23 citations · 48 across the 11 of their papers we have counts for
11 papers · 1 filter
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle
Fang Kong, Yueran Yang, Wei Chen +1
Thompson sampling (TS) has attracted a lot of interest in the bandit area. It was introduced in the 1930s but has not been theoretically proven until recent years. All of its analy…
Incentivizing an Unknown Crowd
Jing Dong, Shuai Li, Baoxiang Wang
Motivated by the common strategic activities in crowdsourcing labeling, we study the problem of sequential eliciting information without verification (EIWV) for workers with a hete…
Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptions
Junyan Liu, Shuai Li, Dapeng Li
We study the problem of stochastic bandits with adversarial corruptions in the cooperative multi-agent setting, where agents interact with a common -armed bandit problem, an…
Cascading Bandit under Differential Privacy
Kun Wang, Jing Dong, Baoxiang Wang +2
This paper studies \emph{differential privacy (DP)} and \emph{local differential privacy (LDP)} in cascading bandits. Under DP, we propose an algorithm which guarantees -indisti…
Understanding Bandits with Graph Feedback
Houshuang Chen, Zengfeng Huang, Shuai Li +1
The bandit problem with graph feedback, proposed in [Mannor and Shamir, NeurIPS 2011], is modeled by a directed graph where is the collection of bandit arms, and once…
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks
Yuxin Wen, Shuai Li, Kui Jia
The problem of adversarial examples has shown that modern Neural Network (NN) models could be rather fragile. Among the more established techniques to solve the problem, one is to…