A Parallel DFA Minimization Algorithm. - Ambuj Tewari; Utkarsh Srivastava; P. Gupta (HiPC, 2002)

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

Sample Complexity of Policy Search with Known Dynamics. - Peter L. Bartlett; Ambuj Tewari (NIPS, 2006)

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

Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs. - Ambuj Tewari; Peter L. Bartlett (NIPS, 2007)

Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results. - Peter L. Bartlett; Ambuj Tewari (J. Mach. Learn. Res., 2007)

On the Consistency of Multiclass Classification Methods. - Ambuj Tewari; Peter L. Bartlett (J. Mach. Learn. Res., 2007)

Optimal Stragies and Minimax Lower Bounds for Online Convex Games. - Jacob D. Abernethy; Peter L. Bartlett; Alexander Rakhlin; 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 (COLT, 2008)

Efficient bandit algorithms for online multiclass prediction. - Sham M. Kakade; Shai Shalev-Shwartz; Ambuj Tewari (ICML, 2008)

On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization. - Sham M. Kakade; Karthik Sridharan; Ambuj Tewari (NIPS, 2008)

On the Generalization Ability of Online Strongly Convex Programming Algorithms. - Sham M. Kakade; Ambuj Tewari (NIPS, 2008)

REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs. - Peter L. Bartlett; Ambuj Tewari (UAI, 2009)

Applications of strong convexity–strong smoothness duality to learning with matrices - Sham M. Kakade; Shai Shalev-Shwartz; Ambuj Tewari (CoRR, 2009)

Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity - Sham M. Kakade; Ohad Shamir; Karthik Sridharan; Ambuj Tewari (CoRR, 2009)

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 (COLT, 2010)

Online Learning: Random Averages, Combinatorial Parameters, and Learnability. - Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (NIPS, 2010)

Smoothness, Low Noise and Fast Rates. - Nathan Srebro; Karthik Sridharan; Ambuj Tewari (NIPS, 2010)

On the Finite Time Convergence of Cyclic Coordinate Descent Methods - Ankan Saha; Ambuj Tewari (CoRR, 2010)

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

Exploiting longer cycles for link prediction in signed networks. - Kai-Yang Chiang; Nagarajan Natarajan; Ambuj Tewari; Inderjit S. Dhillon (CIKM, 2011)

Nearest Neighbor based Greedy Coordinate Descent. - Inderjit S. Dhillon; Pradeep Ravikumar; Ambuj Tewari (NIPS, 2011)

Orthogonal Matching Pursuit with Replacement. - Prateek Jain 002; Ambuj Tewari; Inderjit S. Dhillon (NIPS, 2011)

Online Learning: Stochastic, Constrained, and Smoothed Adversaries. - Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (NIPS, 2011)

On the Universality of Online Mirror Descent. - Nati Srebro; Karthik Sridharan; Ambuj Tewari (NIPS, 2011)

Greedy Algorithms for Structurally Constrained High Dimensional Problems. - Ambuj Tewari; Pradeep Ravikumar; Inderjit S. Dhillon (NIPS, 2011)

Online Learning: Stochastic and Constrained Adversaries - Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (CoRR, 2011)

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

Online Learning: Beyond Regret. - Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (COLT, 2011)

On NDCG Consistency of Listwise Ranking Methods. - Pradeep Ravikumar; Ambuj Tewari; Eunho Yang (AISTATS, 2011)

Improved Regret Guarantees for Online Smooth Convex Optimization with Bandit Feedback. - Ankan Saha; Ambuj Tewari (AISTATS, 2011)

Stochastic Methods for l1-regularized Loss Minimization. - Shai Shalev-Shwartz; Ambuj Tewari (J. Mach. Learn. Res., 2011)

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 001 (ICML, 2012)

Scaling Up Coordinate Descent Algorithms for Large ℓ1 Regularization Problems. - Chad Scherrer; Mahantesh Halappanavar; Ambuj Tewari; David Haglin (ICML, 2012)

Feature Clustering for Accelerating Parallel Coordinate Descent. - Chad Scherrer; Ambuj Tewari; Mahantesh Halappanavar; David Haglin (NIPS, 2012)

Parallelizing ListNet training using spark. - Shilpa Shukla; Matthew Lease; Ambuj Tewari (SIGIR, 2012)

Deterministic MDPs with Adversarial Rewards and Bandit Feedback. - Raman Arora; Ofer Dekel; Ambuj Tewari (UAI, 2012)

The Interplay Between Stability and Regret in Online Learning - Ankan Saha; Prateek Jain 002; Ambuj Tewari (CoRR, 2012)

Regularization Techniques for Learning with Matrices. - Sham M. Kakade; Shai Shalev-Shwartz; Ambuj Tewari (J. Mach. Learn. Res., 2012)

Perturbation based Large Margin Approach for Ranking. - Eunho Yang; Ambuj Tewari; Pradeep Ravikumar (AISTATS, 2012)

On Robust Estimation of High Dimensional Generalized Linear Models. - Eunho Yang; Ambuj Tewari; Pradeep Ravikumar (IJCAI, 2013)

Learning with Noisy Labels. - Nagarajan Natarajan; Inderjit S. Dhillon; Pradeep Ravikumar; Ambuj Tewari (NIPS, 2013)

Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses. - Harish G. Ramaswamy; Shivani Agarwal 001; Ambuj Tewari (NIPS, 2013)

On the Nonasymptotic Convergence of Cyclic Coordinate Descent Methods. - Ankan Saha; Ambuj Tewari (SIAM J. Optim., 2013)

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

On Iterative Hard Thresholding Methods for High-dimensional M-Estimation. - Prateek Jain 002; Ambuj Tewari; Purushottam Kar (NIPS, 2014)

Perceptron-like Algorithms and Generalization Bounds for Learning to Rank. - Sougata Chaudhuri; Ambuj Tewari (CoRR, 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 (J. Mach. Learn. Res., 2014)

Online Ranking with Top-1 Feedback. - Sougata Chaudhuri; Ambuj Tewari (AISTATS, 2015)

Convex Calibrated Surrogates for Hierarchical Classification. - Harish G. Ramaswamy; Ambuj Tewari; Shivani Agarwal 001 (ICML, 2015)

Generalization error bounds for learning to rank: Does the length of document lists matter? - Ambuj Tewari; Sougata Chaudhuri (ICML, 2015)

Fighting Bandits with a New Kind of Smoothness. - Jacob D. Abernethy; Chansoo Lee; Ambuj Tewari (NIPS, 2015)

Predtron: A Family of Online Algorithms for General Prediction Problems. - Prateek Jain 002; Nagarajan Natarajan; Ambuj Tewari (NIPS, 2015)

Alternating Minimization for Regression Problems with Vector-valued Outputs. - Prateek Jain 002; Ambuj Tewari (NIPS, 2015)

Perceptron like Algorithms for Online Learning to Rank. - Sougata Chaudhuri; Ambuj Tewari (CoRR, 2015)

Consistent Algorithms for Multiclass Classification with a Reject Option. - Harish G. Ramaswamy; Ambuj Tewari; Shivani Agarwal 001 (CoRR, 2015)

Online learning via sequential complexities. - Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (J. Mach. Learn. Res., 2015)

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

Online Learning to Rank with Feedback at the Top. - Sougata Chaudhuri; Ambuj Tewari (AISTATS, 2016)

Mixture Proportion Estimation via Kernel Embeddings of Distributions. - Harish G. Ramaswamy; Clayton Scott; Ambuj Tewari (ICML, 2016)

On Structural Properties of MDPs that Bound Loss Due to Shallow Planning. - Nan Jiang 008; Satinder Singh 001; Ambuj Tewari (IJCAI, 2016)

Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games. - Sougata Chaudhuri; Ambuj Tewari (NIPS, 2016)

Mixture Proportion Estimation via Kernel Embedding of Distributions. - Harish G. Ramaswamy; Clayton Scott; Ambuj Tewari (CoRR, 2016)

Regularized Estimation in High Dimensional Time Series under Mixing Conditions. - Kam Chung Wong; Ambuj Tewari; Zifan Li (CoRR, 2016)

From Ads to Interventions: Contextual Bandits in Mobile Health. - Ambuj Tewari; Susan A. Murphy (Mobile Health - Sensors, Analytic Methods, and Applications, 2017)

Action Centered Contextual Bandits. - Kristjan H. Greenewald; Ambuj Tewari; Susan A. Murphy; Predrag V. Klasnja (NIPS, 2017)

Online multiclass boosting. - Young Hun Jung; Jack Goetz; Ambuj Tewari (NIPS, 2017)

Online Multiclass Boosting. - Young Hun Jung; Ambuj Tewari (CoRR, 2017)

An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions. - Huitian Lei; Ambuj Tewari; Susan A. Murphy (CoRR, 2017)

Finite Time Analysis of Optimal Adaptive Policies for Linear-Quadratic Systems. - Mohamad Kazem Shirani Faradonbeh; Ambuj Tewari; George Michailidis (CoRR, 2017)

Online Learning via Differential Privacy. - Jacob D. Abernethy; Chansoo Lee; Audra McMillan; Ambuj Tewari (CoRR, 2017)

Online Learning to Rank with Top-k Feedback. - Sougata Chaudhuri; Ambuj Tewari (J. Mach. Learn. Res., 2017)

Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits. - Zifan Li; Ambuj Tewari (J. Mach. Learn. Res., 2017)

Cost-Sensitive Learning with Noisy Labels. - Nagarajan Natarajan; Inderjit S. Dhillon; Pradeep Ravikumar; Ambuj Tewari (J. Mach. Learn. Res., 2017)

Optimality of Fast-Matching Algorithms for Random Networks With Applications to Structural Controllability. - Mohamad Kazem Shirani Faradonbeh; Ambuj Tewari; George Michailidis (IEEE Trans. Control. Netw. Syst., 2017)

Partial Hard Thresholding. - Prateek Jain 002; Ambuj Tewari; Inderjit S. Dhillon (IEEE Trans. Inf. Theory, 2017)

Online Boosting Algorithms for Multi-label Ranking. - Young Hun Jung; Ambuj Tewari (AISTATS, 2018)

Markov Decision Processes with Continuous Side Information. - Aditya Modi 002; Nan Jiang 008; Satinder Singh 001; Ambuj Tewari (ALT, 2018)

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

But How Does It Work in Theory? Linear SVM with Random Features. - Yitong Sun; Anna C. Gilbert; Ambuj Tewari (NeurIPS, 2018)

Finite time identification in unstable linear systems. - Mohamad Kazem Shirani Faradonbeh; Ambuj Tewari; George Michailidis (Autom., 2018)

On Optimality of Adaptive Linear-Quadratic Regulators. - Mohamad Kazem Shirani Faradonbeh; Ambuj Tewari; George Michailidis (CoRR, 2018)

Finite Time Adaptive Stabilization of LQ Systems. - Mohamad Kazem Shirani Faradonbeh; Ambuj Tewari; George Michailidis (CoRR, 2018)

Random ReLU Features: Universality, Approximation, and Composition. - Yitong Sun; Anna C. Gilbert; Ambuj Tewari (CoRR, 2018)

Input Perturbations for Adaptive Regulation and Learning. - Mohamad Kazem Shirani Faradonbeh; Ambuj Tewari; George Michailidis (CoRR, 2018)

Sampled fictitious play is Hannan consistent. - Zifan Li; Ambuj Tewari (Games Econ. Behav., 2018)

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

On Applications of Bootstrap in Continuous Space Reinforcement Learning. - Mohamad Kazem Shirani Faradonbeh; Ambuj Tewari; George Michailidis (CDC, 2019)

Randomized Algorithms for Data-Driven Stabilization of Stochastic Linear Systems. - Mohamad Kazem Shirani Faradonbeh; Ambuj Tewari; George Michailidis (DSW, 2019)

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

Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems. - Young Hun Jung; Ambuj Tewari (NeurIPS, 2019)

On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems. - Baekjin Kim; Ambuj Tewari (NeurIPS, 2019)

Contextual Markov Decision Processes using Generalized Linear Models. - Aditya Modi 002; Ambuj Tewari (CoRR, 2019)

Regret Bounds for Thompson Sampling in Restless Bandit Problems. - Young Hun Jung; Ambuj Tewari (CoRR, 2019)

Regret Analysis of Causal Bandit Problems. - Yangyi Lu; Amirhossein Meisami; Ambuj Tewari; Zhenyu Yan 001 (CoRR, 2019)

Not All are Made Equal: Consistency of Weighted Averaging Estimators Under Active Learning. - Jack Goetz; Ambuj Tewari (CoRR, 2019)

Thompson Sampling in Non-Episodic Restless Bandits. - Young Hun Jung; Marc Abeille; Ambuj Tewari (CoRR, 2019)

Online Boosting for Multilabel Ranking with Top-k Feedback. - Daniel T. Zhang; Young Hun Jung; Ambuj Tewari (CoRR, 2019)

Near-optimal Oracle-efficient Algorithms for Stationary and Non-Stationary Stochastic Linear Bandits. - Baekjin Kim; Ambuj Tewari (CoRR, 2019)

Learning To Predict Reaction Conditions: Relationships between Solvent, Molecular Structure, and Catalyst. - Eric Walker; Joshua Kammeraad; Jonathan Goetz; Michael T. Robo; Ambuj Tewari; Paul M. Zimmerman (J. Chem. Inf. Model., 2019)

Finite-Time Adaptive Stabilization of Linear Systems. - Mohamad Kazem Shirani Faradonbeh; Ambuj Tewari; George Michailidis (IEEE Trans. Autom. Control., 2019)

Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles. - Aditya Modi 002; Nan Jiang 008; Ambuj Tewari; Satinder Singh 001 (AISTATS, 2020)

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

On the Equivalence between Online and Private Learnability beyond Binary Classification. - Young Hun Jung; Baekjin Kim; Ambuj Tewari (NeurIPS, 2020)

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

No-regret Exploration in Contextual Reinforcement Learning. - Aditya Modi 002; 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 (UAI, 2020)

What You See May Not Be What You Get: UCB Bandit Algorithms Robust to ε-Contamination. - Laura Niss; Ambuj Tewari (UAI, 2020)

Input perturbations for adaptive control and learning. - Mohamad Kazem Shirani Faradonbeh; Ambuj Tewari; George Michailidis (Autom., 2020)

On adaptive Linear-Quadratic regulators. - Mohamad Kazem Shirani Faradonbeh; Ambuj Tewari; George Michailidis (Autom., 2020)

Near-optimal Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms for the Non-episodic Setting. - Ziping Xu; Ambuj Tewari (CoRR, 2020)

On Learnability under General Stochastic Processes. - A. Philip Dawid; Ambuj Tewari (CoRR, 2020)

Federated Learning via Synthetic Data. - Jack Goetz; Ambuj Tewari (CoRR, 2020)

What Does the Machine Learn? Knowledge Representations of Chemical Reactivity. - Joshua Kammeraad; Jack Goetz; Eric Walker; Ambuj Tewari; Paul M. Zimmerman (J. Chem. Inf. Model., 2020)

Low-Rank Generalized Linear Bandit Problems. - Yangyi Lu; Amirhossein Meisami; Ambuj Tewari (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)

Causal Bandits with Unknown Graph Structure. - Yangyi Lu; Amirhossein Meisami; Ambuj Tewari (NeurIPS, 2021)

Representation Learning Beyond Linear Prediction Functions. - Ziping Xu; Ambuj Tewari (NeurIPS, 2021)

Thompson sampling for Markov games with piecewise stationary opponent policies. - Anthony DiGiovanni; Ambuj Tewari (UAI, 2021)

Causal Markov Decision Processes: Learning Good Interventions Efficiently. - Yangyi Lu; Amirhossein Meisami; Ambuj Tewari (CoRR, 2021)

Bandit Algorithms for Precision Medicine. - Yangyi Lu; Ziping Xu; Ambuj Tewari (CoRR, 2021)

Online Learning in Adversarial MDPs: Is the Communicating Case Harder than Ergodic? - Gautam Chandrasekaran; Ambuj Tewari (CoRR, 2021)

Optimism-Based Adaptive Regulation of Linear-Quadratic Systems. - Mohamad Kazem Shirani Faradonbeh; Ambuj Tewari; George Michailidis (IEEE Trans. Autom. Control., 2021)

Weighted Gaussian Process Bandits for Non-stationary Environments. - Yuntian Deng; Xingyu Zhou 001; Baekjin Kim; Ambuj Tewari; Abhishek Gupta 002; Ness B. Shroff (AISTATS, 2022)

Efficient Reinforcement Learning with Prior Causal Knowledge. - Yangyi Lu; Amirhossein Meisami; Ambuj Tewari (CLeaR, 2022)

On the Statistical Benefits of Curriculum Learning. - Ziping Xu; Ambuj Tewari (ICML, 2022)

Online Agnostic Multiclass Boosting. - Vinod Raman; Ambuj Tewari (NeurIPS, 2022)

Adaptive Sampling for Discovery. - Ziping Xu; Eunjae Shim; Ambuj Tewari; Paul M. Zimmerman (NeurIPS, 2022)

Balancing adaptability and non-exploitability in repeated games. - Anthony DiGiovanni; Ambuj Tewari (UAI, 2022)

Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms. - Laura Niss; Yuekai Sun; Ambuj Tewari (CoRR, 2022)

Adaptive Learning for Discovery. - Ziping Xu; Eunjae Shim; Ambuj Tewari; Paul M. Zimmerman (CoRR, 2022)

Probabilistically Robust PAC Learning. - Vinod Raman; Unique Subedi; Ambuj Tewari (CoRR, 2022)

Offline Policy Evaluation and Optimization under Confounding. - Kevin Tan; Yangyi Lu; Chinmaya Kausik; Yixin Wang; Ambuj Tewari (CoRR, 2022)

Conformer-RL: A deep reinforcement learning library for conformer generation. - Runxuan Jiang; Tarun Gogineni; Joshua Kammeraad; Yifei He; Ambuj Tewari; Paul M. Zimmerman (J. Comput. Chem., 2022)

An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge. - Kihyuk Hong; Yuhang Li; Ambuj Tewari (AISTATS, 2023)

Multiclass Online Learning and Uniform Convergence. - Steve Hanneke; Shay Moran; Vinod Raman; Unique Subedi; Ambuj Tewari (COLT, 2023)

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

Learning Mixtures of Markov Chains and MDPs. - Chinmaya Kausik; Kevin Tan; Ambuj Tewari (ICML, 2023)

On the Learnability of Multilabel Ranking. - 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)

Learning in online MDPs: is there a price for handling the communicating case? - Gautam Chandrasekaran; Ambuj Tewari (UAI, 2023)

A Characterization of Multilabel Learnability. - Vinod Raman; Unique Subedi; Ambuj Tewari (CoRR, 2023)

An Asymptotically Optimal Algorithm for the One-Dimensional Convex Hull Feasibility Problem. - Gang Qiao; Ambuj Tewari (CoRR, 2023)

Quantum Learning Theory Beyond Batch Binary Classification. - Preetham Mohan; Ambuj Tewari (CoRR, 2023)

A Characterization of Online Multiclass Learnability. - Vinod Raman; Unique Subedi; Ambuj Tewari (CoRR, 2023)

Variational Inference with Coverage Guarantees. - Yash P. Patel; Declan McNamara; Jackson Loper; Jeffrey Regier; Ambuj Tewari (CoRR, 2023)

A Combinatorial Characterization of Online Learning Games with Bounded Losses. - Vinod Raman; Unique Subedi; Ambuj Tewari (CoRR, 2023)

Multiclass Online Learnability under Bandit Feedback. - Ananth Raman; Vinod Raman; Unique Subedi; Ambuj Tewari (CoRR, 2023)

On the Minimax Regret in Online Ranking with Top-k Feedback. - Mingyuan Zhang; Ambuj Tewari (CoRR, 2023)

On the Computational Complexity of Private High-dimensional Model Selection via the Exponential Mechanism. - Saptarshi Roy; Ambuj Tewari (CoRR, 2023)

Revisiting the Learnability of Apple Tasting. - Vinod Raman; Unique Subedi; Ananth Raman; Ambuj Tewari (CoRR, 2023)

Generalization Bounds in the Predict-Then-Optimize Framework. - Othman El Balghiti; Adam N. Elmachtoub; Paul Grigas; Ambuj Tewari (Math. Oper. Res., 2023)

Effectiveness of gamified team competition as mHealth intervention for medical interns: a cluster micro-randomized trial. - Jitao Wang; Yu Fang; Elena Frank; Maureen A. Walton; Margit Burmeister; Ambuj Tewari; Walter H. Dempsey; Timothy NeCamp; Srijan Sen; Zhenke Wu (npj Digit. Medicine, 2023)

Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit. - Eunjae Shim; Ambuj Tewari; Tim Cernak; Paul M. Zimmerman (J. Chem. Inf. Model., 2023)

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

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

Conformal Contextual Robust Optimization. - Yash P. Patel; Sahana Rayan; Ambuj Tewari (AISTATS, 2024)

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

Multiclass Online Learnability under Bandit Feedback. - Ananth Raman; Vinod Raman; Unique Subedi; Idan Mehalel; Ambuj Tewari (ALT, 2024)

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

Apple Tasting: Combinatorial Dimensions and Minimax Rates. - Vinod Raman; Unique Subedi; Ananth Raman; Ambuj Tewari (COLT, 2024)

Online Learning with Set-valued Feedback. - Vinod Raman; Unique Subedi; Ambuj Tewari (COLT, 2024)

Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks. - Ziping Xu; Zifan Xu; Runxuan Jiang; Peter Stone 001; Ambuj Tewari (ICLR, 2024)

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

Variational Inference with Coverage Guarantees in Simulation-Based Inference. - Yash P. Patel; Declan McNamara; Jackson Loper; Jeffrey Regier; Ambuj Tewari (ICML, 2024)

Smoothed Online Classification can be Harder than Batch Classification. - Vinod Raman; Unique Subedi; Ambuj Tewari (NeurIPS, 2024)

Online Classification with Predictions. - Vinod Raman; Ambuj Tewari (NeurIPS, 2024)

On the Computational Complexity of Private High-dimensional Model Selection. - Saptarshi Roy; Zehua Wang; Ambuj Tewari (NeurIPS, 2024)

Joint learning of linear time-invariant dynamical systems. - Aditya Modi 002; Mohamad Kazem Shirani Faradonbeh; Ambuj Tewari; George Michailidis (Autom., 2024)

A Framework for Partially Observed Reward-States in RLHF. - Chinmaya Kausik; Mirco Mutti; Aldo Pacchiano; Ambuj Tewari (CoRR, 2024)

A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Low-Rank MDPs. - Kihyuk Hong; Ambuj Tewari (CoRR, 2024)

Optimal Thresholding Linear Bandit. - Eduardo Ochoa Rivera; Ambuj Tewari (CoRR, 2024)

Provably Efficient Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs. - Kihyuk Hong; Yufan Zhang; Ambuj Tewari (CoRR, 2024)

Conformal Robust Control of Linear Systems. - Yash P. Patel; Sahana Rayan; Ambuj Tewari (CoRR, 2024)

Leveraging Offline Data in Linear Latent Bandits. - Chinmaya Kausik; Kevin Tan; Ambuj Tewari (CoRR, 2024)

Error Bounds for Learning Fourier Linear Operators. - Unique Subedi; Ambuj Tewari (CoRR, 2024)

Contextual Bandits with Arm Request Costs and Delays. - Lai Wei; Ambuj Tewari; Michael A. Cianfrocco (CoRR, 2024)

Generation through the lens of learning theory. - Vinod Raman; Ambuj Tewari (CoRR, 2024)

On the Benefits of Active Data Collection in Operator Learning. - Unique Subedi; Ambuj Tewari (CoRR, 2024)

Near Optimal Pure Exploration in Logistic Bandits. - Eduardo Ochoa Rivera; Ambuj Tewari (CoRR, 2024)

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

A Unified Theory of Supervised Online Learnability. - Vinod Raman; Unique Subedi; Ambuj Tewari (ALT, 2025)

Generation through the lens of learning theory. - Vinod Raman; Jiaxun Li; Ambuj Tewari (COLT, 2025)

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

The Complexity of Sequential Prediction in Dynamical Systems. - Vinod Raman; Unique Subedi; Ambuj Tewari (L4DC, 2025)

Examining the Impact of Local Constraint Violations on Energy Computations in DFT. - Vaibhav Khanna; Bikash Kanungo; Vikram Gavini; Ambuj Tewari; Paul M. Zimmerman (J. Comput. Chem., 2025)

Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators. - Unique Subedi; Ambuj Tewari (Trans. Mach. Learn. Res., 2025)

Zero-Shot Statistical Tests for LLM-Generated Text Detection using Finite Sample Concentration Inequalities. - Tara Radvand; Mojtaba Abdolmaleki; Mohamed Mostagir; Ambuj Tewari (CoRR, 2025)

Learning to Partially Defer for Sequences. - Sahana Rayan; Ambuj Tewari (CoRR, 2025)

Operator Learning: A Statistical Perspective. - Unique Subedi; Ambuj Tewari (CoRR, 2025)

A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs. - Kihyuk Hong; Ambuj Tewari (CoRR, 2025)

On Next-Token Prediction in LLMs: How End Goals Determine the Consistency of Decoding Algorithms. - Jacob Trauger; Ambuj Tewari (CoRR, 2025)

Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions. - Marc Brooks; Gabriel Durham; Kihyuk Hong; Ambuj Tewari (CoRR, 2025)

Offline Constrained Reinforcement Learning under Partial Data Coverage. - Kihyuk Hong; Ambuj Tewari (CoRR, 2025)

Continuum Transformers Perform In-Context Learning by Operator Gradient Descent. - Abhiti Mishra; Yash Patel; Ambuj Tewari (CoRR, 2025)

Operator Learning for Schrödinger Equation: Unitarity, Error Bounds, and Time Generalization. - Yash P. Patel; Unique Subedi; Ambuj Tewari (CoRR, 2025)