We study PAC learning in the presence of strategic manipulation, where data points may
modify their features in certain predefined ways in order to receive a better outcome. We …

This paper studies adversarial bandits with corruptions. In the basic adversarial bandit
setting, the reward of arms is predetermined by an adversary who is oblivious to the …

We consider a dynamic pricing problem for repeated contextual second-price auctions with
multiple strategic buyers who aim to maximize their long-term time discounted utility. The …

Recommendation systems are dynamic economic systems that balance the needs of
multiple stakeholders. A recent line of work studies incentives from the content providers' …

We consider a variant of the stochastic multi-armed bandit problem. Specifically, the arms
are strategic agents who can improve their rewards or absorb them. The utility of an agent …

Blockchain is the next generation of secure data management that creates near-immutable
decentralized storage. Secure cryptography created a niche for blockchain to provide …

We study a strategic variant of the multi-armed bandit problem, which we coin the strategic
click-bandit. This model is motivated by applications in online recommendation where the …

We examine multi-armed bandit problems featuring strategic arms under debt-free reporting.
In this context, each arm is characterized by a bounded support reward distribution and …

Due to the broad range of applications of stochastic multi-armed bandit model,
understanding the effects of adversarial attacks and designing bandit algorithms robust to …

Most of the existing federated multi-armed bandits (FMAB) designs are based on the
presumption that clients will implement the specified design to collaborate with the server. In …