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James B. Simon
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2020 – today
- 2023
- [c4]James B. Simon, Maksis Knutins, Ziyin Liu, Daniel Geisz, Abraham J. Fetterman, Joshua Albrecht:
On the Stepwise Nature of Self-Supervised Learning. ICML 2023: 31852-31876 - [i11]James B. Simon, Maksis Knutins, Ziyin Liu, Daniel Geisz, Abraham J. Fetterman, Joshua Albrecht:
On the stepwise nature of self-supervised learning. CoRR abs/2303.15438 (2023) - [i10]Abraham J. Fetterman, Ellie Kitanidis, Joshua Albrecht, Zachary Polizzi, Bryden Fogelman, Maksis Knutins, Bartosz Wróblewski, James B. Simon, Kanjun Qiu:
Tune As You Scale: Hyperparameter Optimization For Compute Efficient Training. CoRR abs/2306.08055 (2023) - [i9]Lijia Zhou, James B. Simon, Gal Vardi, Nathan Srebro:
An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression. CoRR abs/2306.13185 (2023) - [i8]Yasaman Bahri, Boris Hanin, Antonin Brossollet, Vittorio Erba, Christian Keup, Rosalba Pacelli, James B. Simon:
Les Houches Lectures on Deep Learning at Large & Infinite Width. CoRR abs/2309.01592 (2023) - [i7]Greg Yang, James B. Simon, Jeremy Bernstein:
A Spectral Condition for Feature Learning. CoRR abs/2310.17813 (2023) - [i6]James B. Simon, Dhruva Karkada, Nikhil Ghosh, Mikhail Belkin:
More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory. CoRR abs/2311.14646 (2023) - 2022
- [c3]Ziyin Liu, Botao Li, James B. Simon, Masahito Ueda:
SGD Can Converge to Local Maxima. ICLR 2022 - [c2]James Benjamin Simon, Sajant Anand, Michael Robert DeWeese:
Reverse Engineering the Neural Tangent Kernel. ICML 2022: 20215-20231 - [c1]Neil Mallinar, James B. Simon, Amirhesam Abedsoltan, Parthe Pandit, Misha Belkin, Preetum Nakkiran:
Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting. NeurIPS 2022 - [i5]Neil Mallinar, James B. Simon, Amirhesam Abedsoltan, Parthe Pandit, Mikhail Belkin, Preetum Nakkiran:
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting. CoRR abs/2207.06569 (2022) - [i4]James B. Simon:
On Kernel Regression with Data-Dependent Kernels. CoRR abs/2209.01691 (2022) - [i3]Joshua Albrecht, Abraham J. Fetterman, Bryden Fogelman, Ellie Kitanidis, Bartosz Wróblewski, Nicole Seo, Michael Rosenthal, Maksis Knutins, Zachary Polizzi, James B. Simon, Kanjun Qiu:
Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds. CoRR abs/2210.13417 (2022) - 2021
- [i2]James B. Simon, Sajant Anand, Michael Robert DeWeese:
Reverse Engineering the Neural Tangent Kernel. CoRR abs/2106.03186 (2021) - [i1]James B. Simon, Madeline Dickens, Michael Robert DeWeese:
Neural Tangent Kernel Eigenvalues Accurately Predict Generalization. CoRR abs/2110.03922 (2021)
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