5 papers
Near-optimal node-private community estimation in polynomial-time
Laurentiu Marchis, Olga Klopp, Po-Ling Loh +1
In this paper, we resolve an open question of Klopp & Zadik (2026) by providing a high-probability polynomial-time, node-private algorithm which nearly matches the performance of t…
Exact Coordinate Descent for High-Dimensional Regularized Huber Regression
Younghoon Kim, Po-Ling Loh, Sumanta Basu
This study develops a coordinate descent algorithm for high-dimensional Huber regression with an elastic-net penalty. Unlike existing gradient descent algorithms and coordinate des…
High-Dimensional Statistics: Reflections on Progress and Open Problems
Arian Maleki, Subhabrata Sen, Sivaraman Balakrishnan +9
Over the past two decades, the field of high-dimensional statistics has experienced substantial progress, driven largely by technological advances that have dramatically reduced th…
Node-private community estimation in stochastic block models: Tractable algorithms and lower bounds
Laurentiu Marchis, Ethan D'souza, Tomáš FlÃdr +1
We study the classical problem of community recovery in stochastic block models with a fixed number of communities, with a twist: We seek algorithms that are stable with respect to…
On the Benefits of Accelerated Optimization in Robust and Private Estimation
Laurentiu Andrei Marchis, Po-Ling Loh
We study the advantages of accelerated gradient methods, specifically based on the Frank-Wolfe method and projected gradient descent, for privacy and heavy-tailed robustness. Our a…