7 citations · 28 across the 16 of their papers we have counts for
8 papers · 1 filter
Learning general Gaussian mixtures with efficient score matching
Sitan Chen, Vasilis Kontonis, Kulin Shah
We study the problem of learning mixtures of Gaussians in dimensions. We make no separation assumptions on the underlying mixture components: we only require that the covar…
Learning Mixtures of Gaussians Using the DDPM Objective
Kulin Shah, Sitan Chen, Adam Klivans
Recent works have shown that diffusion models can learn essentially any distribution provided one can perform score estimation. Yet it remains poorly understood under what settings…
Learning Polynomials of Few Relevant Dimensions
Sitan Chen, Raghu Meka
Polynomial regression is a basic primitive in learning and statistics. In its most basic form the goal is to fit a degree polynomial to a response variable in terms of an $…
Algorithmic Foundations for the Diffraction Limit
Sitan Chen, Ankur Moitra
For more than a century and a half it has been widely-believed (but was never rigorously shown) that the physics of diffraction imposes certain fundamental limits on the resolution…
Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments
Sitan Chen, Jerry Li, Zhao Song
We consider the problem of learning a mixture of linear regressions (MLRs). An MLR is specified by nonnegative mixing weights summing to , and unknown…
Efficiently Learning Structured Distributions from Untrusted Batches
Sitan Chen, Jerry Li, Ankur Moitra
We study the problem, introduced by Qiao and Valiant, of learning from untrusted batches. Here, we assume users, all of whom have samples from some underlying distribution …