Decision-calibrated conformal uncertainty for pacing uses the support function of the signed policy sensitivity set to achieve smaller uncertainty radii on public datasets.
arXiv preprint arXiv:2208.12809 , year=
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UNVERDICTED 3representative citing papers
Online learning algorithms for bidding in repeated second-price auctions achieve rate-optimal regret by modeling ad value as a causal treatment effect and exploiting second-price payment information.
Derives near-optimal regret bounds of O~(log N) for piecewise-linear and O~(N^{1/3}) for smooth primitives for a confidence-bound algorithm that learns the optimal dynamic bidding policy without explicit randomization.
citing papers explorer
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Decision-Calibrated Conformal Uncertainty for Pacing Decisions in Streaming Advertising
Decision-calibrated conformal uncertainty for pacing uses the support function of the signed policy sensitivity set to achieve smaller uncertainty radii on public datasets.
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The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions
Online learning algorithms for bidding in repeated second-price auctions achieve rate-optimal regret by modeling ad value as a causal treatment effect and exploiting second-price payment information.
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Learning to Bid in Repeated Second-Price Auctions with Dynamic Values and Aggregated Feedback
Derives near-optimal regret bounds of O~(log N) for piecewise-linear and O~(N^{1/3}) for smooth primitives for a confidence-bound algorithm that learns the optimal dynamic bidding policy without explicit randomization.