About

Hello! I am a PhD student in the CSE department at UC San Diego in the machine learning and theory groups. I am very fortunate to be advised by Professor Mikhail Belkin. Prior to my PhD, I completed an MS in computer science at Columbia, where I researched under the excellent mentorship of Professor Alexandr Andoni.

I was previously a Student Researcher at Google DeepMind working on feature learning. I was also an ML Research intern at Goldman Sachs.

I am supported by the ARCS Foundation Fellowship.

Broadly, I am excited about developing theory-driven machine learning and algorithmic methods. I am especially interested in:
1.) Feature learning
2.) Deep learning theory
3.) Data-dependent kernel machines

Feel free to email me: dbeaglehole {at} ucsd {dot} edu

(* denotes equal contribution).

Pre-prints

1. Mechanism of feature learning in convolutional neural networks
   Daniel Beaglehole*, Adityanarayanan Radhakrishnan*, Parthe Pandit, Mikhail Belkin
2. 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)
3. 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
   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)
   (twitter link)
   • 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) (presented by Prof. Andoni for NeurIPS’21 ANN competition)

Presentations

1. Google Brain: (“Feature learning in neural networks and kernel machines that recursively learn features”, 3/2023)
2. Yale University: Inference, Information, and Decision Systems Group, (“Feature learning in neural networks and kernel machines that recursively learn features”, 3/2023)
3. UCSD: Theory Seminar, (“Learning to Hash Robustly, Guaranteed”, 10/2021)
4. Goldman Sachs: Data Science and Machine Learning paper club, (“Learning to Hash Robustly, Guaranteed”, 07/2021)
5. Goldman Sachs: Summer internship final presentation, (“Predictive Clustering Time Series for Finance”, 08/2021)