274 citations · 3.1k across the 75 of their papers we have counts for
7 papers · 1 filter
Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition
Rong Ge, Holden Lee, Andrej Risteski
A key task in Bayesian machine learning is sampling from distributions that are only specified up to a partition function (i.e., constant of proportionality). One prevalent example…
High-Dimensional Robust Mean Estimation in Nearly-Linear Time
Yu Cheng, Ilias Diakonikolas, Rong Ge
We study the fundamental problem of high-dimensional mean estimation in a robust model where a constant fraction of the samples are adversarially corrupted. Recent work gave the fi…
Learning Two-layer Neural Networks with Symmetric Inputs
Rong Ge, Rohith Kuditipudi, Zhize Li +1
We give a new algorithm for learning a two-layer neural network under a general class of input distributions. Assuming there is a ground-truth two-layer network $$ y = A σ(Wx) + ξ,…
Non-Convex Matrix Completion Against a Semi-Random Adversary
Yu Cheng, Rong Ge
Matrix completion is a well-studied problem with many machine learning applications. In practice, the problem is often solved by non-convex optimization algorithms. However, the cu…
On the Local Minima of the Empirical Risk
Chi Jin, Lydia T. Liu, Rong Ge +1
Population risk is always of primary interest in machine learning; however, learning algorithms only have access to the empirical risk. Even for applications with nonconvex nonsmoo…
Stronger generalization bounds for deep nets via a compression approach
Sanjeev Arora, Rong Ge, Behnam Neyshabur +1
Deep nets generalize well despite having more parameters than the number of training samples. Recent works try to give an explanation using PAC-Bayes and Margin-based analyses, but…