Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Smoothed Adversarial Linear Contextual Bandits with Knapsacks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace UCB exploration with greedy (exploration-free) parameter estimation under smoothed contexts (Gaussian perturbation) | stochastic LinCBwK · smoothed stochastic LinCBwK | additive regret vs. best context-dependent fixed policy | Helped |
| Greedy primal-dual algorithm with doubling-trick estimation of tradeoff parameter Z under smoothed adaptive adversarial contexts | adversarial LinCBwK · smoothed adversarial LinCBwK | competitive ratio | Helped |
| Smoothed contexts (Gaussian perturbation) reduce the budget requirement for stochastic LinCBwK from Omega(m T^{3/4}) to Omega(m^{2/3} T^{3/4}) | stochastic LinCBwK · theory comparison | budget requirement | Helped |
| Smoothed adversarial setting relaxes the budget requirement to Omega(T^{3/4}) compared with the K T^{7/4}/B condition of adversarial BwK | adversarial LinCBwK · theory comparison | budget requirement | Helped |
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