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20232026
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math.PR2026

TV between Bernoulli products, up to constants

Gleb Smirnov

We obtain upper and lower bounds on the total variation distance between two product Bernoulli measures. The bounds are efficiently computable and match up to an absolute constant…

math.PR2026

MaxCut for Covariances

Gleb Smirnov

Let have a multivariate totally positive () law. We prove that $$ \sum_{i<j}\mathbb{E}\left[\left|\mathrm{Cov}(X_i,X_j \mid X_{[n]\s…

math.PR2026

Random sets are close to low-discrepancy sets

Gleb Smirnov, Roman Vershynin

We show that a random sample from an arbitrary probability measure on is close to a low-discrepancy point set. Namely, after moving only a small fraction of the samp…

math.PR2026

Discrepancy and Fisher information

Gleb Smirnov, Roman Vershynin

We give an online algorithm that keeps a symmetric random walk inside a convex body by discarding some of its steps. The expected number of discarded steps is controlled by a Fishe…

math.PR2025

Thinning to improve two-sample discrepancy

Gleb Smirnov, Roman Vershynin

The discrepancy between two independent samples \(X_1,\dots,X_n\) and \(Y_1,\dots,Y_n\) drawn from the same distribution on typically has order \(O(\sqrt{n})\) even…

math.PR2024

Detecting adversarial attacks on random samples

Gleb Smirnov

This paper studies the problem of detecting adversarial perturbations in a sequence of observations. Given a data sample drawn from a standard normal distributio…