We study the power of randomness in the Number-on-Forehead (NOF) model i...
Sparse linear regression is a central problem in high-dimensional statis...
We consider the well-studied problem of learning a linear combination of...
We give a simple proof of the matrix Spencer conjecture up to
poly-logar...
The well-known Komlós conjecture states that given n vectors in
ℝ^d with...
Sparse linear regression with ill-conditioned Gaussian random designs is...
We give superpolynomial statistical query (SQ) lower bounds for learning...
Arguably the most fundamental question in the theory of generative
adver...
A well-known result of Banaszczyk in discrepancy theory concerns the pre...
Model extraction attacks have renewed interest in the classic problem of...
Sparse linear regression is a fundamental problem in high-dimensional
st...
A polynomial threshold function (PTF) f:ℝ^n →ℝ
is a function of the form...
We consider the problem of learning an unknown ReLU network with respect...
In the stochastic online vector balancing problem, vectors
v_1,v_2,…,v_T...
Polynomial regression is a basic primitive in learning and statistics. I...
Motivated by problems in controlled experiments, we study the discrepanc...
We identify a new notion of pseudorandomness for randomness sources, whi...
Gaussian Graphical Models (GGMs) have wide-ranging applications in machi...
Motivated by the celebrated Beck-Fiala conjecture, we consider the rando...
We construct pseudorandom generators of seed length Õ((n)·(1/ϵ)) that ϵ-...
We give the first polynomial-time algorithm for performing linear or
pol...
We give the first provably efficient algorithm for learning a one hidden...