We demonstrate the first algorithms for the problem of regression for
ge...
We study the problem of list-decodable sparse mean estimation. Specifica...
We study the problem of high-dimensional sparse mean estimation in the
p...
This work tackles the issue of fairness in the context of generative
pro...
We characterize the measurement complexity of compressed sensing of sign...
We consider the problem of distribution-free learning for Boolean functi...
We prove the first superpolynomial lower bounds for learning one-layer n...
We consider the fundamental problem of ReLU regression, where the goal i...
We study the efficient learnability of high-dimensional Gaussian mixture...
The goal of compressed sensing is to learn a structured signal x from a
...
We study high-dimensional sparse estimation tasks in a robust setting wh...
We consider the problem of computing the best-fitting ReLU with respect ...
We give the first polynomial-time algorithm for robust regression in the...
The Fourier-Entropy Influence (FEI) Conjecture states that for any Boole...
We present a simple and effective algorithm for the problem of sparse
ro...