4 papers
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…
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…
Pricing Query Complexity of Multiplicative Revenue Approximation
Wei Tang, Yifan Wang, Mengxiao Zhang
We study the pricing query complexity of revenue maximization for a single buyer whose private valuation is drawn from an unknown distribution. In this setting, the seller must lea…
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…