908 citations · 1.7k across the 21 of their papers we have counts for
6 papers · 1 filter
Mixture Models, Robustness, and Sum of Squares Proofs
Samuel B. Hopkins, Jerry Li
We use the Sum of Squares method to develop new efficient algorithms for learning well-separated mixtures of Gaussians and robust mean estimation, both in high dimensions, that sub…
The Power of Choice in Priority Scheduling
Dan Alistarh, Justin Kopinsky, Jerry Li +1
Consider the following random process: we are given queues, into which elements of increasing labels are inserted uniformly at random. To remove an element, we pick two queues…
On the Limitations of First-Order Approximation in GAN Dynamics
Jerry Li, Aleksander Madry, John Peebles +1
While Generative Adversarial Networks (GANs) have demonstrated promising performance on multiple vision tasks, their learning dynamics are not yet well understood, both in theory a…
Robustly Learning a Gaussian: Getting Optimal Error, Efficiently
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane +3
We study the fundamental problem of learning the parameters of a high-dimensional Gaussian in the presence of noise -- where an -fraction of our samples were chosen by…
Being Robust (in High Dimensions) Can Be Practical
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane +3
Robust estimation is much more challenging in high dimensions than it is in one dimension: Most techniques either lead to intractable optimization problems or estimators that can t…
Robust Sparse Estimation Tasks in High Dimensions
Jerry Li
In this paper we initiate the study of whether or not sparse estimation tasks can be performed efficiently in high dimensions, in the robust setting where an $\eps$-fraction of sam…