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)

Toward Large Kernel Models. - Amirhesam Abedsoltan; Mikhail Belkin; Parthe Pandit (ICML, 2023)

On Emergence of Clean-Priority Learning in Early Stopped Neural Networks. - Chaoyue Liu 001; Amirhesam Abedsoltan; Mikhail Belkin (CoRR, 2023)

On the Nyström Approximation for Preconditioning in Kernel Machines. - Amirhesam Abedsoltan; Parthe Pandit; Luis Rademacher; Mikhail Belkin (AISTATS, 2024)

Uncertainty Estimation with Recursive Feature Machines. - Daniel Gedon; Amirhesam Abedsoltan; Thomas B. Schön; Mikhail Belkin (UAI, 2024)

Context-Scaling versus Task-Scaling in In-Context Learning. - Amirhesam Abedsoltan; Adityanarayanan Radhakrishnan; Jingfeng Wu; Mikhail Belkin (CoRR, 2024)

Fast training of large kernel models with delayed projections. - Amirhesam Abedsoltan; Siyuan Ma; Parthe Pandit; Mikhail Belkin (CoRR, 2024)

Task Generalization With AutoRegressive Compositional Structure: Can Learning From D Tasks Generalize to DT Tasks? - Amirhesam Abedsoltan; Huaqing Zhang 005; Kaiyue Wen; Hongzhou Lin; Jingzhao Zhang; Mikhail Belkin (CoRR, 2025)