← Authors

Mohammad Ghavamzadeh

88 papers · 2006–2026 · 11 conferences · across top CS/AI conferences

Achievements

🌈 Renaissance Researcher (5) 🧭 Keyword Pioneer 🌍 Conference Polyglot (11) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (12) πŸ—ΊοΈ Taxonomy Completionist (32) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (19) 🏠 Conference Loyalist (26) 🌟 Keyword Trendsetter Combo (4) 🌱 Topic Pioneer πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (10) 🧬 Topic Evolution πŸ† Keyword Champion πŸ† Grand Slam 🀝 Dynamic Duo (18) πŸ—ƒοΈ Keyword Collector (115) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (16) πŸš€ Conference Pioneer πŸ’Ž Century Club (87) ⚑ Prolific Year (10)

Conferences

NIPS (26) ICML (18) AISTATS (13) JMLR (8) ICLR (7) IJCAI (6) AAAI (4) L4DC (2) UAI (2) ACML (1) CORL (1)

Top co-authors

Yinlam Chow (18) Branislav Kveton (12) Alessandro Lazaric (12) Csaba SzepesvΓ‘ri (9) Craig Boutilier (7) Shie Mannor (6) Marek Petrik (6) Victor Gabillon (5) RΓ©mi Munos (5) Manzil Zaheer (5)

Research topics

Differential Privacy (1)

Keywords

reinforcement learning (16) regret bound (15) multi-armed bandit (14) markov decision process (13) policy gradient (12) dynamic programming (7) policy iteration (7) policy learning (6) sample complexity (5) contextual bandit (5) regret minimization (5) model-based reinforcement learning (5) online algorithm (5) temporal difference learning (4) value iteration (4) sequential decision making (4) sample efficiency (4) stochastic optimization (4) thompson sampling (4) online learning (4)

Papers

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