Continuous-Time Hierarchical Reinforcement Learning. - Mohammad Ghavamzadeh; Sridhar Mahadevan (ICML, 2001)

A multiagent reinforcement learning algorithm by dynamically merging markov decision processes. - Mohammad Ghavamzadeh; Sridhar Mahadevan (AAMAS, 2002)

Hierarchically Optimal Average Reward Reinforcement Learning. - Mohammad Ghavamzadeh; Sridhar Mahadevan (ICML, 2002)

Hierarchical Policy Gradient Algorithms. - Mohammad Ghavamzadeh; Sridhar Mahadevan (ICML, 2003)

Learning to Communicate and Act Using Hierarchical Reinforcement Learning. - Mohammad Ghavamzadeh; Sridhar Mahadevan (AAMAS, 2004)

The Workshop Program at the Nineteenth National Conference on Artificial Intelligence. - Ion Muslea; Virginia Dignum; Daniel D. Corkill; Catholijn M. Jonker; Frank Dignum; Silvia Coradeschi; Alessandro Saffiotti; Dan Fu; Jeff Orkin; William Cheetham; Kai Goebel; Piero P. Bonissone; et al. (AI Mag., 2005)

Bayesian Policy Gradient Algorithms. - Mohammad Ghavamzadeh; Yaakov Engel (NIPS, 2006)

Hierarchical multi-agent reinforcement learning. - Mohammad Ghavamzadeh; Sridhar Mahadevan; Rajbala Makar (Auton. Agents Multi Agent Syst., 2006)

Bayesian actor-critic algorithms. - Mohammad Ghavamzadeh; Yaakov Engel (ICML, 2007)

Incremental Natural Actor-Critic Algorithms. - Shalabh Bhatnagar; Richard S. Sutton; Mohammad Ghavamzadeh; Mark Lee (NIPS, 2007)

Hierarchical Average Reward Reinforcement Learning. - Mohammad Ghavamzadeh; Sridhar Mahadevan (J. Mach. Learn. Res., 2007)

Regularized Fitted Q-Iteration: Application to Planning. - Amir Massoud Farahmand; Mohammad Ghavamzadeh; Csaba Szepesvári; Shie Mannor (EWRL, 2008)

Regularized Policy Iteration. - Amir Massoud Farahmand; Mohammad Ghavamzadeh; Csaba Szepesvári; Shie Mannor (NIPS, 2008)

Regularized Fitted Q-Iteration for planning in continuous-space Markovian decision problems. - Amir Massoud Farahmand; Mohammad Ghavamzadeh; Csaba Szepesvári; Shie Mannor (ACC, 2009)

Natural actor-critic algorithms. - Shalabh Bhatnagar; Richard S. Sutton; Mohammad Ghavamzadeh; Mark Lee (Autom., 2009)

Bayesian Multi-Task Reinforcement Learning. - Alessandro Lazaric; Mohammad Ghavamzadeh (ICML, 2010)

Analysis of a Classification-based Policy Iteration Algorithm. - Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (ICML, 2010)

Finite-Sample Analysis of LSTD. - Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (ICML, 2010)

LSTD with Random Projections. - Mohammad Ghavamzadeh; Alessandro Lazaric; Odalric-Ambrym Maillard; Rémi Munos (NIPS, 2010)

Finite-sample Analysis of Bellman Residual Minimization. - Odalric-Ambrym Maillard; Rémi Munos; Alessandro Lazaric; Mohammad Ghavamzadeh (ACML, 2010)

Upper-Confidence-Bound Algorithms for Active Learning in Multi-armed Bandits. - Alexandra Carpentier; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos; Peter Auer (ALT, 2011)

Regularized Least Squares Temporal Difference Learning with Nested ℓ2 and ℓ1 Penalization. - Matthew W. Hoffman; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (EWRL, 2011)

Classification-based Policy Iteration with a Critic. - Victor Gabillon; Alessandro Lazaric; Mohammad Ghavamzadeh; Bruno Scherrer (ICML, 2011)

Finite-Sample Analysis of Lasso-TD. - Mohammad Ghavamzadeh; Alessandro Lazaric; Rémi Munos; Matthew W. Hoffman (ICML, 2011)

Speedy Q-Learning. - Mohammad Gheshlaghi Azar; Rémi Munos; Mohammad Ghavamzadeh; Hilbert J. Kappen (NIPS, 2011)

Multi-Bandit Best Arm Identification. - Victor Gabillon; Mohammad Ghavamzadeh; Alessandro Lazaric; Sébastien Bubeck (NIPS, 2011)

Least-Squares Methods for Policy Iteration. - Lucian Busoniu; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos; Robert Babuska; Bart De Schutter (Reinforcement Learning, 2012)

Bayesian Reinforcement Learning. - Nikos Vlassis; Mohammad Ghavamzadeh; Shie Mannor; Pascal Poupart (Reinforcement Learning, 2012)

Conservative and Greedy Approaches to Classification-Based Policy Iteration. - Mohammad Ghavamzadeh; Alessandro Lazaric (AAAI, 2012)

Semi-Supervised Apprenticeship Learning. - Michal Valko; Mohammad Ghavamzadeh; Alessandro Lazaric (EWRL, 2012)

A Dantzig Selector Approach to Temporal Difference Learning. - Matthieu Geist; Bruno Scherrer; Alessandro Lazaric; Mohammad Ghavamzadeh (ICML, 2012)

Approximate Modified Policy Iteration. - Bruno Scherrer; Victor Gabillon; Mohammad Ghavamzadeh; Matthieu Geist (ICML, 2012)

Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence. - Victor Gabillon; Mohammad Ghavamzadeh; Alessandro Lazaric (NIPS, 2012)

Finite-sample analysis of least-squares policy iteration. - Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (J. Mach. Learn. Res., 2012)

A Generalized Kernel Approach to Structured Output Learning. - Hachem Kadri; Mohammad Ghavamzadeh; Philippe Preux (ICML (1), 2013)

Cost-sensitive Multiclass Classification Risk Bounds. - Bernardo Ávila Pires; Csaba Szepesvári; Mohammad Ghavamzadeh (ICML (3), 2013)

Approximate Dynamic Programming Finally Performs Well in the Game of Tetris. - Victor Gabillon; Mohammad Ghavamzadeh; Bruno Scherrer (NIPS, 2013)

Actor-Critic Algorithms for Risk-Sensitive MDPs. - Prashanth L. A.; Mohammad Ghavamzadeh (NIPS, 2013)

Algorithms for CVaR Optimization in MDPs. - Yinlam Chow; Mohammad Ghavamzadeh (NIPS, 2014)

Actor-Critic Algorithms for Risk-Sensitive Reinforcement Learning. - Prashanth L. A.; Mohammad Ghavamzadeh (CoRR, 2014)

Classification-based Approximate Policy Iteration: Experiments and Extended Discussions. - Amir-massoud Farahmand; Doina Precup; André da Motta Salles Barreto; Mohammad Ghavamzadeh (CoRR, 2014)

High-Confidence Off-Policy Evaluation. - Philip S. Thomas; Georgios Theocharous; Mohammad Ghavamzadeh (AAAI, 2015)

High Confidence Policy Improvement. - Philip S. Thomas; Georgios Theocharous; Mohammad Ghavamzadeh (ICML, 2015)

Maximum Entropy Semi-Supervised Inverse Reinforcement Learning. - Julien Audiffren; Michal Valko; Alessandro Lazaric; Mohammad Ghavamzadeh (IJCAI, 2015)

Personalized Ad Recommendation Systems for Life-Time Value Optimization with Guarantees. - Georgios Theocharous; Philip S. Thomas; Mohammad Ghavamzadeh (IJCAI, 2015)

Policy Gradient for Coherent Risk Measures. - Aviv Tamar; Yinlam Chow; Mohammad Ghavamzadeh; Shie Mannor (NIPS, 2015)

Finite-Sample Analysis of Proximal Gradient TD Algorithms. - Bo Liu 006; Ji Liu 002; Mohammad Ghavamzadeh; Sridhar Mahadevan; Marek Petrik (UAI, 2015)

Ad Recommendation Systems for Life-Time Value Optimization. - Georgios Theocharous; Philip S. Thomas; Mohammad Ghavamzadeh (WWW (Companion Volume), 2015)

Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits. - Alexandra Carpentier; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos; Peter Auer; András Antos (CoRR, 2015)

Bayesian Reinforcement Learning: A Survey. - Mohammad Ghavamzadeh; Shie Mannor; Joelle Pineau; Aviv Tamar (Found. Trends Mach. Learn., 2015)

Approximate modified policy iteration and its application to the game of Tetris. - Bruno Scherrer; Mohammad Ghavamzadeh; Victor Gabillon; Boris Lesner; Matthieu Geist (J. Mach. Learn. Res., 2015)

Classification-Based Approximate Policy Iteration. - Amir-massoud Farahmand; Doina Precup; André da Motta Salles Barreto; Mohammad Ghavamzadeh (IEEE Trans. Autom. Control., 2015)

Improved Learning Complexity in Combinatorial Pure Exploration Bandits. - Victor Gabillon; Alessandro Lazaric; Mohammad Ghavamzadeh; Ronald Ortner; Peter L. Bartlett (AISTATS, 2016)

Proximal Gradient Temporal Difference Learning Algorithms. - Bo Liu 006; Ji Liu 002; Mohammad Ghavamzadeh; Sridhar Mahadevan; Marek Petrik (IJCAI, 2016)

Safe Policy Improvement by Minimizing Robust Baseline Regret. - Mohammad Ghavamzadeh; Marek Petrik; Yinlam Chow (NIPS, 2016)

Graphical Model Sketch. - Branislav Kveton; Hung Bui 001; Mohammad Ghavamzadeh; Georgios Theocharous; S. Muthukrishnan 001; Siqi Sun (ECML/PKDD (1), 2016)

Personalized Advertisement Recommendation: A Ranking Approach to Address the Ubiquitous Click Sparsity Problem. - Sougata Chaudhuri; Georgios Theocharous; Mohammad Ghavamzadeh (CoRR, 2016)

Conservative Contextual Linear Bandits. - Abbas Kazerouni; Mohammad Ghavamzadeh; Benjamin Van Roy (CoRR, 2016)

Regularized Policy Iteration with Nonparametric Function Spaces. - Amir-massoud Farahmand; Mohammad Ghavamzadeh; Csaba Szepesvári; Shie Mannor (J. Mach. Learn. Res., 2016)

Bayesian Policy Gradient and Actor-Critic Algorithms. - Mohammad Ghavamzadeh; Yaakov Engel; Michal Valko (J. Mach. Learn. Res., 2016)

Analysis of Classification-based Policy Iteration Algorithms. - Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (J. Mach. Learn. Res., 2016)

Variance-constrained actor-critic algorithms for discounted and average reward MDPs. - Prashanth L. A.; Mohammad Ghavamzadeh (Mach. Learn., 2016)

Automated Data Cleansing through Meta-Learning. - Ian Gemp; Georgios Theocharous; Mohammad Ghavamzadeh (AAAI, 2017)

Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems, with Applications to Digital Marketing. - Philip S. Thomas; Georgios Theocharous; Mohammad Ghavamzadeh; Ishan Durugkar; Emma Brunskill (AAAI, 2017)

Sequential Multiple Hypothesis Testing with Type I Error Control. - Alan Malek; Sumeet Katariya; Yinlam Chow; Mohammad Ghavamzadeh (AISTATS, 2017)

Active Learning for Accurate Estimation of Linear Models. - Carlos Riquelme; Mohammad Ghavamzadeh; Alessandro Lazaric (ICML, 2017)

Bottleneck Conditional Density Estimation. - Rui Shu; Hung Hai Bui; Mohammad Ghavamzadeh (ICML, 2017)

Model-Independent Online Learning for Influence Maximization. - Sharan Vaswani; Branislav Kveton; Zheng Wen 002; Mohammad Ghavamzadeh; Laks V. S. Lakshmanan; Mark Schmidt 001 (ICML, 2017)

Online Learning to Rank in Stochastic Click Models. - Masrour Zoghi; Tomás Tunys; Mohammad Ghavamzadeh; Branislav Kveton; Csaba Szepesvári; Zheng Wen 002 (ICML, 2017)

Importance of Recommendation Policy Space in Addressing Click Sparsity in Personalized Advertisement Display. - Sougata Chaudhuri; Georgios Theocharous; Mohammad Ghavamzadeh (MLDM, 2017)

Conservative Contextual Linear Bandits. - Abbas Kazerouni; Mohammad Ghavamzadeh; Yasin Abbasi; Benjamin Van Roy (NIPS, 2017)

Diffusion Independent Semi-Bandit Influence Maximization. - Sharan Vaswani; Branislav Kveton; Zheng Wen 002; Mohammad Ghavamzadeh; Laks V. S. Lakshmanan; Mark Schmidt 001 (CoRR, 2017)

Disentangling Dynamics and Content for Control and Planning. - Ershad Banijamali; Ahmad Khajenezhad; Ali Ghodsi 001; Mohammad Ghavamzadeh (CoRR, 2017)

Risk-Constrained Reinforcement Learning with Percentile Risk Criteria. - Yinlam Chow; Mohammad Ghavamzadeh; Lucas Janson; Marco Pavone 001 (J. Mach. Learn. Res., 2017)

Sequential Decision Making With Coherent Risk. - Aviv Tamar; Yinlam Chow; Mohammad Ghavamzadeh; Shie Mannor (IEEE Trans. Autom. Control., 2017)

Robust Locally-Linear Controllable Embedding. - Ershad Banijamali; Rui Shu; Mohammad Ghavamzadeh; Hung Bui 001; Ali Ghodsi 001 (AISTATS, 2018)

Path Consistency Learning in Tsallis Entropy Regularized MDPs. - Yinlam Chow; Ofir Nachum; Mohammad Ghavamzadeh (ICML, 2018)

More Robust Doubly Robust Off-policy Evaluation. - Mehrdad Farajtabar; Yinlam Chow; Mohammad Ghavamzadeh (ICML, 2018)

PAC Bandits with Risk Constraints. - Yahel David; Balázs Szörényi; Mohammad Ghavamzadeh; Shie Mannor; Nahum Shimkin (ISAIM, 2018)

A Lyapunov-based Approach to Safe Reinforcement Learning. - Yinlam Chow; Ofir Nachum; Edgar A. Duéñez-Guzmán; Mohammad Ghavamzadeh (NeurIPS, 2018)

A Block Coordinate Ascent Algorithm for Mean-Variance Optimization. - Tengyang Xie; Bo Liu 006; Yangyang Xu; Mohammad Ghavamzadeh; Yinlam Chow; Daoming Lyu; Daesub Yoon (NeurIPS, 2018)

Optimizing over a Restricted Policy Class in Markov Decision Processes. - Ershad Banijamali; Yasin Abbasi-Yadkori; Mohammad Ghavamzadeh; Nikos Vlassis (CoRR, 2018)

Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. - Branislav Kveton; Csaba Szepesvári; Zheng Wen 002; Mohammad Ghavamzadeh; Tor Lattimore (CoRR, 2018)

Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity. - Bo Liu 006; Ian Gemp; Mohammad Ghavamzadeh; Ji Liu 002; Sridhar Mahadevan; Marek Petrik (J. Artif. Intell. Res., 2018)

Optimizing over a Restricted Policy Class in MDPs. - Ershad Banijamali; Yasin Abbasi-Yadkori; Mohammad Ghavamzadeh; Nikos Vlassis (AISTATS, 2019)

Risk-Sensitive Generative Adversarial Imitation Learning. - Jonathan Lacotte; Mohammad Ghavamzadeh; Yinlam Chow; Marco Pavone 001 (AISTATS, 2019)

Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. - Branislav Kveton; Csaba Szepesvári; Sharan Vaswani; Zheng Wen 002; Tor Lattimore; Mohammad Ghavamzadeh (ICML, 2019)

Perturbed-History Exploration in Stochastic Multi-Armed Bandits. - Branislav Kveton; Csaba Szepesvári; Mohammad Ghavamzadeh; Craig Boutilier (IJCAI, 2019)

Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies. - Yonathan Efroni; Nadav Merlis; Mohammad Ghavamzadeh; Shie Mannor (NeurIPS, 2019)

Perturbed-History Exploration in Stochastic Linear Bandits. - Branislav Kveton; Csaba Szepesvári; Mohammad Ghavamzadeh; Craig Boutilier (UAI, 2019)

Lyapunov-based Safe Policy Optimization for Continuous Control. - Yinlam Chow; Ofir Nachum; Aleksandra Faust; Mohammad Ghavamzadeh; Edgar A. Duéñez-Guzmán (CoRR, 2019)

Binary Classification with Bounded Abstention Rate. - Shubhanshu Shekhar; Mohammad Ghavamzadeh; Tara Javidi (CoRR, 2019)

Active Learning for Binary Classification with Abstention. - Shubhanshu Shekhar; Mohammad Ghavamzadeh; Tara Javidi (CoRR, 2019)

Multi-Step Greedy and Approximate Real Time Dynamic Programming. - Yonathan Efroni; Mohammad Ghavamzadeh; Shie Mannor (CoRR, 2019)

Benchmarking Batch Deep Reinforcement Learning Algorithms. - Scott Fujimoto; Edoardo Conti; Mohammad Ghavamzadeh; Joelle Pineau (CoRR, 2019)

Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning. - Manan Tomar; Yonathan Efroni; Mohammad Ghavamzadeh (CoRR, 2019)

Adaptive Sampling for Estimating Multiple Probability Distributions. - Shubhanshu Shekhar; Mohammad Ghavamzadeh; Tara Javidi (CoRR, 2019)

Improved Algorithms for Conservative Exploration in Bandits. - Evrard Garcelon; Mohammad Ghavamzadeh; Alessandro Lazaric; Matteo Pirotta (AAAI, 2020)

Conservative Exploration in Reinforcement Learning. - Evrard Garcelon; Mohammad Ghavamzadeh; Alessandro Lazaric; Matteo Pirotta (AISTATS, 2020)

Randomized Exploration in Generalized Linear Bandits. - Branislav Kveton; Manzil Zaheer; Csaba Szepesvári; Lihong Li 001; Mohammad Ghavamzadeh; Craig Boutilier (AISTATS, 2020)

Safe Policy Learning for Continuous Control. - Yinlam Chow; Ofir Nachum; Aleksandra Faust; Edgar A. Duéñez-Guzmán; Mohammad Ghavamzadeh (CoRL, 2020)

Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control. - Nir Levine; Yinlam Chow; Rui Shu; Ang Li; Mohammad Ghavamzadeh; Hung Bui (ICLR, 2020)

Adaptive Sampling for Estimating Probability Distributions. - Shubhanshu Shekhar; Tara Javidi; Mohammad Ghavamzadeh (ICML, 2020)

Predictive Coding for Locally-Linear Control. - Rui Shu; Tung Nguyen; Yinlam Chow; Tuan Pham; Khoat Than; Mohammad Ghavamzadeh; Stefano Ermon; Hung H. Bui (ICML, 2020)

Multi-step Greedy Reinforcement Learning Algorithms. - Manan Tomar; Yonathan Efroni; Mohammad Ghavamzadeh (ICML, 2020)

Online Planning with Lookahead Policies. - Yonathan Efroni; Mohammad Ghavamzadeh; Shie Mannor (NeurIPS, 2020)

Active Model Estimation in Markov Decision Processes. - Jean Tarbouriech; Shubhanshu Shekhar; Matteo Pirotta; Mohammad Ghavamzadeh; Alessandro Lazaric (UAI, 2020)

Policy-Aware Model Learning for Policy Gradient Methods. - Romina Abachi; Mohammad Ghavamzadeh; Amir-massoud Farahmand (CoRR, 2020)

Automatic Policy Synthesis to Improve the Safety of Nonlinear Dynamical Systems. - Arash Mehrjou; Mohammad Ghavamzadeh; Bernhard Schölkopf (CoRR, 2020)

Finite-Sample Analysis of GTD Algorithms. - Bo Liu 006; Ji Liu 002; Mohammad Ghavamzadeh; Sridhar Mahadevan; Marek Petrik (CoRR, 2020)

Variance-Reduced Off-Policy Memory-Efficient Policy Search. - Daoming Lyu; Qi Qi 006; Mohammad Ghavamzadeh; Hengshuai Yao; Tianbao Yang; Bo Liu 006 (CoRR, 2020)

Soft-Robust Algorithms for Handling Model Misspecification. - Elita A. Lobo; Mohammad Ghavamzadeh; Marek Petrik (CoRR, 2020)

Non-Stationary Latent Bandits. - Joey Hong; Branislav Kveton; Manzil Zaheer; Yinlam Chow; Amr Ahmed 001; Mohammad Ghavamzadeh; Craig Boutilier (CoRR, 2020)

Deep Bayesian Quadrature Policy Optimization. - Ravi Tej Akella; Kamyar Azizzadenesheli; Mohammad Ghavamzadeh; Animashree Anandkumar; Yisong Yue (AAAI, 2021)

Stochastic Bandits with Linear Constraints. - Aldo Pacchiano; Mohammad Ghavamzadeh; Peter L. Bartlett; Heinrich Jiang (AISTATS, 2021)

Control-Aware Representations for Model-based Reinforcement Learning. - Brandon Cui; Yinlam Chow; Mohammad Ghavamzadeh (ICLR, 2021)

PID Accelerated Value Iteration Algorithm. - Amir Massoud Farahmand; Mohammad Ghavamzadeh (ICML, 2021)

Variational Model-based Policy Optimization. - Yinlam Chow; Brandon Cui; Moonkyung Ryu; Mohammad Ghavamzadeh (IJCAI, 2021)

Neural Lyapunov Redesign. - Arash Mehrjou; Mohammad Ghavamzadeh; Bernhard Schölkopf (L4DC, 2021)

Adaptive Sampling for Minimax Fair Classification. - Shubhanshu Shekhar; Greg Fields; Mohammad Ghavamzadeh; Tara Javidi (NeurIPS, 2021)

Adaptive Sampling for Minimax Fair Classification. - Shubhanshu Shekhar; Mohammad Ghavamzadeh; Tara Javidi (CoRR, 2021)

Fixed-Budget Best-Arm Identification in Contextual Bandits: A Static-Adaptive Algorithm. - Mohammad Javad Azizi; Branislav Kveton; Mohammad Ghavamzadeh (CoRR, 2021)

Parameter and Feature Selection in Stochastic Linear Bandits. - Ahmadreza Moradipari; Yasin Abbasi-Yadkori; Mahnoosh Alizadeh; Mohammad Ghavamzadeh (CoRR, 2021)

A review of uncertainty quantification in deep learning: Techniques, applications and challenges. - Moloud Abdar; Farhad Pourpanah; Sadiq Hussain; Dana Rezazadegan; Li Liu 002; Mohammad Ghavamzadeh; Paul W. Fieguth; Xiaochun Cao; Abbas Khosravi; U. Rajendra Acharya; Vladimir Makarenkov; Saeid Nahavandi (Inf. Fusion, 2021)

Active Learning for Classification With Abstention. - Shubhanshu Shekhar; Mohammad Ghavamzadeh; Tara Javidi (IEEE J. Sel. Areas Inf. Theory, 2021)

Hierarchical Bayesian Bandits. - Joey Hong; Branislav Kveton; Manzil Zaheer; Mohammad Ghavamzadeh (AISTATS, 2022)

Thompson Sampling with a Mixture Prior. - Joey Hong; Branislav Kveton; Manzil Zaheer; Mohammad Ghavamzadeh; Craig Boutilier (AISTATS, 2022)

Collaborative Multi-agent Stochastic Linear Bandits. - Ahmadreza Moradipari; Mohammad Ghavamzadeh; Mahnoosh Alizadeh (ACC, 2022)

Mirror Descent Policy Optimization. - Manan Tomar; Lior Shani; Yonathan Efroni; Mohammad Ghavamzadeh (ICLR, 2022)

Deep Hierarchy in Bandits. - Joey Hong; Branislav Kveton; Sumeet Katariya; Manzil Zaheer; Mohammad Ghavamzadeh (ICML, 2022)

Feature and Parameter Selection in Stochastic Linear Bandits. - Ahmadreza Moradipari; Berkay Turan; Yasin Abbasi-Yadkori; Mahnoosh Alizadeh; Mohammad Ghavamzadeh (ICML, 2022)

Fixed-Budget Best-Arm Identification in Structured Bandits. - Mohammad Javad Azizi; Branislav Kveton; Mohammad Ghavamzadeh (IJCAI, 2022)

Multi-Environment Meta-Learning in Stochastic Linear Bandits. - Ahmadreza Moradipari; Mohammad Ghavamzadeh; Taha Rajabzadeh; Christos Thrampoulidis; Mahnoosh Alizadeh (ISIT, 2022)

Private and Communication-Efficient Algorithms for Entropy Estimation. - Gecia Bravo Hermsdorff; Róbert Busa-Fekete; Mohammad Ghavamzadeh; Andrés Muñoz Medina; Umar Syed (NeurIPS, 2022)

Efficient Risk-Averse Reinforcement Learning. - Ido Greenberg; Yinlam Chow; Mohammad Ghavamzadeh; Shie Mannor (NeurIPS, 2022)

Robust Reinforcement Learning using Offline Data. - Kishan Panaganti; Zaiyan Xu; Dileep Kalathil; Mohammad Ghavamzadeh (NeurIPS, 2022)

Operator Splitting Value Iteration. - Amin Rakhsha; Andrew Wang; Mohammad Ghavamzadeh; Amir-massoud Farahmand (NeurIPS, 2022)

A Mixture-of-Expert Approach to RL-based Dialogue Management. - Yinlam Chow; Aza Tulepbergenov; Ofir Nachum; Moonkyung Ryu; Mohammad Ghavamzadeh; Craig Boutilier (CoRR, 2022)

Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings. - Jorge A. Mendez; Alborz Geramifard; Mohammad Ghavamzadeh; Bing Liu (CoRR, 2022)

RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk. - Jia Lin Hau; Marek Petrik; Mohammad Ghavamzadeh; Reazul Hasan Russel (CoRR, 2022)

Meta-Learning for Simple Regret Minimization. - Mohammad Javad Azizi; Branislav Kveton; Mohammad Ghavamzadeh; Sumeet Katariya (AAAI, 2023)

Multiple-policy High-confidence Policy Evaluation. - Christoph Dann; Mohammad Ghavamzadeh; Teodor V. Marinov (AISTATS, 2023)

Entropic Risk Optimization in Discounted MDPs. - Jia Lin Hau; Marek Petrik; Mohammad Ghavamzadeh (AISTATS, 2023)

Distributionally Robust Behavioral Cloning for Robust Imitation Learning. - Kishan Panaganti; Zaiyan Xu; Dileep Kalathil; Mohammad Ghavamzadeh (CDC, 2023)

A Mixture-of-Expert Approach to RL-based Dialogue Management. - Yinlam Chow; Aza Tulepbergenov; Ofir Nachum; Dhawal Gupta; Moonkyung Ryu; Mohammad Ghavamzadeh; Craig Boutilier (ICLR, 2023)

Multi-Task Off-Policy Learning from Bandit Feedback. - Joey Hong; Branislav Kveton; Manzil Zaheer; Sumeet Katariya; Mohammad Ghavamzadeh (ICML, 2023)

Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models. - Ying Fan; Olivia Watkins; Yuqing Du; Hao Liu 055; Moonkyung Ryu; Craig Boutilier; Pieter Abbeel; Mohammad Ghavamzadeh; Kangwook Lee 001; Kimin Lee (NeurIPS, 2023)

Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management. - Dhawal Gupta; Yinlam Chow; Azamat Tulepbergenov; Mohammad Ghavamzadeh; Craig Boutilier (NeurIPS, 2023)

On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes. - Jia Lin Hau; Erick Delage; Mohammad Ghavamzadeh; Marek Petrik (NeurIPS, 2023)

Ordering-based Conditions for Global Convergence of Policy Gradient Methods. - Jincheng Mei; Bo Dai 001; Alekh Agarwal; Mohammad Ghavamzadeh; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2023)

Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management. - Dhawal Gupta; Yinlam Chow; Mohammad Ghavamzadeh; Craig Boutilier (CoRR, 2023)

Aligning Text-to-Image Models using Human Feedback. - Kimin Lee; Hao Liu 055; Moonkyung Ryu; Olivia Watkins; Yuqing Du; Craig Boutilier; Pieter Abbeel; Mohammad Ghavamzadeh; Shixiang Shane Gu (CoRR, 2023)

A Review of Deep Learning for Video Captioning. - Moloud Abdar; Meenakshi Kollati; Swaraja Kuraparthi; Farhad Pourpanah; Daniel McDuff; Mohammad Ghavamzadeh; Shuicheng Yan; Abduallah Mohamed; Abbas Khosravi; Erik Cambria; Fatih Porikli (CoRR, 2023)

On Dynamic Program Decompositions of Static Risk Measures. - Jia Lin Hau; Erick Delage; Mohammad Ghavamzadeh; Marek Petrik (CoRR, 2023)

DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models. - Ying Fan; Olivia Watkins; Yuqing Du; Hao Liu 055; Moonkyung Ryu; Craig Boutilier; Pieter Abbeel; Mohammad Ghavamzadeh; Kangwook Lee 001; Kimin Lee (CoRR, 2023)

A Convex Relaxation Approach to Bayesian Regret Minimization in Offline Bandits. - Mohammad Ghavamzadeh; Marek Petrik; Guy Tennenholtz (CoRR, 2023)

Factual and Personalized Recommendations using Language Models and Reinforcement Learning. - Jihwan Jeong; Yinlam Chow; Guy Tennenholtz; Chih-Wei Hsu; Azamat Tulepbergenov; Mohammad Ghavamzadeh; Craig Boutilier (CoRR, 2023)

Preference Elicitation with Soft Attributes in Interactive Recommendation. - Erdem Biyik; Fan Yao; Yinlam Chow; Alex Haig; Chih-Wei Hsu; Mohammad Ghavamzadeh; Craig Boutilier (CoRR, 2023)

Confidence-aware Reward Optimization for Fine-tuning Text-to-Image Models. - Kyuyoung Kim; Jongheon Jeong; Minyong An; Mohammad Ghavamzadeh; Krishnamurthy Dj Dvijotham; Jinwoo Shin; Kimin Lee (ICLR, 2024)

Maximum Entropy Model Correction in Reinforcement Learning. - Amin Rakhsha; Mete Kemertas; Mohammad Ghavamzadeh; Amir-massoud Farahmand (ICLR, 2024)

Bayesian Regret Minimization in Offline Bandits. - Marek Petrik; Guy Tennenholtz; Mohammad Ghavamzadeh (ICML, 2024)

Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms. - Mohammad Javad Azizi; Thang Duong; Yasin Abbasi-Yadkori; András György 001; Claire Vernade; Mohammad Ghavamzadeh (RLJ, 2024)

Non-adaptive Online Finetuning for Offline Reinforcement Learning. - Audrey Huang; Mohammad Ghavamzadeh; Nan Jiang 008; Marek Petrik (RLJ, 2024)

Contextual Bandits with Stage-wise Constraints. - Aldo Pacchiano; Mohammad Ghavamzadeh; Peter L. Bartlett (CoRR, 2024)

Discovering Personalized Semantics for Soft Attributes in Recommender Systems Using Concept Activation Vectors. - Christina Göpfert; Alex Haig; Chih-Wei Hsu; Yinlam Chow; Ivan Vendrov; Tyler Lu; Deepak Ramachandran; Hubert Pham; Mohammad Ghavamzadeh; Craig Boutilier (Trans. Recomm. Syst., 2024)

Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence Analysis. - Jia Lin Hau; Erick Delage; Esther Derman; Mohammad Ghavamzadeh; Marek Petrik (AISTATS, 2025)

Conservative Contextual Bandits: Beyond Linear Representations. - Rohan Deb; Mohammad Ghavamzadeh; Arindam Banerjee 001 (ICLR, 2025)

Bridging Distributionally Robust Learning and Offline RL: An Approach to Mitigate Distribution Shift and Partial Data Coverage. - Kishan Panaganti; Zaiyan Xu; Dileep Kalathil; Mohammad Ghavamzadeh (L4DC, 2025)

Preference Optimization via Contrastive Divergence: Your Reward Model is Secretly an NLL Estimator. - Zhuotong Chen; Fang Liu; Xuan Zhu; Yanjun Qi; Mohammad Ghavamzadeh (CoRR, 2025)

C-3DPO: Constrained Controlled Classification for Direct Preference Optimization. - Kavosh Asadi; Julien Han; Xingzi Xu; Dominique Perrault-Joncas; Shoham Sabach; Karim Bouyarmane; Mohammad Ghavamzadeh (CoRR, 2025)

Diffusion Blend: Inference-Time Multi-Preference Alignment for Diffusion Models. - Min Cheng; Fatemeh Doudi; Dileep Kalathil; Mohammad Ghavamzadeh; Panganamala R. Kumar (CoRR, 2025)

Does Thinking More always Help? Understanding Test-Time Scaling in Reasoning Models. - Soumya Suvra Ghosal; Souradip Chakraborty; Avinash Reddy; Yifu Lu; Mengdi Wang; Dinesh Manocha; Furong Huang; Mohammad Ghavamzadeh; Amrit Singh Bedi (CoRR, 2025)