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ORCIDDaniel Beaglehole
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2020 – today
- 2024
[i9]Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala:
Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks. CoRR abs/2402.05271 (2024)- [i8]Daniel Beaglehole, Peter Súkeník, Marco Mondelli, Mikhail Belkin:
Average gradient outer product as a mechanism for deep neural collapse. CoRR abs/2402.13728 (2024) - [i7]Thomas Valles, Daniel Beaglehole:
Fast, optimal, and dynamic electoral campaign budgeting by a generalized Colonel Blotto game. CoRR abs/2406.15714 (2024) - [i6]Neil Mallinar, Daniel Beaglehole, Libin Zhu, Adityanarayanan Radhakrishnan, Parthe Pandit, Mikhail Belkin:
Emergence in non-neural models: grokking modular arithmetic via average gradient outer product. CoRR abs/2407.20199 (2024) - 2023
- [j1]Daniel Beaglehole, Mikhail Belkin, Parthe Pandit:
On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions. SIAM J. Math. Data Sci. 5(4): 854-872 (2023) - [c2]Daniel Beaglehole, Max Hopkins, Daniel Kane, Sihan Liu, Shachar Lovett:
Sampling Equilibria: Fast No-Regret Learning in Structured Games. SODA 2023: 3817-3855 - [i5]Daniel Beaglehole, Adityanarayanan Radhakrishnan, Parthe Pandit, Mikhail Belkin:
Mechanism of feature learning in convolutional neural networks. CoRR abs/2309.00570 (2023) - 2022
- [c1]Alexandr Andoni, Daniel Beaglehole:
Learning to Hash Robustly, Guaranteed. ICML 2022: 599-618 - [i4]Daniel Beaglehole:
An Efficient Approximation Algorithm for the Colonel Blotto Game. CoRR abs/2201.10758 (2022) - [i3]Daniel Beaglehole, Mikhail Belkin, Parthe Pandit:
Kernel Ridgeless Regression is Inconsistent for Low Dimensions. CoRR abs/2205.13525 (2022) - [i2]Adityanarayanan Radhakrishnan, Daniel Beaglehole, Parthe Pandit, Mikhail Belkin:
Feature learning in neural networks and kernel machines that recursively learn features. CoRR abs/2212.13881 (2022) - 2021
- [i1]Alexandr Andoni, Daniel Beaglehole:
Learning to Hash Robustly, with Guarantees. CoRR abs/2108.05433 (2021)
Coauthor Index
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