A robust variant of binary search achieves regret O(C + log T) for dynamic pricing with known corruption C and O(C + log² T) when unknown.
Statistical science: a review journal of the Institute of Mathematical Statistics , volume=
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Active sampling with allocation q_j proportional to p_j to the 2/3 achieves tight regret sqrt(n/T) times norm of p to the 2/3 for known context distribution p, with improvement up to Theta(k to the 1/4) over passive sampling.
Adapts bandit algorithms to the Cox PH survival model for online treatment optimization under censoring, with theoretical sublinear regret and validation on simulations plus SEER cancer data.
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Toward Optimal Regret in Robust Pricing: Decoupling Corruption and Time
A robust variant of binary search achieves regret O(C + log T) for dynamic pricing with known corruption C and O(C + log² T) when unknown.
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Active Context Selection Improves Simple Regret in Contextual Bandits
Active sampling with allocation q_j proportional to p_j to the 2/3 achieves tight regret sqrt(n/T) times norm of p to the 2/3 for known context distribution p, with improvement up to Theta(k to the 1/4) over passive sampling.
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Online Survival Analysis: A Bandit Approach under Cox PH Model
Adapts bandit algorithms to the Cox PH survival model for online treatment optimization under censoring, with theoretical sublinear regret and validation on simulations plus SEER cancer data.