Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Linear Contextual Bandits with Knapsacks
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Compare linCBwK regret bound to unstructured general contextual bandit with knapsacks approach (Agrawal & Devanur 2016), replacing sqrt(K log|Pi|) factor with m | linear contextual bandits with knapsacks · theoretical regret bound | regret bound dependence | Helped |
| Require budget B > m T^{3/4} (instead of arbitrary budgets) for the near-optimal bound | linCBwK algorithm · theoretical regret bound | regret bound validity range | Hurt |
| Estimate Z from first T0 rounds of exploration (T0 = sqrt(T)) rather than given Z | linCBwK algorithm · theoretical regret bound | regret bound | No clear effect |
Closest papers by meaning
- On Stochastic Contextual Bandits with Knapsacks in Small Budget Regime (ICLR 2025 · similarity 0.93)
- Contextual Decision-Making with Knapsacks Beyond the Worst Case (NeurIPS 2024 · similarity 0.91)
- On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice (NeurIPS 2024 · similarity 0.90)
- No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization (ICML 2025 · similarity 0.90)
- High-dimensional Linear Bandits with Knapsacks (ICML 2024 · similarity 0.90)
- Greedy Algorithms for Structured Bandits: A Sharp Characterization of Asymptotic Success / Failure (NeurIPS 2025 · similarity 0.90)
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.