Abstract

Let A be and n by n matrix with iid entries which take 1 with probability p and 0 otherwise. In this talk we will be interested in the distribution of the complex eigenvalues of A as n tends to infinity. Due to an impressive sequence of results, today we know that whenever pn tends to infinity and A is appropriately normalized, the limiting spectral distribution tends to the ""circular law"" and n tends to infinity. However the situation for p = d/n, d fixed, has remained somewhat more mysterious and, in fact, it was not even known if a deterministic limiting law even exists. In this talk I will discuss the recent resolution of this problem, showing that such random matrices have a deterministic limiting spectral distribution. This talk is based on joint work with Ashwin Sah and Mehtaab Sawhney.

Abstract

We consider the matrix completion problem in the very sparse regime where, on average, a constant number of entries of the matrix are observed per row (or column). In this very sparse regime, we cannot expect to have perfect recovery and the celebrated nuclear norm based matrix completion fails because the singular value decomposition (SVD) of the underlying very sparse matrix completely breaks down. We demonstrate that it is indeed possible to reliably recover the matrix. The key idea is the use of a ""randomized asymmetric SVD"" to find informative singular vectors in this regime in a way that the SVD cannot. We provide sharp theoretical analysis of the phenomenon, the lower limits of statistical recovery and demonstrate the efficacy of the new method using simulations.

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Abstract

We study the Glauber dynamics for sampling from distributions on the n-dimensional discrete Boolean hypercube. Techniques based on the powerful recent notion of spectral independence have successfully yielded optimal O(n) relaxation times for a host of different distributions. We show that spectral independence is universal: a relaxation time of O(n) implies spectral independence. As an application, we obtain approximate tensorization of entropy and the optimal mixing time for the classically studied mixed p-spin model at sufficiently high temperature. Joint work with Nima Anari, Frederic Koehler, Huy Tuan Pham, and Thuy-Duong Vuong.

Abstract

Many properties of a graph can be deduced from the eigenvalues and eigenvectors of its Laplacian matrix. Because of this, the Laplacian matrix plays a key role in spectral graph theory and is used in algorithms for graph clustering, network control theory, and combinatorial optimization. This talk focuses on the behavior of the extreme eigenvalues of a random Laplacian matrix with Gaussian entries. We show that, after shifting and scaling, the largest eigenvalue converges to a Gumbel distribution as the size of the matrix tends to infinity. The proof uses techniques from random matrix theory and free probability. This talk is based on joint work with Andrew Campbell and Kyle Luh.

Abstract

Matrix perturbation theory is a foundational subject across multiple disciplines, including probability, statistics, machine learning, and applied mathematics. Perturbation bounds play a critical role in quantifying the impact of small noise on matrix spectral parameters in various applications such as matrix completion, principal component analysis, and community detection. In this talk, we focus on the additive perturbation model, where a low-rank data matrix is perturbed by Gaussian noise. We provide a comprehensive analysis of the singular subspaces, extending the classical Davis-Kahan-Wedin theorem and offering fine-grained analysis of the singular vector matrices. Also, we will show the practical implications of our perturbation results, specifically highlighting their application in Gaussian mixture models. The talk is based on joint works with Sean O'Rourke and Van Vu.

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In applications, we often need perturbation bounds for the leading spectral parameters of a matrix, such as the leading eigenvectors, eigenvalues, eigensubspaces or low rank approximation. In this talk, we are going to discuss a new kind of perturbation bounds, which would improve significantly classical bounds such as Davis-Kahan or Weyl, given that the spectrum of the truth matrix decays in a reasonable manner. (based on joined works with S. O'rourke, K. Wang, P. Tran)

Abstract

Using the sample covariance to estimate the principal components of a hidden covariance matrix is a central problem in statistics and data science. We view this as a perturbation problem where the noise is a zero mean random matrix which is the difference between the sample covariance matrix and the truth. In certain special settings, it can also be viewed as a deformed random matrix model. In this talk, using recent progress on matrices with random perturbation, we derive new estimates, which extend and improve several well known results in the field. (join work with V. Vu)

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Let J be a mean 0, variance 1 random variable with subGaussian tails. Let M be an n by n matrix with iid entries J and G be an n by n matrix with iid standard normal entries. We prove that for all nonnegative x that P[s_n(M)<x/n^{1/2}] = (1+o(1))P[s_n(G)<x/n^{1/2}] + O(e^{-cn}). This resolves up to a (1+o(1)) a conjecture of Spielman and Teng regarding the distribution of the least singular value. This furthermore unifies works of Tao and Vu (on the distribution of the least singular value) and Rudelson and Vershynin (which proved the conjecture up to an absolute constant). Joint with Ashwin Sah and Julian Sahasrabudhe

Abstract

One way to understand the concentration of the norm of a random matrix X with Gaussian entries is to apply a standard concentration inequality, such as the one for Lipschitz functions of i.i.d. standard Gaussian variables, which yields subgaussian tail bounds on the norm of X. However, as was shown by Tracy and Widom in 1990s, when the entries of X are i.i.d. the norm of X exhibits even sharper concentration. The phenomenon of a function of many i.i.d. variables having strictly smaller tails than those predicted by classical concentration inequalities is sometimes referred to as «superconcentration», a term originally dubbed by Chatterjee. I will discuss novel results that can be interpreted as superconcentration inequalities for the norm of X, where X is a Gaussian random matrix with independent entries and an arbitrary variance profile. We can also view our results as a nonhomogeneous extension of Tracy-Widom-type upper tail estimates for the norm of X.

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Abstract

We study the asymptotic spectral distribution and resolvent of a n^2-by-n^2 Kronecker-product random matrix A x I + I x B + Theta x Xi, when A, B are independent Wigner matrices and Theta, Xi are deterministic and diagonal. For fixed spectral arguments z in C^+, we establish a quantitative approximation for the Stieltjes transform by that of an approximating free operator, and a diagonal deterministic equivalent approximation for the resolvent. We further perform an entrywise analysis of the resolvent, showing that off-diagonal entries fall on two differing scales n^{-1/2} and n^{-1} depending on their locations with respect to the Kronecker structure. Our study is motivated by consideration of a matrix-valued least-squares optimization problem min_{X in R^{n x n}} \|XA+BX\|_F^2 + sum_{ij} Theta_i Xi_j X_{ij}^2 subject to a linear constraint. For random instances of this problem defined by Wigner inputs A and B, our analyses imply an asymptotic characterization of the minimizer X and its associated minimum objective value as in the limit as n -> infinity. This is joint work with Renyuan (Jack) Ma.

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I consider the problem of efficiently solving a system of n non-linear equations in R^d, where each equation is random homogeneous polynomial of arbitrary degrees. In the complex case and for n=d−1, Beltran and Pardo proved the existence of an efficient randomized algorithm and Lairez recently showed it can be de-randomized to produce a deterministic efficient algorithm. Here we consider the real setting, to which previously developed methods do not apply. We describe an algorithm that efficiently finds solutions (with high probability) for n=d−O(sqrt{dlog d}). [Based on joint work with Eliran Subag]

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Nonlinear random matrices have emerged as an important area of interest in statistics and machine learning. In recent years, the study of nonlinear random matrix theory has proven valuable in providing rigorous explanations for empirically observed phenomena in deep learning. From a random matrix perspective, this creates new challenges in handling nonlinearity and dependence. In this talk, I will survey recent developments in the spectral analysis of nonlinear random matrices and their applications. This includes discussions on random feature models, kernel random matrices, and random geometric graphs.

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Abstract

The Ising perceptron is a toy model of a single-layer neural network, which can be viewed as a random constraint satisfaction problem with dense connectivity. The problem requires finding a length-N vector of +1 or -1 entries, such that its product with a random Gaussian M by N matrix is entrywise lower bounded. We work in the regime where both M and N grow large with a fixed ratio M/N = alpha. In this talk, we present some recent findings on the algorithmic aspects of the Ising perceptron. Inspired by the rich literature on discrepancy minimization, we propose an efficient algorithm that improves on previous algorithms (works for higher values of alpha). We also show that at high enough alpha, the Ising perceptron exhibits the multi Overlap Gap Property (m-OGP), which matches our algorithmic threshold up to poly-logarithmic factors. This is based on joint work with Tselil Schramm and Kangjie Zhou.

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Neural networks have become so large that their behaviour can be well approximated by “infinite neural networks”, which are obtained by considering the limit as the number of neurons goes to infinity. However, there are many possible infinite limits one can take! In this talk, I will introduce the "infinite depth-and-width limit", where both the depth and the width are scaled to infinity simultaneously. Mathematically, this limit corresponds to a limit of products of random matrices where *both* the size of each individual matrix and the number of matrices in the product are scaled proportionally.

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Despite their surplus of parameters, modern deep learning models often generalize well, a phenomenon exemplified by the "double descent curve." While this behavior is theoretically grasped for problems such as ridge regression under linear scaling of dimensions, intriguing phenomenon emerge under quadratic scaling, where sample size equals parameter count. In this presentation, we study the eigenvalues of the Neural Tangent Kernel, a matrix model pertinent to wide neural networks trained via gradient descent, within this quadratic regime.

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Principal component regression (PCR) is a classical two-step approach to linear regression, where one first reduces the data dimension by projecting onto its large principal components, and then performs ordinary least squares regression. We study PCR in an asymptotic high-dimensional regression setting, where the number of data points is proportional to the dimension. We derive exact limiting formulas for the estimation and prediction risks, which depend in a complicated manner on the eigenvalues of the population covariance, the alignment between the population PCs and the true signal, and the number of selected PCs. A key challenge is the fact that in this regime, the sample covariance is an inconsistent estimate of its population counterpart, hence sample PCs may fail to fully capture potential latent low-dimensional structure in the data. We demonstrate this point through several case studies, including that of a spiked covariance model. Joint work with Alden Green.

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