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.
Learning to bid in non-stationary repeated first-price auctions.arXiv preprint arXiv:2501.13358
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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.