This work studies the question of Representation Learning in RL: how can we learn a
compact low-dimensional representation such that on top of the representation we can …

We study matrix estimation problems arising in reinforcement learning with low-rank
structure. In low-rank bandits, the matrix to be recovered specifies the expected arm …

Obtaining rigorous statistical guarantees for generalization under distribution shift remains
an open and active research area. We study a setting we call\emph {combinatorial …

Discretization-based approaches to solving online reinforcement learning problems are
studied extensively on applications such as resource allocation and cache management …

Machine learning systems, especially with overparameterized deep neural networks, can
generalize to novel test instances drawn from the same distribution as the training data …

The practicality of reinforcement learning algorithms has been limited due to poor scaling
with respect to the problem size, as the sample complexity of learning an ε-optimal policy is …

We consider the problem of model estimation in episodic Block MDPs. In these MDPs, the
decision maker has access to rich observations or contexts generated from a small number …

We consider offline reinforcement learning (RL) with heterogeneous agents under severe
data scarcity, ie, we only observe a single historical trajectory for every agent under an …

There have been many recent advances on provably efficient Reinforcement Learning (RL)
in problems with rich observation spaces. However, all these works share a strong …

Modern language models rely on the transformer architecture and attention mechanism to
perform language understanding and text generation. In this work, we study learning a 1 …