We consider the problem (P) of fitting n standard Gaussian
random vector...
When can the input of a ReLU neural network be inferred from its output?...
We exhibit examples of high-dimensional unimodal posterior distributions...
In this expository note, we discuss an early partial coloring result of ...
Many high-dimensional statistical inference problems are believed to pos...
Semidefinite programming is an important tool to tackle several problems...
Non-adaptive group testing refers to the problem of inferring a sparse s...
Montanari and Richard (2015) asked whether a natural semidefinite progra...
We study the problem of efficiently refuting the k-colorability of a gra...
In compressed sensing, the restricted isometry property (RIP) on M × N
s...
We study statistical and computational limits of clustering when the mea...
This note explores the applicability of unsupervised machine learning
te...
We show that, if W∈R^N × N_sym is
drawn from the gaussian orthogonal ens...
These notes survey and explore an emerging method, which we call the
low...
We study the computational cost of recovering a unit-norm sparse princip...
Given a random n × n symmetric matrix W drawn from the
Gaussian orthogo...
We study the problem of community detection in a random hypergraph model...
A central problem of random matrix theory is to understand the eigenvalu...
In these notes we describe heuristics to predict computational-to-statis...
Motivated by geometric problems in signal processing, computer vision, a...
Many inverse problems are formulated as optimization problems over certa...
We study the statistical limits of both detecting and estimating a rank-...
The Gromov-Hausdorff distance provides a metric on the set of isometry
c...
Various alignment problems arising in cryo-electron microscopy, communit...
A central problem of random matrix theory is to understand the eigenvalu...
We consider the problem of identifying underlying community-like structu...
We have observed an interesting, yet unexplained, phenomenon: Semidefini...