A unified primal-dual framework learns latent linear treatment effect valuations and competitor bids in constrained first-price auctions, achieving near-optimal regret via strong Slater condition and adaptive burn-in.
EFFICIENT ALGORITHMS FOR LOGISTIC CONTEXTUAL SLATE BANDITS WITH BANDIT FEEDBACK
2 Pith papers cite this work. Polarity classification is still indexing.
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Proposes Slate-GLM-OFU and Slate-GLM-TS algorithms for logistic contextual slate bandits that achieve polynomial per-round time and sublinear regret under a diversity assumption.
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Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctions
A unified primal-dual framework learns latent linear treatment effect valuations and competitor bids in constrained first-price auctions, achieving near-optimal regret via strong Slater condition and adaptive burn-in.
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Efficient Algorithms for Logistic Contextual Slate Bandits with Bandit Feedback
Proposes Slate-GLM-OFU and Slate-GLM-TS algorithms for logistic contextual slate bandits that achieve polynomial per-round time and sublinear regret under a diversity assumption.