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.
A standard self-normalized Bernstein / matrix-Freedman inequality (e.g
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.