Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Online Clustering of Bandits with Misspecified User Models
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts (ICLR 2025 · similarity 0.94)
- Online Clustering of Dueling Bandits (ICML 2025 · similarity 0.92)
- Clustering Items through Bandit Feedback: Finding the Right Feature out of Many (ICML 2025 · similarity 0.89)
- Leveraging Offline Data in Linear Latent Contextual Bandits (ICML 2025 · similarity 0.89)
- Variance-aware Regret Bounds for Stochastic Contextual Dueling Bandits (ICLR 2024 · similarity 0.88)
- Efficient Contextual Bandits with Uninformed Feedback Graphs (ICML 2024 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
