We revisit the well-studied problem of learning a linear combination of ...
Recent works have shown that diffusion models can learn essentially any
...
We provide the first polynomial-time convergence guarantees for the
prob...
We consider the well-studied problem of learning a linear combination of...
We develop a framework for non-asymptotic analysis of deterministic samp...
We present an efficient machine learning (ML) algorithm for predicting a...
The recent proliferation of NISQ devices has made it imperative to under...
We provide theoretical convergence guarantees for score-based generative...
We consider the classic question of state tomography: given copies of an...
Motivated by the recent empirical successes of deep generative models, w...
We consider the problem of quantum state certification, where we are giv...
We consider the problem of learning high dimensional polynomial
transfor...
We give superpolynomial statistical query (SQ) lower bounds for learning...
Arguably the most fundamental question in the theory of generative
adver...
Quantum technology has the potential to revolutionize how we acquire and...
Here we revisit the classic problem of linear quadratic estimation, i.e....
We study the power of quantum memory for learning properties of quantum
...
We prove that given the ability to make entangled measurements on at mos...
Model extraction attacks have renewed interest in the classic problem of...
We revisit the basic problem of quantum state certification: given copie...
In this work we examine the security of InstaHide, a recently proposed s...
In this work, we examine the security of InstaHide, a scheme recently
pr...
In this work we revisit two classic high-dimensional online learning
pro...
We consider the problem of learning an unknown ReLU network with respect...
In this paper we revisit some classic problems on classification under
m...
Polynomial regression is a basic primitive in learning and statistics. I...
There has been a surge of progress in recent years in developing algorit...
For more than a century and a half it has been widely-believed (but was ...
We revisit the problem of learning from untrusted batches introduced by ...
We consider the problem of learning a mixture of linear regressions (MLR...
We study the problem, introduced by Qiao and Valiant, of learning from
u...
A well-known conjecture in computer science and statistical physics is t...
Here we study the problem of sampling random proper colorings of a bound...
Learning mixtures of k binary product distributions is a central problem...