54 citations · 64 across the 7 of their papers we have counts for
9 papers
Computing Multi-Slot First-Price Pacing Equilibria at Scale via Entropic Smoothing
Yuchen Xie, Shenyinying Tu, Yuan Gao +2
Pacing equilibria describe how budget-constrained advertisers clear an ad market, but computing one at production scale is hard when every request fills several slots: the allocati…
Large-Scale Regularized Matching on GPU Clusters
Aida Rahmattalabi, Gregory Dexter, Sanjana Garg +5
Production decision systems such as ad allocation or content matching involve millions of users and thousands of items, reducing to large-scale linear programs with sparse block-di…
Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization
Kaixuan Huang, Yu Wu, Xuezhou Zhang +4
Online influence maximization aims to maximize the influence spread of a content in a social network with unknown network model by selecting a few seed nodes. Recent studies follow…
A Decomposition Algorithm for Large-Scale Security-Constrained AC Optimal Power Flow
Frank E. Curtis, Daniel K. Molzahn, Shenyinying Tu +3
A decomposition algorithm for solving large-scale security-constrained AC optimal power flow problems is presented. The formulation considered is the one used in the ARPA-E Grid Op…
A Primer on Coordinate Descent Algorithms
Hao-Jun Michael Shi, Shenyinying Tu, Yangyang Xu +1
This monograph presents a class of algorithms called coordinate descent algorithms for mathematicians, statisticians, and engineers outside the field of optimization. This particul…
Optimizing quantization for Lasso recovery
Xiaoyi Gu, Shenyinying Tu, Hao-Jun Michael Shi +3
This letter is focused on quantized Compressed Sensing, assuming that Lasso is used for signal estimation. Leveraging recent work, we provide a framework to optimize the quantizati…
Methods for Quantized Compressed Sensing
Hao-Jun Michael Shi, Mindy Case, Xiaoyi Gu +2
In this paper, we compare and catalog the performance of various greedy quantized compressed sensing algorithms that reconstruct sparse signals from quantized compressed measuremen…
A note on practical approximate projection schemes in signal space methods
Xiaoyi Gu, Deanna Needell, Shenyinying Tu
Compressive sensing (CS) is a new technology which allows the acquisition of signals directly in compressed form, using far fewer measurements than traditional theory dictates. Rec…
Practical approximate projection schemes in greedy signal space methods
Chris Garnatz, Xiaoyi Gu, Alison Kingman +3
Compressive sensing (CS) is a new signal acquisition paradigm which shows that far fewer samples are required to reconstruct sparse signals than previously thought. Although most o…