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.

Go to: [Publications, Presentations, Posters]

** denotes equal contribution*

**Policy-Induced Self-Supervision Improves Representation Finetuning in Visual RL**

S. M. R. Arnold, F. Sha

*ArXiv Preprint*, 2023

[ArXiv,
pdf,
website]

**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]

**Policy Learning and Evaluation with Randomized Quasi-Monte Carlo**

S. M. R. Arnold, P. L’Ecuyer, L. Chen, Y-f. Chen, F. Sha

*AISTATS*, 2022

[ArXiv,
pdf,
website,
code]

**Uniform Sampling over Episode Difficulty**

S. M. R. Arnold*, G. S. Dhillon*, A. Ravichandran, S. Soatto

*NeurIPS*, 2021
Spotlight

[ArXiv, pdf, website, code]

**Embedding Adaptation is Still Needed for Few-Shot Learning**

S. M. R. Arnold, F. Sha

*ArXiv Preprint*, 2021

[ArXiv, pdf, website]

**When MAML Can Adapt Fast and How to Assist When It Cannot**

S. M. R. Arnold, S. Iqbal, F. Sha

*AISTATS*, 2021

[ArXiv, pdf, website, code]

**learn2learn: A Library for Meta-Learning Research**

S. M. R. Arnold, P. Mahajan, D. Datta, I. Bunner, K. S. Zarkias

*ArXiv Preprint*, 2020

[ArXiv, pdf, website, code]

**Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator**

J. Preiss*, S. M. R. Arnold*, C-Y. Wei*, M. Kloft

*NeurIPS OptRL Workshop*, 2019

[ArXiv, pdf]

**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.

**Shapechanger: Environments for Transfer Learning**

S. M. R. Arnold, E. Pun, T. Denisart, F. Valero-Cuevas

*SoCal Robotics Symposium*, 2017

[ArXiv, pdf, website]

**Accelerating SGD for Distributed Deep Learning Using an Approximated Hessian Matrix**

S. M. R. Arnold, C. Wang

*ICLR Workshop*, 2017

[ArXiv, pdf]

**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**

Thesis defense.

*University of Southern California*, Los Angeles, CA (Remote; December 2022)

[pdf]

**Policy Learning and Evaluation with Randomized Quasi-Monte Carlo**

Presentation of our work on RQMC for RL.

*AISTATS 2022*, Virtual, (Remote; March 2022)

[pdf, talk]

**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).

**When MAML Can Adapt Fast and How to Assist When it Cannot**

Slidelive presentation of our work on helping MAML learn to adapt.

*AISTATS 2021*, Virtual (Remote; April 2021)

[pdf, talk]

**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)

[pdf, talk]

**learn2learn: A Meta-Learning Framework**

Short presentation of learn2learn and some applications of meta-learning.

*PyTorch Dev Conference*, San Francisco, CA (October 2019)

[pdf, talk]

**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)

[pdf]

**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)

[pdf]

**Policy Learning and Evaluation with Randomized Quasi-Monte Carlo**

Poster for our work on reducing the variance with RQMC in reinforcement learning.

*AISTATS*, 2022

[pdf]

**Uniform Sampling Over Episode Difficulty**

Poster for our work on sampling episodes in few-shot learning.

*NeurIPS*, 2021

[pdf]

**When MAML Can Adapt Fast and How to Assist When it Cannot**

Poster for our work on helping MAML learn to adapt.

*AISTATS*, 2021

[pdf]

**Reducing the variance in online optimization by transporting past gradients**

Poster for our work on implicit gradient transport.

*NeurIPS*, 2019

[pdf]

**cherry: A Reinforcement Learning Framework for Researchers**

An overview of cherry.

*PyTorch Dev Conference*, 2019

[pdf]

**learn2learn: A Meta-Learning Framework for Researchers**

An overview of learn2learn.

*PyTorch Dev Conference*, 2019

[pdf]

**Managing Machine Learning Experiments**

How to use randopt to manage machine learning experiments.

*PyCon*, 2018

[pdf]

**Accelerating SGD for Distributed Deep Learning Using Approximated Hessian Matrix**

Approximating the Hessian via finite differences in the distributed setting.

*ICLR Workshop*, 2017

[pdf]