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P. Lopatto

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Preprint Sep 2026

Optimal central limit theorem for bounded random variables in high dimensions

Let $W=n^{-1/2}\sum_{i=1}^n X_i$, where the $X_i$ are independent centered random vectors in ${\mathbb R}^p$ with $|X_{ij}|\le B$ almost surely. Suppose that $\text{Cov}(W)$ has unit diagonal and smallest eigenvalue at least $b^2>0$. We prove that the distance between $W$ and a Gaussian vector with the same covariance,...

P. M. Aronow, Patrick Lopatto · 0 citations
#machine learning Preprint Sep 2026

A positive resolution of the gap-entropy conjecture

We prove the gap-entropy conjecture for fixed-confidence best-arm identification with independent unit-variance Gaussian arms, means in $[0,1]$, and a unique optimal arm. For each suboptimal arm $i$, let $\Delta_i=\mu_*-\mu_i$ be its gap from the optimal mean, and write $H=\sum_{i\ne *}\Delta_i^{-2}$. Let $p_r$ be the...

P. M. Aronow, Nathan Kallus, Patrick Lopatto · 1 citation
Preprint Aug 2026

Adaptive Confidence Sets for Binary Regression without Design Smoothness

We study honest adaptive confidence sets for the regression function in random-design binary regression under $L^2(dx)$ loss. Assuming only known bounds $0<c\leq g\leq C<\infty$ on the unknown design density, we construct asymptotically honest, rate-adaptive confidence sets without requiring $g$ to be smooth. Full adap...

P. M. Aronow, Patrick Lopatto · 0 citations
Preprint Jul 2026

On Rates Attainable under Random Design: A Negative Answer to a Problem of Robins

We give a negative answer to a problem posed by James Robins on estimating a constant conditional variance in nonparametric regression under random design. For every $s>1$ and integer $d>4s$, when the regression function is $s$-H\"older, the unknown design density is bounded above and away from zero, and the conditiona...

P. Aronow, P. Lopatto · 0 citations

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