Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Generalized Linear Bandits with Local Differential Privacy
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed LDP bandit methods instead of existing LDP-UCB and LDP-GLOC | Synthetic linear contextual bandits in single- and multi-parameter settings · Synthetic contextual bandit simulations | regret | Helped |
| Use LDP-SGD rather than the other compared methods at stronger privacy requirements | Synthetic contextual bandit simulations · Synthetic contextual bandit simulations | regret | Helped |
| Use the proposed private OLS or private SGD framework in stochastic single-parameter contextual bandits | Single-parameter contextual bandits · Stochastic contextual bandits, no margin | regret | Helped |
| Use synthetic updates for unselected arms and warm-up-based elimination | Multi-parameter contextual bandits · Stochastic multi-parameter contextual bandits | regret | Helped |
Closest papers by meaning
- Shuffle and Joint Differential Privacy for Generalized Linear Contextual Bandits (NeurIPS 2026 · similarity 0.93)
- On Differentially Private Federated Linear Contextual Bandits (ICLR 2024 · similarity 0.89)
- Faster Rates for Private Adversarial Bandits (ICML 2025 · similarity 0.89)
- Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility (AAAI 2024 · similarity 0.88)
- Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update (NeurIPS 2025 · similarity 0.88)
- Faster Algorithms for User-Level Private Stochastic Convex Optimization (NeurIPS 2024 · similarity 0.88)
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