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Amirhesam Abedsoltan
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2020 – today
- 2024
- [c3]Amirhesam Abedsoltan, Parthe Pandit, Luis Rademacher, Mikhail Belkin:
On the Nyström Approximation for Preconditioning in Kernel Machines. AISTATS 2024: 3718-3726 - [i5]Amirhesam Abedsoltan, Adityanarayanan Radhakrishnan, Jingfeng Wu, Mikhail Belkin:
Context-Scaling versus Task-Scaling in In-Context Learning. CoRR abs/2410.12783 (2024) - 2023
- [c2]Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit:
Toward Large Kernel Models. ICML 2023: 61-78 - [i4]Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit:
Toward Large Kernel Models. CoRR abs/2302.02605 (2023) - [i3]Chaoyue Liu, Amirhesam Abedsoltan, Mikhail Belkin:
On Emergence of Clean-Priority Learning in Early Stopped Neural Networks. CoRR abs/2306.02533 (2023) - [i2]Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit, Luis Rademacher:
On the Nystrom Approximation for Preconditioning in Kernel Machines. CoRR abs/2312.03311 (2023) - 2022
- [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 - [i1]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)
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