no code implementations • ICML 2020 • Yuan Deng, Sébastien Lahaie, Vahab Mirrokni
Motivated by the repeated sale of online ads via auctions, optimal pricing in repeated auctions has attracted a large body of research.
no code implementations • 3 Feb 2023 • Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni
In light of this finding, under a bandit feedback setting that mimics real-world scenarios where advertisers have limited information on ad auctions in each channels and how channels procure ads, we present an efficient learning algorithm that produces per-channel budgets whose resulting conversion approximates that of the global optimal problem.
no code implementations • NeurIPS 2021 • Yuan Deng, Hanrui Zhang
We study prior-independent dynamic auction design with production costs for a value-maximizing buyer, a paradigm that is becoming prevalent recently following the development of automatic bidding algorithms in advertising platforms.
no code implementations • NeurIPS 2019 • Yuan Deng, Jon Schneider, Balasubramanian Sivan
We show that even in this prior-free setting, it is possible to extract a $(1-\varepsilon)$-approximation of the full economic surplus for any $\varepsilon > 0$.
no code implementations • NeurIPS 2019 • Yuan Deng, Sébastien Lahaie, Vahab Mirrokni
Dynamic mechanisms offer powerful techniques to improve on both revenue and efficiency by linking sequential auctions using state information, but these techniques rely on exact distributional information of the buyers’ valuations (present and future), which limits their use in learning settings.
no code implementations • NeurIPS 2019 • Yuan Deng, Jon Schneider, Balusubramanian Sivan
How should a player who repeatedly plays a game against a no-regret learner strategize to maximize his utility?
no code implementations • 28 Oct 2016 • Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi
For the challenging problem of tensor PCA, we prove global convergence of the homotopy method in the "high noise" regime.
