activity
20202026
most citedControlling the False Discovery Rate in Transformational Sparsity: Split Knockoffs

8 citations · 8 across the 4 of their papers we have counts for

collaborators

5 papers

cs.GT2026

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.…

cs.LG2022

On Private Online Convex Optimization: Optimal Algorithms in -Geometry and High Dimensional Contextual Bandits

Yuxuan Han, Zhicong Liang, Zhipeng Liang +3

Differentially private (DP) stochastic convex optimization (SCO) is ubiquitous in trustworthy machine learning algorithm design. This paper studies the DP-SCO problem with streamin…

cs.LG2022

NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release

Donghao Li, Yang Cao, Yuan Yao

Privacy-preserving data release algorithms have gained increasing attention for their ability to protect user privacy while enabling downstream machine learning tasks. However, the…

stat.ME2021★ 8 cited

Controlling the False Discovery Rate in Transformational Sparsity: Split Knockoffs

Yang Cao, Xinwei Sun, Yuan Yao

Controlling the False Discovery Rate (FDR) in a variable selection procedure is critical for reproducible discoveries, and it has been extensively studied in sparse linear models.…

cs.LG2020

Differentially Private Federated Learning with Laplacian Smoothing

Zhicong Liang, Bao Wang, Quanquan Gu +2

Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. However, an adversary may still be able to infer the p…