publications

Preprints

1. xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
   Daniel Beaglehole, David Holzmüller, Adityanarayanan Radhakrishnan, Mikhail Belkin
2. Toward universal steering and monitoring of AI models
   Daniel Beaglehole*, Adityanarayanan Radhakrishnan*, Enric Boix-Adserà, Mikhail Belkin
3. Mechanism of feature learning in convolutional neural networks
   Daniel Beaglehole*, Adityanarayanan Radhakrishnan*, Parthe Pandit, Mikhail Belkin
4. Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features
   Adityanarayanan Radhakrishnan*, Daniel Beaglehole*, Parthe Pandit, Mikhail Belkin
   (twitter link)
5. CAPYBARA: A Generalizable Framework for Predicting Serological Measurements Across Human Cohorts
   Sierra Orsinelli-Rivers, Daniel Beaglehole, Tal Einav
6. Fast, optimal, and dynamic electoral campaign budgeting by a generalized Colonel Blotto game
   Thomas Valles, Daniel Beaglehole

Publications

1. Mechanism for feature learning in neural networks and backpropagation-free machine learning models
   Adityanarayanan Radhakrishnan*, Daniel Beaglehole*, Parthe Pandit, Mikhail Belkin
   Science
2. Average gradient outer product as a mechanism for deep neural collapse
   Daniel Beaglehole*, Peter Súkeník*, Marco Mondelli, Mikhail Belkin
   Conference on Neural Information Processing Systems (NeurIPS 2024)
3. Feature learning as alignment: a structural property of gradient descent in non-linear neural networks
   Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala
   Transactions on Machine Learning Research (TMLR)
   Workshop on High-dimensional Learning Dynamics (HiLD) @ ICML 2024
4. 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
   International Conference on Machine Learning (ICML 2025) (Oral, Top 1%)
   Mathematics of Modern Machine Learning (M3L) @ NeurIPS 2024
5. On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions
   Daniel Beaglehole, Mikhail Belkin, Parthe Pandit
   SIAM Journal on Mathematics of Data Science (SIMODS)
   Conference on the Mathematical Theory of Deep Neural Networks (DeepMath 2022)
6. Sampling Equilibria: Fast No-Regret Learning in Structured Games
   Daniel Beaglehole*, Max Hopkins*, Daniel Kane*, Sihan Liu*, Shachar Lovett*
   Symposium on Discrete Algorithms (SODA 2023)
   This began as an earlier version: An Efficient Approximation Algorithm for the Colonel Blotto Game — Daniel Beaglehole
7. Learning to Hash Robustly, Guaranteed
   Alexandr Andoni*, Daniel Beaglehole*
   International Conference on Machine Learning (ICML 2022)
   (twitter link)