In first-price auctions with feedback-only shilling, an algorithm combining robust interval elimination and optimistic debiasing with racing achieves near-optimal regret rates of O(T^{2/3}) or O(sqrt(T)) and matches a lower bound in the single-active-region case.
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Adaptive GLM with MQLE and GP regression with UCB for dynamic insurance pricing, showing parameter convergence and regret analysis under delayed claims.
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Do Not Trust The Auctioneer: Learning to Bid in Feedback-Manipulated Auctions
In first-price auctions with feedback-only shilling, an algorithm combining robust interval elimination and optimistic debiasing with racing achieves near-optimal regret rates of O(T^{2/3}) or O(sqrt(T)) and matches a lower bound in the single-active-region case.
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Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches
Adaptive GLM with MQLE and GP regression with UCB for dynamic insurance pricing, showing parameter convergence and regret analysis under delayed claims.