Research map / Bandits and online learning / Multi-armed bandits
Algorithms for Differentially Private Multi-Armed Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use interval-based private mean releases with lazy updates and noise independent of ε | DP-UCB-Int · 2-arm and 10-arm Bernoulli bandits | regret | Helped |
| use DP-UCB instead of DP-UCB-Bound | private UCB algorithms · 2-arm and 10-arm Bernoulli bandits | regret | Mixed |
| use DP-UCB-Bound, DP-UCB, or DP-UCB-Int instead of Private-UCB | private UCB algorithms · 2-arm and 10-arm Bernoulli bandits | regret | Helped |
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- An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule (ICML 2019 · similarity 0.92)
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- Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret (ICML 2025 · similarity 0.89)
- Shuffle and Joint Differential Privacy for Generalized Linear Contextual Bandits (NeurIPS 2026 · similarity 0.89)
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