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Tomer Milo

Publications and source records attributed to Tomer Milo.

6 recordsLinked to original sources
arXiv2026

Nearly-polynomial inverse theorem for the U^d norm in degree d+1

We prove a nearly polynomial inverse theorem for the Gowers $U^d$ norm, over finite fields of non-small characteristic, for polynomials of degree $d+1$. The case of degree $d$ was very recently settled by Mili\'{c}evi\'{c} and Randelovi\'{c} with a fully polynomial bound. We moreover provide a nearly polynomial inverse theorem for homogeneous polynomials of any degree smaller than $2d$. Our methods may be of independent interest, and include a refined notion of polynomial decomposition that captures correlation with polynomials of lower degree than classical notions do, and a new correlation lemma that improves upon similar lemmas in the literature. Additionally, we illustrate the usefulness of the new correlation lemma by using it to give an alternative proof for the aforementioned result of Mili\'{c}evi\'{c} and Randelovi\'{c}.

math.CO↗
arXiv2025

On the Figiel-Lindenstrauss-Milman inequality

The Figiel-Lindenstrauss-Milman inequality is a fundamental inequality in the combinatorial theory of polytopes. It is classically obtained as a corollary of Milman's version of Dvoretzky's theorem. The goal of this paper is to provide a short and elementary proof of this inequality, derive more general versions of it, and discuss its tightness, where much is not known.

math.MG↗
arXiv2025

A very short proof of the Figiel-Lindenstrauss-Milman theorem

We provide a short proof for the Figiel, Lindenstrauss and Milman inequality regarding the number of vertices and faces of certain polytope, with an explicit bound on the universal constant involved. The proof is completely elementary and avoids any form of Dvoretzky's theorem, as well as the spherical isoperimetric inequality.

math.MG↗
arXiv2024

On the Many Faces of Easily Covered Polytopes

Assume that $rB_{2}^{n} \subset P$ for some polytope $P \subset \mathbb{R}^n$, where $r \in (\frac{1}{2},1]$. Denote by $\mathcal{F}$ the set of facets of $P$, and by $N=N(P,B_2^n)$ the covering number of $P$ by the Euclidean unit ball $B_2^n$. We prove that if $\log N \le\frac{n}{8}$, then \[ |\mathcal{F}| \ge \left( \frac{1}{ 2\left(1 - r \sqrt{1-\frac{4\log N}{n}}\right) } \right)^{\frac{n-1}{2}}. \]

math.MG↗
arXiv2024

Robust CATE Estimation Using Novel Ensemble Methods

The estimation of Conditional Average Treatment Effects (CATE) is crucial for understanding the heterogeneity of treatment effects in clinical trials. We evaluate the performance of common methods, including causal forests and various meta-learners, across a diverse set of scenarios, revealing that each of the methods struggles in one or more of the tested scenarios. Given the inherent uncertainty of the data-generating process in real-life scenarios, the robustness of a CATE estimator to various scenarios is critical for its reliability. To address this limitation of existing methods, we propose two new ensemble methods that integrate multiple estimators to enhance prediction stability and performance - Stacked X-Learner which uses the X-Learner with model stacking for estimating the nuisance functions, and Consensus Based Averaging (CBA), which averages only the models with highest internal agreement. We show that these models achieve good performance across a wide range of scenarios varying in complexity, sample size and structure of the underlying-mechanism, including a biologically driven model for PD-L1 inhibition pathway for cancer treatment. Furthermore, we demonstrate improved performance by the Stacked X-Learner also when comparing to other ensemble methods, including R-Stacking, Causal-Stacking and others.

stat.ME↗