Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Model Selection in Contextual Stochastic Bandit Problems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Run CORRAL master over UCB and LinUCB bases instead of using a single base | Contextual bandit with 50 arms · Synthetic linear and non-linear reward arms | cumulative regret | Helped |
| Apply CORRAL over a grid of epsilon-greedy bases with different exploration constants c | epsilon-greedy · Two Bernoulli arms (0.5, 0.45), T=50,000 | cumulative regret | Helped |
| Smooth RL algorithms (UCRL, PSRL) and place a CORRAL master on top | UCRL2 / PSRL · River Swim | performance | Helped |
Closest papers by meaning
- Model Selection for Contextual Bandits (NeurIPS 2019 · similarity 0.95)
- Universal and data-adaptive algorithms for model selection in linear contextual bandits (ICML 2022 · similarity 0.93)
- Best of Both Worlds Model Selection (NeurIPS 2022 · similarity 0.93)
- Dynamic Balancing for Model Selection in Bandits and RL (ICML 2021 · similarity 0.93)
- Feature and Parameter Selection in Stochastic Linear Bandits (ICML 2022 · similarity 0.92)
- Sparsity-Agnostic Linear Bandits with Adaptive Adversaries (NeurIPS 2024 · similarity 0.89)
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