Learning to Hash Robustly, with Guarantees. - Alexandr Andoni; Daniel Beaglehole (CoRR, 2021)

Learning to Hash Robustly, Guaranteed. - Alexandr Andoni; Daniel Beaglehole (ICML, 2022)

An Efficient Approximation Algorithm for the Colonel Blotto Game. - Daniel Beaglehole (CoRR, 2022)

Kernel Ridgeless Regression is Inconsistent for Low Dimensions. - Daniel Beaglehole; Mikhail Belkin; Parthe Pandit (CoRR, 2022)

Feature learning in neural networks and kernel machines that recursively learn features. - Adityanarayanan Radhakrishnan; Daniel Beaglehole; Parthe Pandit; Mikhail Belkin (CoRR, 2022)

Sampling Equilibria: Fast No-Regret Learning in Structured Games. - Daniel Beaglehole; Max Hopkins; Daniel Kane 001; Sihan Liu; Shachar Lovett (SODA, 2023)

Mechanism of feature learning in convolutional neural networks. - Daniel Beaglehole; Adityanarayanan Radhakrishnan; Parthe Pandit; Mikhail Belkin (CoRR, 2023)

On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions. - Daniel Beaglehole; Mikhail Belkin; Parthe Pandit (SIAM J. Math. Data Sci., 2023)

Average gradient outer product as a mechanism for deep neural collapse. - Daniel Beaglehole; Peter Súkeník; Marco Mondelli; Mikhail Belkin (NeurIPS, 2024)

Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks. - Daniel Beaglehole; Ioannis Mitliagkas; Atish Agarwala (CoRR, 2024)

Fast, optimal, and dynamic electoral campaign budgeting by a generalized Colonel Blotto game. - Thomas Valles; Daniel Beaglehole (CoRR, 2024)

Emergence in non-neural models: grokking modular arithmetic via average gradient outer product. - Neil Mallinar; Daniel Beaglehole; Libin Zhu; Adityanarayanan Radhakrishnan; Parthe Pandit; Mikhail Belkin (CoRR, 2024)

Feature learning as alignment: a structural property of gradient descent in non-linear neural networks. - Daniel Beaglehole; Ioannis Mitliagkas; Atish Agarwala (Trans. Mach. Learn. Res., 2024)

Aggregate and conquer: detecting and steering LLM concepts by combining nonlinear predictors over multiple layers. - Daniel Beaglehole; Adityanarayanan Radhakrishnan; Enric Boix-Adserà; Mikhail Belkin (CoRR, 2025)

xRFM: Accurate, scalable, and interpretable feature learning models for tabular data. - Daniel Beaglehole; David Holzmüller; Adityanarayanan Radhakrishnan; Mikhail Belkin (CoRR, 2025)