Mohammad Ghavamzadeh

Preference Optimization via Contrastive Divergence: Your Policy Is Secretly an NLL Estimator AAAI 2026 Contextual Bandits with Stage-wise Constraints JMLR 2025 Conservative Contextual Bandits: Beyond Linear Representations ICLR 2025 Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence Analysis AISTATS 2025 Bridging Distributionally Robust Learning and Offline RL: An Approach to Mitigate Distribution Shift and Partial Data Coverage L4DC 2025 Confidence-aware Reward Optimization for Fine-tuning Text-to-Image Models ICLR 2024 Bayesian Regret Minimization in Offline Bandits ICML 2024 Maximum Entropy Model Correction in Reinforcement Learning ICLR 2024 Ordering-based Conditions for Global Convergence of Policy Gradient Methods NIPS 2023 Multiple-policy High-confidence Policy Evaluation AISTATS 2023 Multi-Task Off-Policy Learning from Bandit Feedback ICML 2023 A Mixture-of-Expert Approach to RL-based Dialogue Management ICLR 2023 Entropic Risk Optimization in Discounted MDPs AISTATS 2023 Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management NIPS 2023 Meta-Learning for Simple Regret Minimization AAAI 2023 DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models NIPS 2023 On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes NIPS 2023 Thompson Sampling with a Mixture Prior AISTATS 2022 Feature and Parameter Selection in Stochastic Linear Bandits ICML 2022 Private and Communication-Efficient Algorithms for Entropy Estimation NIPS 2022 Robust Reinforcement Learning using Offline Data NIPS 2022 Efficient Risk-Averse Reinforcement Learning NIPS 2022 Operator Splitting Value Iteration NIPS 2022 Deep Hierarchy in Bandits ICML 2022 Hierarchical Bayesian Bandits AISTATS 2022 Mirror Descent Policy Optimization ICLR 2022 Fixed-Budget Best-Arm Identification in Structured Bandits IJCAI 2022 Adaptive Sampling for Minimax Fair Classification NIPS 2021 Control-Aware Representations for Model-based Reinforcement Learning ICLR 2021 PID Accelerated Value Iteration Algorithm ICML 2021 Deep Bayesian Quadrature Policy Optimization AAAI 2021 Stochastic Bandits with Linear Constraints AISTATS 2021 Variational Model-based Policy Optimization IJCAI 2021 Neural Lyapunov Redesign L4DC 2021 Safe Policy Learning for Continuous Control CORL 2020 Active Model Estimation in Markov Decision Processes UAI 2020 Multi-step Greedy Reinforcement Learning Algorithms ICML 2020 Predictive Coding for Locally-Linear Control ICML 2020 Conservative Exploration in Reinforcement Learning AISTATS 2020 Randomized Exploration in Generalized Linear Bandits AISTATS 2020 Adaptive Sampling for Estimating Probability Distributions ICML 2020 Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control ICLR 2020 Online Planning with Lookahead Policies NIPS 2020 Improved Algorithms for Conservative Exploration in Bandits AAAI 2020 Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies NIPS 2019 Optimizing over a Restricted Policy Class in MDPs AISTATS 2019 Perturbed-History Exploration in Stochastic Multi-Armed Bandits IJCAI 2019 Perturbed-History Exploration in Stochastic Linear Bandits UAI 2019 Risk-Sensitive Generative Adversarial Imitation Learning AISTATS 2019 Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits ICML 2019 Robust Locally-Linear Controllable Embedding AISTATS 2018 A Block Coordinate Ascent Algorithm for Mean-Variance Optimization NIPS 2018 A Lyapunov-based Approach to Safe Reinforcement Learning NIPS 2018 Path Consistency Learning in Tsallis Entropy Regularized MDPs ICML 2018 More Robust Doubly Robust Off-policy Evaluation ICML 2018 Risk-Constrained Reinforcement Learning with Percentile Risk Criteria JMLR 2018 Model-Independent Online Learning for Influence Maximization ICML 2017 Online Learning to Rank in Stochastic Click Models ICML 2017 Sequential Multiple Hypothesis Testing with Type I Error Control AISTATS 2017 Conservative Contextual Linear Bandits NIPS 2017 Active Learning for Accurate Estimation of Linear Models ICML 2017 Bottleneck Conditional Density Estimation ICML 2017 Improved Learning Complexity in Combinatorial Pure Exploration Bandits AISTATS 2016 Analysis of Classification-based Policy Iteration Algorithms JMLR 2016 Bayesian Policy Gradient and Actor-Critic Algorithms JMLR 2016 Proximal Gradient Temporal Difference Learning Algorithms IJCAI 2016 Regularized Policy Iteration with Nonparametric Function Spaces JMLR 2016 Safe Policy Improvement by Minimizing Robust Baseline Regret NIPS 2016 Approximate Modified Policy Iteration and its Application to the Game of Tetris JMLR 2015 High Confidence Policy Improvement ICML 2015 Policy Gradient for Coherent Risk Measures NIPS 2015 Personalized Ad Recommendation Systems for Life-Time Value Optimization with Guarantees IJCAI 2015 Maximum Entropy Semi-Supervised Inverse Reinforcement Learning IJCAI 2015 Algorithms for CVaR Optimization in MDPs NIPS 2014 Actor-Critic Algorithms for Risk-Sensitive MDPs NIPS 2013 Approximate Dynamic Programming Finally Performs Well in the Game of Tetris NIPS 2013 A Generalized Kernel Approach to Structured Output Learning ICML 2013 Cost-sensitive Multiclass Classification Risk Bounds ICML 2013 Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence NIPS 2012 Finite-Sample Analysis of Least-Squares Policy Iteration JMLR 2012 Multi-Bandit Best Arm Identification NIPS 2011 Speedy Q-Learning NIPS 2011 LSTD with Random Projections NIPS 2010 Finite-sample Analysis of Bellman Residual Minimization ACML 2010 Regularized Policy Iteration NIPS 2008 Hierarchical Average Reward Reinforcement Learning JMLR 2007 Incremental Natural Actor-Critic Algorithms NIPS 2007 Bayesian Policy Gradient Algorithms NIPS 2006