101 citations · 451 across the 77 of their papers we have counts for
77 papers
Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs
Lin Yang, Xuchuang Wang, Mohammad Hajiesmaili +3
Recently, there has been extensive study of cooperative multi-agent multi-armed bandits where a set of distributed agents cooperatively play the same multi-armed bandit game. The g…
Multi-Fidelity Multi-Armed Bandits Revisited
Xuchuang Wang, Qingyun Wu, Wei Chen +1
We study the multi-fidelity multi-armed bandit (MF-MAB), an extension of the canonical multi-armed bandit (MAB) problem. MF-MAB allows each arm to be pulled with different costs (f…
Communication Efficient Distributed Newton Method with Fast Convergence Rates
Chengchang Liu, Lesi Chen, Luo Luo +1
We propose a communication and computation efficient second-order method for distributed optimization. For each iteration, our method only requires communication c…
A Survey of Federated Evaluation in Federated Learning
Behnaz Soltani, Yipeng Zhou, Venus Haghighi +1
In traditional machine learning, it is trivial to conduct model evaluation since all data samples are managed centrally by a server. However, model evaluation becomes a challenging…
Contextual Combinatorial Bandits with Probabilistically Triggered Arms
Xutong Liu, Jinhang Zuo, Siwei Wang +4
We study contextual combinatorial bandits with probabilistically triggered arms (CMAB-T) under a variety of smoothness conditions that capture a wide range of applications, suc…
Uncertainty-Aware Instance Reweighting for Off-Policy Learning
Xiaoying Zhang, Junpu Chen, Hongning Wang +4
Off-policy learning, referring to the procedure of policy optimization with access only to logged feedback data, has shown importance in various real-world applications, such as se…