Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

Traditional online algorithms encapsulate decision making under uncertainty, and give ways
to hedge against all possible future events, while guaranteeing a nearly optimal solution, as …

We consider online allocation problems with concave revenue functions and resource
constraints, which are central problems in revenue management and online advertising. In …

We investigate the problem of corruption robustness in offline reinforcement learning (RL)
with general function approximation, where an adversary can corrupt each sample in the …

A major research direction in contextual bandits is to develop algorithms that are
computationally efficient, yet support flexible, general-purpose function approximation …

We study the stochastic multi-armed bandits problem in the presence of adversarial
corruption. We present a new algorithm for this problem whose regret is nearly optimal …

We study the linear contextual bandit problem in the presence of adversarial corruption,
where the reward at each round is corrupted by an adversary, and the corruption level (ie …

We consider the problem faced by a firm that receives highly differentiated products in an
online fashion. The firm needs to price these products to sell them to its customer base …

Despite the significant interest and progress in reinforcement learning (RL) problems with
adversarial corruption, current works are either confined to the linear setting or lead to an …

We initiate the study of episodic reinforcement learning under adversarial corruptions in both
the rewards and the transition probabilities of the underlying system extending recent results …