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Found 6 papers, 2 papers with code
Are Graph Neural Networks Optimal Approximation Algorithms?
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).
Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems
In this work we give a new approach to learning mixtures of linear dynamical systems that is based on tensor decompositions.
A New Approach to Learning Linear Dynamical Systems
Linear dynamical systems are the foundational statistical model upon which control theory is built.
Kalman Filtering with Adversarial Corruptions
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.
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination
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.
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability
In particular, we study the problem of learning halfspaces under Massart noise with rate $\eta$.
