4 papers · 1 filter
Near-Optimal Regret in Adversarial Kernel Bandits
Yu-Jie Zhang, Hao Qiu, Jonathan Scarlett +1
We study the adversarial kernel bandit problem, in which the loss at each round is induced by an arbitrary bounded element of a reproducing kernel Hilbert space (RKHS). We propose…
Parameter-free Dynamic Regret: Time-varying Movement Costs, Delayed Feedback, and Memory
Hao Qiu, Andrew Jacobsen, Emmanuel Esposito +1
In this paper, we study dynamic regret in unconstrained online convex optimization (OCO) with movement costs. Specifically, we generalize the standard setting by allowing the movem…
Near-Optimal Regret for Distributed Adversarial Bandits: A Black-Box Approach
Hao Qiu, Mengxiao Zhang, Nicolò Cesa-Bianchi
We study distributed adversarial bandits, where agents cooperate to minimize the global average loss while observing only their own local losses. We show that the minimax regre…
Decentralized Online Convex Optimization with Unknown Feedback Delays
Hao Qiu, Mengxiao Zhang, Juliette Achddou
Decentralized online convex optimization (D-OCO), where multiple agents within a network collaboratively learn optimal decisions in real-time, arises naturally in applications such…