180 citations · 286 across the 91 of their papers we have counts for
9 papers · 1 filter
Dynamic Global Sensitivity for Differentially Private Contextual Bandits
Huazheng Wang, David Zhao, Hongning Wang
Bandit algorithms have become a reference solution for interactive recommendation. However, as such algorithms directly interact with users for improved recommendations, serious pr…
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback
Yiling Jia, Hongning Wang
Deep neural networks (DNNs) demonstrate significant advantages in improving ranking performance in retrieval tasks. Driven by the recent technical developments in optimization and…
Communication Efficient Distributed Learning for Kernelized Contextual Bandits
Chuanhao Li, Huazheng Wang, Mengdi Wang +1
We tackle the communication efficiency challenge of learning kernelized contextual bandits in a distributed setting. Despite the recent advances in communication-efficient distribu…
Meta Policy Learning for Cold-Start Conversational Recommendation
Zhendong Chu, Hongning Wang, Yun Xiao +2
Conversational recommender systems (CRS) explicitly solicit users' preferences for improved recommendations on the fly. Most existing CRS solutions count on a single policy trained…
Learning from a Learning User for Optimal Recommendations
Fan Yao, Chuanhao Li, Denis Nekipelov +2
In real-world recommendation problems, especially those with a formidably large item space, users have to gradually learn to estimate the utility of any fresh recommendations from…
Communication Efficient Federated Learning for Generalized Linear Bandits
Chuanhao Li, Hongning Wang
Contextual bandit algorithms have been recently studied under the federated learning setting to satisfy the demand of keeping data decentralized and pushing the learning of bandit…
IMO: Interactive Multi-Objective Off-Policy Optimization
Nan Wang, Hongning Wang, Maryam Karimzadehgan +2
Most real-world optimization problems have multiple objectives. A system designer needs to find a policy that trades off these objectives to reach a desired operating point. This p…
Learning Neural Ranking Models Online from Implicit User Feedback
Yiling Jia, Hongning Wang
Existing online learning to rank (OL2R) solutions are limited to linear models, which are incompetent to capture possible non-linear relations between queries and documents. In thi…
Learning Neural Contextual Bandits Through Perturbed Rewards
Yiling Jia, Weitong Zhang, Dongruo Zhou +2
Thanks to the power of representation learning, neural contextual bandit algorithms demonstrate remarkable performance improvement against their classical counterparts. But because…