The stochastic block model (SBM) is a random graph model with planted clusters. It is widely
employed as a canonical model to study clustering and community detection, and provides …

Many questions of fundamental interest in today's science can be formulated as inference
problems: some partial, or noisy, observations are performed over a set of variables and the …

How do statistical dependencies in measurement noise influence high-dimensional
inference? To answer this, we study the paradigmatic spiked matrix model of principal …

Over the last decade or so, Approximate Message Passing (AMP) algorithms have become
extremely popular in various structured high-dimensional statistical problems. Although the …

Recent years witnessed the development of powerful generative models based on flows,
diffusion, or autoregressive neural networks, achieving remarkable success in generating …

We consider the high-dimensional inference problem where the signal is a low-rank
symmetric matrix which is corrupted by an additive Gaussian noise. Given a probabilistic …

In regression settings where explanatory variables have very low correlations and there are
relatively few effects, each of large magnitude, we expect the Lasso to find the important …

Approximate Message Passing algorithms for rotationally invariant matrices Page 1 The
Annals of Statistics 2022, Vol. 50, No. 1, 197–224 https://doi.org/10.1214/21-AOS2101 © …

Factorizing low-rank matrices has many applications in machine learning and statistics. For
probabilistic models in the Bayes optimal setting, a general expression for the mutual …

We study planted problems-finding hidden structures in random noisy inputs-through the
lens of the sum-of-squares semidefinite programming hierarchy (SoS). This family of …