Subedi, Idan Mehalel, Ambuj Tewari

ICMLW 2024 Non-Parameteric Conformal Distributionally Robust Optimization Yash Patel, Guyang Cao, Ambuj Tewari

AISTATS 2024 Offline Policy Evaluation and Optimization Under Confounding Chinmaya Kausik, Yangyi Lu, Kevin Tan, Maggie Makar, Yixin Wang, Ambuj Tewari

NeurIPS 2024 On the Computational Complexity of Private High-Dimensional Model Selection Saptarshi Roy, Zehua Wang, Ambuj Tewari

NeurIPS 2024 Online Classification with Predictions Vinod Raman, Ambuj Tewari

ALT 2024 Online Infinite-Dimensional Regression: Learning Linear Operators Unique Subedi, Vinod Raman, Ambuj Tewari

COLT 2024 Online Learning with Set-Valued Feedback Vinod Raman, Unique Subedi, Ambuj Tewari

ICLR 2024 Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks Ziping Xu, Zifan Xu, Runxuan Jiang, Peter Stone, Ambuj Tewari

AISTATS 2024 Sequence Length Independent Norm-Based Generalization Bounds for Transformers Jacob Trauger, Ambuj Tewari

NeurIPS 2024 Smoothed Online Classification Can Be Harder than Batch Classification Vinod Raman, Unique Subedi, Ambuj Tewari

ICML 2024 Variational Inference with Coverage Guarantees in Simulation-Based Inference Yash Patel, Declan Mcnamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari

AISTATS 2023 An Optimization-Based Algorithm for Non-Stationary Kernel Bandits Without Prior Knowledge Kihyuk Hong, Yuhang Li, Ambuj Tewari

NeurIPSW 2023 Coupling Semi-Supervised Learning with Reinforcement Learning for Better Decision Making --- an Application to Cryo-EM Data Collection Ziping Xu, Quanfu Fan, Yilai Li, Emma Rose Lee, John Maxwell Cohn, Ambuj Tewari, Seychelle M Vos, Michael Cianfrocco

ICML 2023 Learning Mixtures of Markov Chains and MDPs Chinmaya Kausik, Kevin Tan, Ambuj Tewari

UAI 2023 Learning in Online MDPs: Is There a Price for Handling the Communicating Case? Gautam Chandrasekaran, Ambuj Tewari

COLT 2023 Multiclass Online Learning and Uniform Convergence Steve Hanneke, Shay Moran, Vinod Raman, Unique Subedi, Ambuj Tewari

NeurIPS 2023 On Proper Learnability Between Average- and Worst-Case Robustness Vinod Raman, Unique Subedi, Ambuj Tewari

NeurIPS 2023 On the Learnability of Multilabel Ranking Vinod Raman, Unique Subedi, Ambuj Tewari

ICML 2023 Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits Sunrit Chakraborty, Saptarshi Roy, Ambuj Tewari

AISTATS 2022 Weighted Gaussian Process Bandits for Non-Stationary Environments Yuntian Deng, Xingyu Zhou, Baekjin Kim, Ambuj Tewari, Abhishek Gupta, Ness Shroff

NeurIPS 2022 Adaptive Sampling for Discovery Ziping Xu, Eunjae Shim, Ambuj Tewari, Paul Zimmerman

UAI 2022 Balancing Adaptability and Non-Exploitability in Repeated Games Anthony DiGiovanni, Ambuj Tewari

CLeaR 2022 Efficient Reinforcement Learning with Prior Causal Knowledge Yangyi Lu, Amirhossein Meisami, Ambuj Tewari

ICML 2022 On the Statistical Benefits of Curriculum Learning Ziping Xu, Ambuj Tewari

NeurIPS 2022 Online Agnostic Multiclass Boosting Vinod Raman, Ambuj Tewari

NeurIPSW 2022 Probabilistically Robust PAC Learning Vinod Raman, Ambuj Tewari, Unique Subedi

AISTATS 2021 Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns Ziping Xu, Amirhossein Meisami, Ambuj Tewari

AISTATS 2021 Low-Rank Generalized Linear Bandit Problems Yangyi Lu, Amirhossein Meisami, Ambuj Tewari

NeurIPS 2021 Causal Bandits with Unknown Graph Structure Yangyi Lu, Amirhossein Meisami, Ambuj Tewari

NeurIPS 2021 Representation Learning Beyond Linear Prediction Functions Ziping Xu, Ambuj Tewari

UAI 2021 Thompson Sampling for Markov Games with Piecewise Stationary Opponent Policies Anthony DiGiovanni, Ambuj Tewari

UAI 2020 What You See May Not Be What You Get: UCB Bandit Algorithms Robust to $\varepsilon$-Contamination Laura Niss, Ambuj Tewari

UAI 2020 No-Regret Exploration in Contextual Reinforcement Learning Aditya Modi, Ambuj Tewari

NeurIPS 2020 On the Equivalence Between Online and Private Learnability Beyond Binary Classification Young Jung, Baekjin Kim, Ambuj Tewari

UAI 2020 Randomized Exploration for Non-Stationary Stochastic Linear Bandits Baekjin Kim, Ambuj Tewari

UAI 2020 Regret Analysis of Bandit Problems with Causal Background Knowledge Yangyi Lu, Amirhossein Meisami, Ambuj Tewari, William Yan

NeurIPS 2020 Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting Ziping Xu, Ambuj Tewari

AISTATS 2020 Sample Complexity of Reinforcement Learning Using Linearly Combined Model Ensembles Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder Singh

NeurIPS 2020 TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search Tarun Gogineni, Ziping Xu, Exequiel Punzalan, Runxuan Jiang, Joshua Kammeraad, Ambuj Tewari, Paul Zimmerman

ICMLW 2019 Contextual Markov Decision Processes Using Generalized Linear Models Aditya Modi, Ambuj Tewari

NeurIPS 2019 Generalization Bounds in the Predict-Then-Optimize Framework Othman El Balghiti, Adam N. Elmachtoub, Paul Grigas, Ambuj Tewari

NeurIPS 2019 On the Optimality of Perturbations in Stochastic and Adversarial Multi-Armed Bandit Problems Baekjin Kim, Ambuj Tewari

NeurIPS 2019 Online Learning via the Differential Privacy Lens Jacob D. Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari

AISTATS 2019 Online Multiclass Boosting with Bandit Feedback Daniel T. Zhang, Young Hun Jung, Ambuj Tewari

NeurIPS 2019 Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems Young Hun Jung, Ambuj Tewari

NeurIPS 2018 Active Learning for Non-Parametric Regression Using Purely Random Trees Jack Goetz, Ambuj Tewari, Paul Zimmerman

NeurIPS 2018 But How Does It Work in Theory? Linear SVM with Random Features Yitong Sun, Anna Gilbert, Ambuj Tewari

ALT 2018 Markov Decision Processes with Continuous Side Information Aditya Modi, Nan Jiang, Satinder Singh, Ambuj Tewari

AISTATS 2018 Online Boosting Algorithms for Multi-Label Ranking Young Hun Jung, Ambuj Tewari

NeurIPS 2017 Action Centered Contextual Bandits Kristjan Greenewald, Ambuj Tewari, Susan Murphy, Predag Klasnja

JMLR 2017 Online Learning to Rank with Top-K Feedback Sougata Chaudhuri, Ambuj Tewari

NeurIPS 2017 Online Multiclass Boosting Young Hun Jung, Jack Goetz, Ambuj Tewari

AAAI 2016 Handling Class Imbalance in Link Prediction Using Learning to Rank Techniques Bopeng Li, Sougata Chaudhuri, Ambuj Tewari

ICML 2016 Mixture Proportion Estimation via Kernel Embeddings of Distributions Harish Ramaswamy, Clayton Scott, Ambuj Tewari

IJCAI 2016 On Structural Properties of MDPs That Bound Loss Due to Shallow Planning Nan Jiang, Satinder Singh, Ambuj Tewari

AISTATS 2016 Online Learning to Rank with Feedback at the Top Sougata Chaudhuri, Ambuj Tewari

NeurIPS 2016 Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games Sougata Chaudhuri, Ambuj Tewari

NeurIPS 2015 Alternating Minimization for Regression Problems with Vector-Valued Outputs Prateek Jain, Ambuj Tewari

ICML 2015 Convex Calibrated Surrogates for Hierarchical Classification Harish Ramaswamy, Ambuj Tewari, Shivani Agarwal

NeurIPS 2015 Fighting Bandits with a New Kind of Smoothness Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari

ICML 2015 Generalization Error Bounds for Learning to Rank: Does the Length of Document Lists Matter? Ambuj Tewari, Sougata Chaudhuri

JMLR 2015 Online Learning via Sequential Complexities Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari

AISTATS 2015 Online Ranking with Top-1 Feedback Sougata Chaudhuri, Ambuj Tewari

NeurIPS 2015 Predtron: A Family of Online Algorithms for General Prediction Problems Prateek Jain, Nagarajan Natarajan, Ambuj Tewari

NeurIPS 2014 On Iterative Hard Thresholding Methods for High-Dimensional M-Estimation Prateek Jain, Ambuj Tewari, Purushottam Kar

COLT 2014 Online Linear Optimization via Smoothing Jacob D. Abernethy, Chansoo Lee, Abhinav Sinha, Ambuj Tewari

JMLR 2014 Prediction and Clustering in Signed Networks: A Local to Global Perspective Kai-Yang Chiang, Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon, Ambuj Tewari

NeurIPS 2013 Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses Harish G. Ramaswamy, Shivani Agarwal, Ambuj Tewari

NeurIPS 2013 Learning with Noisy Labels Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, Ambuj Tewari

IJCAI 2013 On Robust Estimation of High Dimensional Generalized Linear Models Eunho Yang, Ambuj Tewari, Pradeep Ravikumar

UAI 2012 Deterministic MDPs with Adversarial Rewards and Bandit Feedback Raman Arora, Ofer Dekel, Ambuj Tewari

NeurIPS 2012 Feature Clustering for Accelerating Parallel Coordinate Descent Chad Scherrer, Ambuj Tewari, Mahantesh Halappanavar, David Haglin

ICML 2012 Online Bandit Learning Against an Adaptive Adversary: From Regret to Policy Regret Ofer Dekel, Ambuj Tewari, Raman Arora

ICML 2012 PAC Subset Selection in Stochastic Multi-Armed Bandits Shivaram Kalyanakrishnan, Ambuj Tewari, Peter Auer, Peter Stone

AISTATS 2012 Perturbation Based Large Margin Approach for Ranking Eunho Yang, Ambuj Tewari, Pradeep Ravikumar

JMLR 2012 Regularization Techniques for Learning with Matrices Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari

ICML 2012 Scaling up Coordinate Descent Algorithms for Large ℓ1 Regularization Problems Chad Scherrer, Mahantesh Halappanavar, Ambuj Tewari, David Haglin

COLT 2011 Complexity-Based Approach to Calibration with Checking Rules Dean P. Foster, Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari

NeurIPS 2011 Greedy Algorithms for Structurally Constrained High Dimensional Problems Ambuj Tewari, Pradeep K. Ravikumar, Inderjit S. Dhillon

AISTATS 2011 Improved Regret Guarantees for Online Smooth Convex Optimization with Bandit Feedback Ankan Saha, Ambuj Tewari

NeurIPS 2011 Nearest Neighbor Based Greedy Coordinate Descent Inderjit S. Dhillon, Pradeep K. Ravikumar, Ambuj Tewari

AISTATS 2011 On NDCG Consistency of Listwise Ranking Methods Pradeep Ravikumar, Ambuj Tewari, Eunho Yang

NeurIPS 2011 On the Universality of Online Mirror Descent Nati Srebro, Karthik Sridharan, Ambuj Tewari

COLT 2011 Online Learning: Beyond Regret Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari

NeurIPS 2011 Online Learning: Stochastic, Constrained, and Smoothed Adversaries Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari

NeurIPS 2011 Orthogonal Matching Pursuit with Replacement Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon

JMLR 2011 Stochastic Methods for L1-Regularized Loss Minimization Shai Shalev-Shwartz, Ambuj Tewari

COLT 2010 Composite Objective Mirror Descent John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Ambuj Tewari

COLT 2010 Convex Games in Banach Spaces Karthik Sridharan, Ambuj Tewari

AISTATS 2010 Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity Sham Kakade, Ohad Shamir, Karthik Sindharan, Ambuj Tewari

NeurIPS 2010 Online Learning: Random Averages, Combinatorial Parameters, and Learnability Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari

NeurIPS 2010 Smoothness, Low Noise and Fast Rates Nathan Srebro, Karthik Sridharan, Ambuj Tewari

UAI 2009 REGAL: A Regularization Based Algorithm for Reinforcement Learning in Weakly Communicating MDPs Peter L. Bartlett, Ambuj Tewari

ICML 2009 Stochastic Methods for L1 Regularized Loss Minimization Shai Shalev-Shwartz, Ambuj Tewari

ICML 2008 Efficient Bandit Algorithms for Online Multiclass Prediction Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari

COLT 2008 High-Probability Regret Bounds for Bandit Online Linear Optimization Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham M. Kakade, Alexander Rakhlin, Ambuj Tewari

NeurIPS 2008 On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization Sham M. Kakade, Karthik Sridharan, Ambuj Tewari

NeurIPS 2008 On the Generalization Ability of Online Strongly Convex Programming Algorithms Sham M. Kakade, Ambuj Tewari

COLT 2008 Optimal Stragies and Minimax Lower Bounds for Online Convex Games Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari

COLT 2007 Bounded Parameter Markov Decision Processes with Average Reward Criterion Ambuj Tewari, Peter L. Bartlett

JMLR 2007 On the Consistency of Multiclass Classification Methods Ambuj Tewari, Peter L. Bartlett

NeurIPS 2007 Optimistic Linear Programming Gives Logarithmic Regret for Irreducible MDPs Ambuj Tewari, Peter L. Bartlett

JMLR 2007 Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results Peter L. Bartlett, Ambuj Tewari

NeurIPS 2006 Sample Complexity of Policy Search with Known Dynamics Peter L. Bartlett, Ambuj Tewari

COLT 2005 On the Consistency of Multiclass Classification Methods Ambuj Tewari, Peter L. Bartlett

COLT 2004 Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results Peter L. Bartlett, Ambuj Tewari