most citedScaling Limit: Exact and Tractable Analysis of Online Learning Algorithms with Applications to Regularized Regression and PCA

19 citations · 38 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG201719 cited

Scaling Limit: Exact and Tractable Analysis of Online Learning Algorithms with Applications to Regularized Regression and PCA

Chuang Wang, Jonathan Mattingly, Yue M. Lu

We present a framework for analyzing the exact dynamics of a class of online learning algorithms in the high-dimensional scaling limit. Our results are applied to two concrete exam…

cs.LG201710 cited

The Scaling Limit of High-Dimensional Online Independent Component Analysis

Chuang Wang, Yue M. Lu

We analyze the dynamics of an online algorithm for independent component analysis in the high-dimensional scaling limit. As the ambient dimension tends to infinity, and with proper…

cs.IT2017

Phase Retrieval via Linear Programming: Fundamental Limits and Algorithmic Improvements

Oussama Dhifallah, Christos Thrampoulidis, Yue M. Lu

A recently proposed convex formulation of the phase retrieval problem estimates the unknown signal by solving a simple linear program. This new scheme, known as PhaseMax, is comput…

cs.IT20174 cited

Fundamental Limits of PhaseMax for Phase Retrieval: A Replica Analysis

Oussama Dhifallah, Yue M. Lu

We consider a recently proposed convex formulation, known as the PhaseMax method, for solving the phase retrieval problem. Using the replica method from statistical mechanics, we a…

cs.IT20171 cited

Multiprocessor Approximate Message Passing with Column-Wise Partitioning

Yanting Ma, Yue M. Lu, Dror Baron

Solving a large-scale regularized linear inverse problem using multiple processors is important in various real-world applications due to the limitations of individual processors a…

cs.IT20153 cited

Optimal Detection of Random Walks on Graphs: Performance Analysis via Statistical Physics

Ameya Agaskar, Yue M. Lu

We study the problem of detecting a random walk on a graph from a sequence of noisy measurements at every node. There are two hypotheses: either every observation is just meaningle…