Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies
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
- Honor Among Bandits: No-Regret Learning for Online Fair Division (NeurIPS 2024 · similarity 0.92)
- Improved Regret Bounds for Online Fair Division with Bandit Learning (AAAI 2025 · similarity 0.92)
- Online Fair Division Meets Reordering Buffers (NeurIPS 2026 · similarity 0.90)
- Greedy-Based Online Fair Allocation with Adversarial Input: Enabling Best-of-Many-Worlds Guarantees (AAAI 2024 · similarity 0.87)
- Fairness and Efficiency in Online Class Matching (NeurIPS 2024 · similarity 0.87)
- Almost Envy-Free Allocations of Indivisible Goods or Chores with Entitlements (AAAI 2024 · similarity 0.87)
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.