The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

Foundation models pretrained on diverse data at scale have demonstrated extraordinary
capabilities in a wide range of vision and language tasks. When such models are deployed …

Offline (or batch) reinforcement learning (RL) algorithms seek to learn an optimal policy from
a fixed dataset without active data collection. Based on the composition of the offline dataset …

In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based
solely on a dataset of historical interactions with the environment. This serves as an extreme …

A fundamental challenge in interactive learning and decision making, ranging from bandit
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …

Offline or batch reinforcement learning seeks to learn a near-optimal policy using history
data without active exploration of the environment. To counter the insufficient coverage and …

We study reinforcement learning (RL) with linear function approximation where the
underlying transition probability kernel of the Markov decision process (MDP) is a linear …

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 …

Natural policy gradient (NPG) methods are among the most widely used policy optimization
algorithms in contemporary reinforcement learning. This class of methods is often applied in …

Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …