Amir Yehudayoff

collaborators

5 papers

math.MG2026

A simplex-based measure of symmetry

Egor Bakaev, Amir Yehudayoff

For compact convex sets $L, K \subset R^{n}$ , denote by $I^»_{K} (L)$ the smallest size of a homothet of $K$ that contains $L$ . We define a measure of symmetry based on the $n$ -s…

math.MG2026

A note on the affine plank conjecture

Egor Bakaev, Amir Yehudayoff

In 1951, Bang posed the affine plank conjecture, which remains open: If a convex body in $R^{d}$ is covered by planks, then the total relative width of the planks is at leas…

cs.LG2026

Better Neural Network Expressivity: Subdividing the Simplex

Egor Bakaev, Florestan Brunck, Christoph Hertrich +2

This work studies the expressivity of ReLU neural networks with a focus on their depth. A sequence of previous works showed that $⌈ lo g_{2} (n + 1)⌉$ hidden layers are suffi…

math.MG2025

Approximation Depth of Convex Polytopes

Egor Bakaev, Florestan Brunck, Amir Yehudayoff

We study approximations of polytopes in the standard model for computing polytopes using Minkowski sums and (convex hulls of) unions. Specifically, we study the ability to approxim…

cs.LG2025

On the Depth of Monotone ReLU Neural Networks and ICNNs

Egor Bakaev, Florestan Brunck, Christoph Hertrich +2

We study two models of ReLU neural networks: monotone networks (ReLU $^{+}$ ) and input convex neural networks (ICNN). Our focus is on expressivity, mostly in terms of depth, and we pr…