Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Practical Contextual Bandits with Feedback Graphs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use full-information feedback instead of bandit feedback | GraphCB · RCV1 | progressive validation loss | Helped |
| Use the cops-and-robbers feedback graph instead of bandit feedback | GraphCB · RCV1 | progressive validation loss | Helped |
| Use graph-aware action probabilities rather than SquareCB action probabilities | GraphCB vs. SquareCB · RCV1, random directed self-aware graphs | progressive validation loss | Helped |
| Use graph-aware action probabilities rather than SquareCB action probabilities | GraphCB vs. SquareCB · Synthetic inventory dataset, fixed discretized action set | progressive validation loss | Helped |
| Use graph-aware action probabilities rather than SquareCB action probabilities | GraphCB vs. SquareCB · Synthetic inventory dataset, adaptive discretization | progressive validation loss | Helped |
Closest papers by meaning
- Efficient Contextual Bandits with Uninformed Feedback Graphs (ICML 2024 · similarity 0.95)
- Stochastic contextual bandits with graph feedback: from independence number to MAS number (NeurIPS 2024 · similarity 0.94)
- Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback (ICML 2019 · similarity 0.90)
- Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles (ICML 2020 · similarity 0.90)
- Model Selection for Contextual Bandits (NeurIPS 2019 · similarity 0.89)
- Neural Dueling Bandits: Preference-Based Optimization with Human Feedback (ICLR 2025 · similarity 0.89)
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