We study a Markov matching market involving a planner and a set of strategic agents on the
two sides of the market. At each step, the agents are presented with a dynamical context …

Storm prediction provides the early alert for preparation, avoiding potential damage to
property and human safety. However, a traditional storm prediction model usually incurs …

We study offline reinforcement learning (RL) in partially observable Markov decision
processes. In particular, we aim to learn an optimal policy from a dataset collected by a …

Linear contextual bandits represent a fundamental class of models with numerous real-
world applications, and it is critical to develop algorithms that can effectively manage noise …

We study multi-agent reinforcement learning in the setting of episodic Markov decision
processes, where many agents cooperate via communication through a central server. We …

We study the off-policy evaluation (OPE) problem in reinforcement learning with linear
function approximation, which aims to estimate the value function of a target policy based on …

In this work, we study the low-rank MDPs with adversarially changed losses in the full-
information feedback setting. In particular, the unknown transition probability kernel admits a …

In this paper, we study gap-dependent regret guarantees for risk-sensitive reinforcement
learning based on the entropic risk measure. We propose a novel definition of sub-optimality …

The online linear optimization paradigm is important to many real-world network
applications as well as theoretical algorithmic studies. Recent studies have made attempts …

We study reinforcement learning (RL) with linear function approximation, unknown
transition, and adversarial losses in the bandit feedback setting. Specifically, the unknown …