Separation of scales and a thermodynamic description of feature learning in some CNNs. - Inbar Seroussi; Zohar Ringel (CoRR, 2021)

Lower Bounds on the Generalization Error of Nonlinear Learning Models. - Inbar Seroussi; Ofer Zeitouni (IEEE Trans. Inf. Theory, 2022)

Speed Limits for Deep Learning. - Inbar Seroussi; Alexander A. Alemi; Moritz Helias; Zohar Ringel (CoRR, 2023)

Hitting the High-Dimensional Notes: An ODE for SGD learning dynamics on GLMs and multi-index models. - Elizabeth Collins-Woodfin; Courtney Paquette; Elliot Paquette; Inbar Seroussi (CoRR, 2023)

Droplets of Good Representations: Grokking as a First Order Phase Transition in Two Layer Networks. - Noa Rubin; Inbar Seroussi; Zohar Ringel (CoRR, 2023)

Grokking as a First Order Phase Transition in Two Layer Networks. - Noa Rubin; Inbar Seroussi; Zohar Ringel (ICLR, 2024)

The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms. - Elizabeth Collins-Woodfin; Inbar Seroussi; Begoña García Malaxechebarría; Andrew W. Mackenzie; Elliot Paquette; Courtney Paquette (NeurIPS, 2024)

Spectral-bias and kernel-task alignment in physically informed neural networks. - Inbar Seroussi; Asaf Miron; Zohar Ringel (Mach. Learn. Sci. Technol., 2024)

From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning. - Noa Rubin; Kirsten Fischer; Javed Lindner; David Dahmen; Inbar Seroussi; Zohar Ringel; Michael Krämer; Moritz Helias (CoRR, 2025)

Applications of Statistical Field Theory in Deep Learning. - Zohar Ringel; Noa Rubin; Edo Mor; Moritz Helias; Inbar Seroussi (CoRR, 2025)

Better Rates for Private Linear Regression in the Proportional Regime via Aggressive Clipping. - Simone Bombari; Inbar Seroussi; Marco Mondelli (CoRR, 2025)