Papers - Sébastien M. R. Arnold
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* denotes equal contribution

Policy-Induced Self-Supervised Learning for 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
[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
[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

A Greedy Algorithm to Cluster Specialists
S. M. R. Arnold
ArXiv Preprint, 2016
[ArXiv, pdf]