Linear sketches have been widely adopted to process fast data streams, and they can be
used to accurately answer frequency estimation, approximate top K items, and summarize …

The offline reinforcement learning (RL) problem is often motivated by the need to learn data-
driven decision policies in financial, legal and healthcare applications. However, the learned …

We study the problem of online dynamic pricing with two types of fairness constraints: a
“procedural fairness” which requires the “proposed” prices to be equal in expectation among …

We consider the problem of privacy protection in Reinforcement Learning (RL) algorithms
that operate over population processes, a practical but understudied setting that includes, for …

In this paper we study the problem of (finite horizon tabular) Markov decision processes
(MDPs) with heavy-tailed rewards under the constraint of differential privacy (DP) …

We study learning adversarial Markov decision process (MDP) in the episodic setting under
the constraint of differential privacy (DP). This is motivated by the widespread applications of …

We study adversarial bandit problems with potentially heavy-tailed losses. Unlike standard
settings with non-negative and bounded losses, managing negative and unbounded losses …