no code implementations • 30 Oct 2023 • Jialin Yi
The regret bounds I present in Chapter 3, 4 and 5 quantify how the regret depends on the connectivity of the communication network and the communication delay, thus giving useful guidance on design of the communication protocol in MACL systems
no code implementations • 22 Jan 2023 • Jialin Yi, Milan Vojnović
For the bandit feedback setting, we propose a near-optimal federated bandit algorithm called FEDEXP3.
no code implementations • 30 Nov 2022 • Jialin Yi, Milan Vojnović
We show that with suitable regularizers and communication protocols, a collaborative multi-agent \emph{follow-the-regularized-leader} (FTRL) algorithm has an individual regret upper bound that matches the lower bound up to a constant factor when the number of arms is large enough relative to degrees of agents in the communication graph.
no code implementations • ICLR 2022 • Boshi Wang, Jialin Yi, Hang Dong, Bo Qiao, Chuan Luo, QIngwei Lin
Combinatorial optimization problems with parameters to be predicted from side information are commonly seen in a variety of problems during the paradigm shift from reactive decision making to proactive decision making.
no code implementations • 31 Jul 2021 • Flore Sentenac, Jialin Yi, Clément Calauzènes, Vianney Perchet, Milan Vojnovic
Finding an optimal matching in a weighted graph is a standard combinatorial problem.