Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Fairness in Learning: Classic and Contextual Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add fairness constraint (chained confidence intervals) to bandit learning | classic stochastic bandits · k-arm stochastic bandits | regret / learning rate | Hurt |
| Fairness requirement on contextual bandit with linear payoffs via KWIK reduction | linear contextual bandit · linear contextual bandit | regret | No clear effect |
| Fairness constraint on conjunction contextual bandits | conjunction contextual bandit · boolean conjunctions | worst-case regret | Hurt |
Closest papers by meaning
- Achieving Fairness in the Stochastic Multi-Armed Bandit Problem (AAAI 2020 · similarity 0.93)
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- Improved Regret Bounds for Online Fair Division with Bandit Learning (AAAI 2025 · similarity 0.89)
- Causal Logistic Bandits with Counterfactual Fairness Constraints (ICML 2025 · similarity 0.88)
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