A Pareto-optimal randomized learning-augmented algorithm for online bidding is obtained by reducing any algorithm to a bidding profile whose optimal form is characterized by a system of delayed differential equations.
Optimal stopping with a predicted prior.CoRR, abs/2511.03289
2 Pith papers cite this work. Polarity classification is still indexing.
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Private unreliable signals in single-buyer pricing yield strictly better consistency-robustness Pareto frontiers than public signals, with mechanisms achieving perfect consistency and at least 1/2 robustness for any prior.
citing papers explorer
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Optimal Learning-Augmented Algorithm for Online Bidding
A Pareto-optimal randomized learning-augmented algorithm for online bidding is obtained by reducing any algorithm to a bidding profile whose optimal form is characterized by a system of delayed differential equations.
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Optimal Pricing with Unreliable Signals
Private unreliable signals in single-buyer pricing yield strictly better consistency-robustness Pareto frontiers than public signals, with mechanisms achieving perfect consistency and at least 1/2 robustness for any prior.