Sparse Grid Discretizations based on a Discontinuous Galerkin Method. - Alexander B. Atanasov; Erik Schnetter (CoRR, 2017)

Neural Networks as Kernel Learners: The Silent Alignment Effect. - Alexander B. Atanasov; Blake Bordelon; Cengiz Pehlevan (ICLR, 2022)

The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes. - Alexander B. Atanasov; Blake Bordelon; Sabarish Sainathan; Cengiz Pehlevan (ICLR, 2023)

Feature-Learning Networks Are Consistent Across Widths At Realistic Scales. - Nikhil Vyas 001; Alexander B. Atanasov; Blake Bordelon; Depen Morwani; Sabarish Sainathan; Cengiz Pehlevan (NeurIPS, 2023)

A Dynamical Model of Neural Scaling Laws. - Blake Bordelon; Alexander B. Atanasov; Cengiz Pehlevan (ICML, 2024)

Scaling and renormalization in high-dimensional regression. - Alexander B. Atanasov; Jacob A. Zavatone-Veth; Cengiz Pehlevan (CoRR, 2024)

Risk and cross validation in ridge regression with correlated samples. - Alexander B. Atanasov; Jacob A. Zavatone-Veth; Cengiz Pehlevan (CoRR, 2024)

The Optimization Landscape of SGD Across the Feature Learning Strength. - Alexander B. Atanasov; Alexandru Meterez; James B. Simon; Cengiz Pehlevan (ICLR, 2025)

How Feature Learning Can Improve Neural Scaling Laws. - Blake Bordelon; Alexander B. Atanasov; Cengiz Pehlevan (ICLR, 2025)

Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in Linear Models. - Alexander B. Atanasov; Blake Bordelon; Jacob A. Zavatone-Veth; Courtney Paquette; Cengiz Pehlevan (CoRR, 2025)