My research interests lie in machine learning with a focus on problems where data is scarce and compute is limited.
My main contributions concern meta-learning algorithms — algorithms that discover and improve new learning algorithms. Specifically, my PhD dissertation showed how (and why!) some inductive biases are transferred from one task to the next while others have to be learned from scratch. Through my work I was fortunate to tackle problems ranging from computer vision and language to robotics.
Ultimately, I hope my research can help democratize machine learning so we can all reap the benefits of artifical intelligence.
* denotes equal contribution
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
M. M. Baker, A. New, M. Aguilar-Simon, Z. Al-Halah, S. M. R. Arnold, E. Ben-Iwhiwhu, A. P. Brna, E. Brooks, R. C. Brown, Z. Daniels, A. Daram, F. Delattre, R. Dellana, E. Eaton, H. Fu, K. Grauman, J. Hostetler, S. Iqbal, D. Kent, N. Ketz, S. Kolouri, G. Konidaris, D. Kudithipudi, E. Learned-Miller, S. Lee, M. L. Littman, S. Madireddy, J. A. Mendez, E. Q. Nguyen, C. Piatko, P. K. Pilly, A. Raghavan, A. Rahman, S. K. Ramakrishnan, N. Ratzlaff, A. Soltoggio, P. Stone, I. Sur, Z. Tang, S. Tiwari, K. Vedder, F. Wang, Z. Xu, A. Yanguas-Gil, H. Yedidsion, S. Yu, G. K. Vallabha.
Neural Networks, Volume 160, 2023
[ArXiv, pdf, ScienceDirect]
Reducing the variance in online optimization by transporting past gradients
S. M. R. Arnold, P.-A. Manzagol, R. Babanezhad, I. Mitliagkas, N. Le Roux
NeurIPS, 2019 Spotlight
[ArXiv, pdf, website, code]
Understanding the Variance of Policy Gradient Estimators in Reinforcement Learning
S. M. R. Arnold*, J. Preiss*, C-Y. Wei*, M. Kloft
SoCal Machine Learning Symposium, 2019 Best Poster
See subsequent workshop submission for updated preprint.
A Performance Comparison between TRPO and CEM for Reinforcement Learning
S. M. R. Arnold, E. Chu, F. Valero-Cuevas
SoCal ML Symposium, 2016
Quickly solving new tasks, with meta-learning and without
University of Southern California, Los Angeles, CA (Remote; December 2022)
Uniform Sampling Over Episode Difficulty
Spotlight presentation of our work on sampling episodes in few-shot learning.
NeurIPS 2021, Virtual, (Remote; December 2021)
EPFL’s NeurIPS 2021 mirror event, Lausanne, Switzerland (December 2021)
[pdf, talk (NeurIPS), talk (EPFL)]
To Transfer or To Adapt: A Study Through Few-Shot Learning
Overview of my recent research research on adaptation and transfer in few-shot learning.
Google, Mountain View, CA (Remote; April 2021)
Amazon, Seattle, WA (August 2021).
Reducing the Variance in Online Optimization by Transporting Past Gradients
Spotlight presentation of our work on implicit gradient transport.
NeurIPS 2019, Vancouver, Canada (December 2019)
Information Geometric Optimization
Tutorial on recent approaches using information geometric principles for optimization. Inspired by Yann Ollivier’s presentation and James Martens’ paper.
ShaLab reading group, Los Angeles, CA (October 2018)
Managing Machine Learning Experiments
Presentation of randopt and how to use it to manage machine learning experiments.
SoCal Python Meetup, Los Angeles, CA (May 2018)
Accelerating SGD for Distributed Deep Learning Using Approximated Hessian Matrix
Approximating the Hessian via finite differences in the distributed setting.
ICLR Workshop, 2017