1 code implementation • 1 Oct 2023 • Morris Yau, Eric Lu, Nikolaos Karalias, Jessica Xu, Stefanie Jegelka
In this work we design graph neural network architectures that capture optimal approximation algorithms for a large class of combinatorial optimization problems, using powerful algorithmic tools from semidefinite programming (SDP).
no code implementations • 13 Jul 2023 • Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau
In this work we give a new approach to learning mixtures of linear dynamical systems that is based on tensor decompositions.
no code implementations • 23 Jan 2023 • Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau
Linear dynamical systems are the foundational statistical model upon which control theory is built.
no code implementations • 11 Nov 2021 • Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
In a pioneering work, Schick and Mitter gave provable guarantees when the measurement noise is a known infinitesimal perturbation of a Gaussian and raised the important question of whether one can get similar guarantees for large and unknown perturbations.
no code implementations • 8 Oct 2020 • Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Our approach is based on a novel alternating minimization scheme that interleaves ordinary least-squares with a simple convex program that finds the optimal reweighting of the distribution under a spectral constraint.
1 code implementation • 8 Jun 2020 • Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
In particular, we study the problem of learning halfspaces under Massart noise with rate $\eta$.