Daniel Beaglehole

According to our database, Daniel Beaglehole authored at least 20 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

The Weight Gram Matrix Captures Sequential Feature Linearization in Deep Networks.

Taehun Cha, Daniel Beaglehole, Adityanarayanan Radhakrishnan, Donghun Lee

CoRR, May, 2026

Contextual Linear Activation Steering of Language Models.

Brandon Hsu, Daniel Beaglehole, Adityanarayanan Radhakrishnan, Mikhail Belkin

CoRR, April, 2026

DANCE: Doubly Adaptive Neighborhood Conformal Estimation.

Brandon R. Feng, Brian J. Reich, Daniel Beaglehole, Xihaier Luo, David Keetae Park, Shinjae Yoo, Zhechao Huang, Xueyu Mao, Olcay Boz, Jungeum Kim

CoRR, February, 2026

CAPYBARA: A generalizable framework for predicting serological measurements across human cohorts.

Sierra Orsinelli-Rivers, Daniel Beaglehole, Tal Einav

PLoS Comput. Biol., 2026

2025

Steering Autoregressive Music Generation with Recursive Feature Machines.

Daniel Zhao, Daniel Beaglehole, Taylor Berg-Kirkpatrick, Julian J. McAuley, Zachary Novack

CoRR, October, 2025

xRFM: Accurate, scalable, and interpretable feature learning models for tabular data.

Daniel Beaglehole, David Holzmüller, Adityanarayanan Radhakrishnan, Mikhail Belkin

CoRR, August, 2025

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, February, 2025

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

Proceedings of the Forty-second International Conference on Machine Learning, 2025

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

Fast, optimal, and dynamic electoral campaign budgeting by a generalized Colonel Blotto game.

Thomas Valles, Daniel Beaglehole

CoRR, 2024

Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks.

Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala

CoRR, 2024

Average gradient outer product as a mechanism for deep neural collapse.

Daniel Beaglehole, Peter Súkeník, Marco Mondelli, Mikhail Belkin

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions.

Daniel Beaglehole, Mikhail Belkin, Parthe Pandit

SIAM J. Math. Data Sci., December, 2023

Mechanism of feature learning in convolutional neural networks.

Daniel Beaglehole, Adityanarayanan Radhakrishnan, Parthe Pandit, Mikhail Belkin

CoRR, 2023

Sampling Equilibria: Fast No-Regret Learning in Structured Games.

Daniel Beaglehole, Max Hopkins, Daniel Kane, Sihan Liu, Shachar Lovett

Proceedings of the 2023 ACM-SIAM Symposium on Discrete Algorithms, 2023

2022

Feature learning in neural networks and kernel machines that recursively learn features.

Adityanarayanan Radhakrishnan, Daniel Beaglehole, Parthe Pandit, Mikhail Belkin

CoRR, 2022

Kernel Ridgeless Regression is Inconsistent for Low Dimensions.

Daniel Beaglehole, Mikhail Belkin, Parthe Pandit

CoRR, 2022

An Efficient Approximation Algorithm for the Colonel Blotto Game.

Daniel Beaglehole

CoRR, 2022

Learning to Hash Robustly, Guaranteed.

Alexandr Andoni, Daniel Beaglehole

Proceedings of the International Conference on Machine Learning, 2022

2021

Learning to Hash Robustly, with Guarantees.

Alexandr Andoni, Daniel Beaglehole

CoRR, 2021