180 citations · 286 across the 91 of their papers we have counts for
8 papers · 1 filter
Calibrating Explore-Exploit Trade-off for Fair Online Learning to Rank
Yiling Jia, Hongning Wang
Online learning to rank (OL2R) has attracted great research interests in recent years, thanks to its advantages in avoiding expensive relevance labeling as required in offline supe…
Comparative Explanations of Recommendations
Aobo Yang, Nan Wang, Renqin Cai +2
As recommendation is essentially a comparative (or ranking) process, a good explanation should illustrate to users why an item is believed to be better than another, i.e., comparat…
Learning the Optimal Recommendation from Explorative Users
Fan Yao, Chuanhao Li, Denis Nekipelov +2
We propose a new problem setting to study the sequential interactions between a recommender system and a user. Instead of assuming the user is omniscient, static, and explicit, as…
Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits
Chuanhao Li, Hongning Wang
Linear contextual bandit is a popular online learning problem. It has been mostly studied in centralized learning settings. With the surging demand of large-scale decentralized mod…
When Are Linear Stochastic Bandits Attackable?
Huazheng Wang, Haifeng Xu, Hongning Wang
We study adversarial attacks on linear stochastic bandits: by manipulating the rewards, an adversary aims to control the behaviour of the bandit algorithm. Perhaps surprisingly, we…
Improve Learning from Crowds via Generative Augmentation
Zhendong Chu, Hongning Wang
Crowdsourcing provides an efficient label collection schema for supervised machine learning. However, to control annotation cost, each instance in the crowdsourced data is typicall…
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution
Chuanhao Li, Qingyun Wu, Hongning Wang
Collaborative bandit learning, i.e., bandit algorithms that utilize collaborative filtering techniques to improve sample efficiency in online interactive recommendation, has attrac…
Incentivizing Exploration in Linear Bandits under Information Gap
Huazheng Wang, Haifeng Xu, Chuanhao Li +2
We study the problem of incentivizing exploration for myopic users in linear bandits, where the users tend to exploit arm with the highest predicted reward instead of exploring. In…