2.7k citations · 4k across the 84 of their papers we have counts for
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Multi-Task Off-Policy Learning from Bandit Feedback
Joey Hong, Branislav Kveton, Sumeet Katariya +2
Many practical applications, such as recommender systems and learning to rank, involve solving multiple similar tasks. One example is learning of recommendation policies for users…
Operator Splitting Value Iteration
Amin Rakhsha, Andrew Wang, Mohammad Ghavamzadeh +1
We introduce new planning and reinforcement learning algorithms for discounted MDPs that utilize an approximate model of the environment to accelerate the convergence of the value…
RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk
Jia Lin Hau, Marek Petrik, Mohammad Ghavamzadeh +1
Prior work on safe Reinforcement Learning (RL) has studied risk-aversion to randomness in dynamics (aleatory) and to model uncertainty (epistemic) in isolation. We propose and anal…
Robust Reinforcement Learning using Offline Data
Kishan Panaganti, Zaiyan Xu, Dileep Kalathil +1
The goal of robust reinforcement learning (RL) is to learn a policy that is robust against the uncertainty in model parameters. Parameter uncertainty commonly occurs in many real-w…
A Mixture-of-Expert Approach to RL-based Dialogue Management
Yinlam Chow, Aza Tulepbergenov, Ofir Nachum +3
Despite recent advancements in language models (LMs), their application to dialogue management (DM) problems and ability to carry on rich conversations remain a challenge. We use r…
Collaborative Multi-agent Stochastic Linear Bandits
Ahmadreza Moradipari, Mohammad Ghavamzadeh, Mahnoosh Alizadeh
We study a collaborative multi-agent stochastic linear bandit setting, where agents that form a network communicate locally to minimize their overall regret. In this setting, e…
Multi-Environment Meta-Learning in Stochastic Linear Bandits
Ahmadreza Moradipari, Mohammad Ghavamzadeh, Taha Rajabzadeh +2
In this work we investigate meta-learning (or learning-to-learn) approaches in multi-task linear stochastic bandit problems that can originate from multiple environments. Inspired…
Efficient Risk-Averse Reinforcement Learning
Ido Greenberg, Yinlam Chow, Mohammad Ghavamzadeh +1
In risk-averse reinforcement learning (RL), the goal is to optimize some risk measure of the returns. A risk measure often focuses on the worst returns out of the agent's experienc…
Deep Hierarchy in Bandits
Joey Hong, Branislav Kveton, Sumeet Katariya +2
Mean rewards of actions are often correlated. The form of these correlations may be complex and unknown a priori, such as the preferences of a user for recommended products and the…
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms
MohammadJavad Azizi, Thang Duong, Yasin Abbasi-Yadkori +3
We study a sequential decision problem where the learner faces a sequence of -armed bandit tasks. The task boundaries might be known (the bandit meta-learning setting), or unkno…
Meta-Learning for Simple Regret Minimization
Mohammadjavad Azizi, Branislav Kveton, Mohammad Ghavamzadeh +1
We develop a meta-learning framework for simple regret minimization in bandits. In this framework, a learning agent interacts with a sequence of bandit tasks, which are sampled i.i…