We consider the Sherrington-Kirkpatrick model of spin glasses at high-temperature and no
external field, and study the problem of sampling from the Gibbs distribution μ in polynomial …

We consider the problem of finding nearly optimal solutions of optimization problems with
random objective functions. Such problems arise widely in the theory of random graphs …

We study the algorithmic thresholds for principal component analysis of Gaussian k-tensors
with a planted rank-one spike, via Langevin dynamics and gradient descent. In order to …

We consider Ising mixed $ p $-spin glasses at high-temperature and without external field,
and study the problem of sampling from the Gibbs distribution $\mu $ in polynomial time. We …

We focus on spherical spin glasses whose Parisi distribution has support of the form [0, q].
For such models we construct paths from the origin to the sphere that consistently remain …

We study Glauber dynamics for sampling from discrete distributions μ on the hypercube {±1}
n. Recently, techniques based on spectral independence have successfully yielded optimal …

We present a quantum algorithm that has rigorous runtime guarantees for several families of
binary optimization problems, including Quadratic Unconstrained Binary Optimization …

We study the landscapes of multi-species spherical spin glasses. Our results determine the
phase boundary for annealed trivialization of the number of critical points, and establish its …

We consider the problem of finding nearly optimal solutions of optimization problems with
random objective functions. Such problems arise widely in the theory of random graphs …

We present a very simple proof that the O (n) model satisfies a uniform logarithmic Sobolev
inequality (LSI) if the difference between the largest and the smallest eigenvalue of the …