Anonymous Bandits for Multi-User Systems
Not on the map. Why?
Abstract (arXiv)
In this work, we present and study a new framework for online learning in systems with multiple users that provide user anonymity. Specifically, we extend the notion of bandits to obey the standard k-anonymity constraint by requiring each observation to be an aggregation of rewards for at least k users. This provides a simple yet effective framework where one can learn a clustering of users in an online fashion without observing any user's individual decision. We initiate the study of anonymous bandits and provide the first sublinear regret algorithms and lower bounds for this setting.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 50 references that are in the map. First posted on arXiv 21 Oct 2022.
Builds on
21 of 50 references.
References · 21
- 2019Batched Multi-armed Bandits Problemcited by 98
- 2020Differentially-Private Federated Linear Banditscited by 83
- 2018Differentially Private Contextual Linear Banditscited by 82
- 2021Gaussian Process Bandit Optimization with Few Batchescited by 57
- 2016Distributed Clustering of Linear Bandits in Peer to Peer Networkscited by 50
- 2019Regret Bounds for Batched Banditscited by 49
- 2016Distributed Cooperative Decision-Making in Multiarmed Bandits: Frequentist and Bayesian Algorithmscited by 48
- 2019Social Learning in Multi Agent Multi Armed Banditscited by 48
- 2015Algorithms for Differentially Private Multi-Armed Banditscited by 45
- 2016On Context-Dependent Clustering of Banditscited by 44
- 2016Collaborative Learning of Stochastic Bandits over a Social Networkcited by 29
- 2019Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost?cited by 27
9 more
- 2020Data Minimization for GDPR Compliance in Machine Learning Modelscited by 21
- 2020Robust Multi-Agent Multi-Armed Banditscited by 20
- 2021Parallelizing Thompson Samplingcited by 20
- 2021Batched Thompson Samplingcited by 19
- 2021Design of Experiments for Stochastic Contextual Linear Banditscited by 18
- 2021k-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifierscited by 12
- 2013Bounded regret in stochastic multi-armed banditscited by 10
- 2018Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Accesscited by 9
- 2021Dynamic Spectrum Access using Stochastic Multi-User Banditscited by 5
Built on by
Citing papers per year: 2025: 1.
1 paper.