Research map / Bandits and online learning / Online convex optimization and games
Weighted Linear Bandits for Non-Stationary Environments
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use exponentially discounted observations rather than all past observations | D-LinUCB · Synthetic abruptly-changing environment | cumulative regret | Helped |
| Use exponentially discounted observations rather than all past observations | D-LinUCB · Synthetic slowly-varying environment | cumulative regret | Helped |
| Use exponentially discounted observations rather than all past observations | D-LinUCB · Criteo simulated partial class inversion | cumulative dynamic regret | Helped |
| Use only recent observations instead of all available data | D-LinUCB · Criteo simulated stationary period | cumulative dynamic regret | Hurt |
| Use D-LinUCB rather than dLinUCB change detection | Linear bandit policies · Synthetic slowly-varying environment | cumulative regret | Helped |
| Use D-LinUCB rather than dLinUCB change detection | Linear bandit policies · Criteo simulated partial class inversion | cumulative dynamic regret | Helped |
| Use D-LinUCB rather than SW-LinUCB | Linear bandit policies · Synthetic slowly-varying environment | cumulative regret | No clear effect |
| Use D-LinUCB rather than LinUCB-OR with known change points | Linear bandit policies · Synthetic abruptly-changing environment | cumulative regret | Hurt |
| Decrease the noise variance | dLinUCB · Synthetic abruptly-changing environment | cumulative regret | Helped |
Closest papers by meaning
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- Online Linear Regression in Dynamic Environments via Discounting (ICML 2024 · similarity 0.88)
- Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update (NeurIPS 2025 · similarity 0.88)
- Linear Bandits with Partially Observable Features (ICML 2025 · similarity 0.88)
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