7 papers
Dynamic Regret for Non-Stationary Linear Bandits via Misspecification Reductions
Zihao Hu, Yuan Yao, Jiheng Zhang +1
Many online decision-making problems involve both round-specific feasible actions and drifting reward models: eligible ad impressions, feasible prices, and available treatments can…
The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions
Yuxiao Wen, Zihao Hu, Yanjun Han +2
Existing auto-bidding algorithms in digital advertising often treat the value of an ad opportunity as the revenue obtained when an ad is shown and/or clicked, and bid accordingly.…
Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctions
Zihao Hu, Yuxiao Wen, Yuan Yao +2
We study the operational problem of automated bidding in repeated first-price auctions under budget and return-on-spend (RoS) constraints. In this setting, an auto-bidder must tran…
Learning to Bid in Non-Stationary Repeated First-Price Auctions
Zihao Hu, Xiaoyu Fan, Yuan Yao +2
First-price auctions have recently gained significant traction in digital advertising markets, exemplified by Google's transition from second-price to first-price auctions. Unlike…
Extragradient Type Methods for Riemannian Variational Inequality Problems
Zihao Hu, Guanghui Wang, Xi Wang +3
Riemannian convex optimization and minimax optimization have recently drawn considerable attention. Their appeal lies in their capacity to adeptly manage the non-convexity of the o…
Faster Margin Maximization Rates for Generic and Adversarially Robust Optimization Methods
Guanghui Wang, Zihao Hu, Claudio Gentile +2
First-order optimization methods tend to inherently favor certain solutions over others when minimizing an underdetermined training objective that has multiple global optima. This…
Bahadur representations for the bootstrap median absolute deviation and the application to projection depth weighted mean
Qing Liu, Xiaohui Liu
Median absolute deviation (hereafter MAD) is known as a robust alternative to the ordinary variance. It has been widely utilized to induce robust statistical inferential procedures…