125 citations · 546 across the 30 of their papers we have counts for
32 papers
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…
Towards Black-box Attacks on Deep Learning Apps
Hongchen Cao, Shuai Li, Yuming Zhou +3
Deep learning is a powerful weapon to boost application performance in many fields, including face recognition, object detection, image classification, natural language understandi…
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…
On Learning to Rank Long Sequences with Contextual Bandits
Anirban Santara, Claudio Gentile, Gaurav Aggarwal +1
Motivated by problems of learning to rank long item sequences, we introduce a variant of the cascading bandit model that considers flexible length sequences with varying rewards 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…