Neural Tangent Kernel Eigenvalues Accurately Predict Generalization. - James B. Simon; Madeline Dickens; Michael Robert DeWeese (CoRR, 2021)

SGD Can Converge to Local Maxima. - Liu Ziyin 001; Botao Li; James B. Simon; Masahito Ueda (ICLR, 2022)

Reverse Engineering the Neural Tangent Kernel. - James Benjamin Simon; Sajant Anand; Michael Robert DeWeese (ICML, 2022)

Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting. - Neil Mallinar; James B. Simon; Amirhesam Abedsoltan; Parthe Pandit; Mikhail Belkin; Preetum Nakkiran (NeurIPS, 2022)

Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting. - Neil Mallinar; James B. Simon; Amirhesam Abedsoltan; Parthe Pandit; Mikhail Belkin; Preetum Nakkiran (CoRR, 2022)

On Kernel Regression with Data-Dependent Kernels. - James B. Simon (CoRR, 2022)

Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds. - Joshua Albrecht; Abraham J. Fetterman; Bryden Fogelman; Ellie Kitanidis; Bartosz Wróblewski; Nicole Seo; Michael Rosenthal; Maksis Knutins; Zachary Polizzi; James B. Simon; Kanjun Qiu (CoRR, 2022)

On the Stepwise Nature of Self-Supervised Learning. - James B. Simon; Maksis Knutins; Liu Ziyin 001; Daniel Geisz; Abraham J. Fetterman; Joshua Albrecht (ICML, 2023)

Tune As You Scale: Hyperparameter Optimization For Compute Efficient Training. - Abraham J. Fetterman; Ellie Kitanidis; Joshua Albrecht; Zachary Polizzi; Bryden Fogelman; Maksis Knutins; Bartosz Wróblewski; James B. Simon; Kanjun Qiu (CoRR, 2023)

Les Houches Lectures on Deep Learning at Large & Infinite Width. - Yasaman Bahri; Boris Hanin; Antonin Brossollet; Vittorio Erba; Christian Keup; Rosalba Pacelli; James B. Simon (CoRR, 2023)

A Spectral Condition for Feature Learning. - Greg Yang; James B. Simon; Jeremy Bernstein (CoRR, 2023)

More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory. - James B. Simon; Dhruva Karkada; Nikhil Ghosh; Mikhail Belkin (CoRR, 2023)

The Eigenlearning Framework: A Conservation Law Perspective on Kernel Ridge Regression and Wide Neural Networks. - James B. Simon; Madeline Dickens; Dhruva Karkada; Michael Robert DeWeese (Trans. Mach. Learn. Res., 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

More is Better: when Infinite Overparameterization is Optimal and Overfitting is Obligatory. - James B. Simon; Dhruva Karkada; Nikhil Ghosh; Mikhail Belkin (ICLR, 2024)

An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression. - Lijia Zhou; James B. Simon; Gal Vardi; Nathan Srebro (ICLR, 2024)

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

Solvable Dynamics of Self-Supervised Word Embeddings and the Emergence of Analogical Reasoning. - Dhruva Karkada; James B. Simon; Yasaman Bahri; Michael Robert DeWeese (CoRR, 2025)

Saddle-To-Saddle Dynamics in Deep ReLU Networks: Low-Rank Bias in the First Saddle Escape. - Ioannis Bantzis; James B. Simon; Arthur Jacot (CoRR, 2025)

Alternating Gradient Flows: A Theory of Feature Learning in Two-layer Neural Networks. - Daniel Kunin; Giovanni Luca Marchetti; Feng Chen; Dhruva Karkada; James B. Simon; Michael Robert DeWeese; Surya Ganguli; Nina Miolane (CoRR, 2025)

FACT: the Features At Convergence Theorem for neural networks. - Enric Boix-Adserà; Neil Mallinar; James B. Simon; Mikhail Belkin (CoRR, 2025)