TH1.R3.3

Federated Learning for Heterogeneous Bandits with Unobserved Contexts

Jiabin Lin, Shana Moothedath, Iowa State University, United States
Session:
Multi-Armed Bandits 1
Track:
8: Bandits
Location:
Ypsilon IV-V-VI
Presentation Time:
Thu, 11 Jul, 10:25 - 10:45
Session Chair:
Ali Tajer, Rensselaer Polytechnic Institute
Presentation
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Discussion
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Session TH1.R3
TH1.R3.1: Improved Bound for Robust Causal Bandits with Linear Models
Zirui Yan, Arpan Mukherjee, Burak Varıcı, Ali Tajer, Rensselaer Polytechnic Institute, United States
TH1.R3.2: Multi-armed Bandits with Probing
Eray Can Elumar, Carnegie Mellon University, United States; Cem Tekin, Bilkent University, Turkey; Osman Yagan, Carnegie Mellon University, United States
TH1.R3.3: Federated Learning for Heterogeneous Bandits with Unobserved Contexts
Jiabin Lin, Shana Moothedath, Iowa State University, United States
TH1.R3.4: Explore-then-Commit Algorithms for Decentralized Two-sided Matching Markets
Tejas Pagare, Department of Electrical Engineering, IIT Bombay, India; Avishek Ghosh, SysCon and CMInDS, IIT Bombay, India
Resources
No resources available.