Damek Davis

collaborators

11 papers

stat.ML2026

Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models

Libin Zhu, Damek Davis, Dmitriy Drusvyatskiy +1

We study a prototypical situation when a learned predictor can discover useful low-dimensional structure in data, while using fewer samples than are needed for accurate prediction.…

math.CO2026

Forbidden subgraphs in divisor graphs and an ErdÅs divisibility problem

Damek Davis

ErdÅs asked for the largest size $f (n)$ of a subset of ${1, \dots, n}$ with no element dividing two others. We show that $f (n) = c_{2} n + o (n)$ for an effectively computable constant…

math.OC2026

A short proof of near-linear convergence of adaptive gradient descent under fourth-order growth and convexity

Damek Davis, Dmitriy Drusvyatskiy

Davis, Drusvyatskiy, and Jiang showed that gradient descent with an adaptive stepsize converges locally at a nearly-linear rate for smooth functions that grow at least quartically…

math.PR2026

The sharp one-dimensional convex sub-Gaussian comparison constant

Damek Davis, Sam Power

Let $X$ be an integrable real random variable with mean zero and two-sided sub-Gaussian tail $P (∣ X ∣ > t) \leq 2 e^{- t^{2} /2}$ for all $t \geq 0$ . We determine the smallest consta…

math.PR2026

Counting partial Hadamard matrices in the cubic regime

Damek Davis

We give a precise asymptotic formula for the number of $n \times 4 t$ partial Hadamard matrices in the regimes $t / n^{3} \to \infty$ and $t / n^{3} \to I^$ for sufficiently large fixed $I^$ . Th…

cs.LG2026

When do spectral gradient updates help in deep learning?

Damek Davis, Dmitriy Drusvyatskiy

Spectral gradient methods, such as the recently popularized Muon optimizer, are a promising alternative to standard Euclidean gradient descent for training deep neural networks and…

Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models

Forbidden subgraphs in divisor graphs and an ErdÅs divisibility problem

A short proof of near-linear convergence of adaptive gradient descent under fourth-order growth and convexity

The sharp one-dimensional convex sub-Gaussian comparison constant

Counting partial Hadamard matrices in the cubic regime

When do spectral gradient updates help in deep learning?

Forbidden subgraphs in divisor graphs and an ErdÅs divisibility problem