Research map / Bandits and online learning / Contextual bandits and Thompson sampling
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SOLID instead of LinUCB/LinTS (optimism-based) | contextual linear bandit · Toy CLB with structure, balanced contexts | cumulative regret | Helped |
| Use SOLID instead of OAM | contextual linear bandit · Toy CLB with structure, balanced contexts | cumulative regret | Helped |
| Use SOLID instead of OAM under unbalanced context distribution (rho=0.9, 0.99) | contextual linear bandit · Toy CLB, unbalanced contexts | cumulative regret | Helped |
| Use SOLID instead of OAM as number of arms grows from 4 to 32 | contextual linear bandit · Random problems d=8, |X|=4 | cumulative regret | Helped |
| Forced exploration in OAM (epsilon>0 vs 0) on balanced toy problem | OAM · Toy CLB, balanced contexts | cumulative regret | Hurt |
Closest papers by meaning
- Contextual Bandits for Unbounded Context Distributions (ICML 2025 · similarity 0.92)
- Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit (NeurIPS 2024 · similarity 0.92)
- Generalized Linear Bandits with Limited Adaptivity (NeurIPS 2024 · similarity 0.92)
- Active Context Selection Improves Simple Regret in Contextual Bandits (NeurIPS 2026 · similarity 0.92)
- No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization (ICML 2025 · similarity 0.91)
- On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice (NeurIPS 2024 · similarity 0.91)
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