Sébastien M. R. Arnold

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\( % Universal Mathematics \newcommand{\paren}[1]{\left( #1 \right)} \newcommand{\brackets}[1]{\left[ #1 \right]} \newcommand{\braces}[1]{\left\{ #1 \right\}} \newcommand{\norm}[1]{\left\lVert#1\right\rVert} \newcommand{\case}[1]{\begin{cases} #1 \end{cases}} \newcommand{\bigO}[1]{\mathcal{O}\left(#1\right)} % Analysis % Linear Algebra \newcommand{\mat}[1]{\begin{pmatrix}#1\end{pmatrix}} \newcommand{\bmat}[1]{\begin{bmatrix}#1\end{bmatrix}} % Probability Theory \DeclareMathOperator*{\V}{\mathop{\mathrm{Var}}} \DeclareMathOperator*{\E}{\mathop{\mathbb{E}}} \newcommand{\Exp}[2][]{\E_{#1}\brackets{#2}} \newcommand{\Var}[2][]{\V_{#1}\brackets{#2}} \newcommand{\Cov}[2][]{\mathop{\mathrm{Cov}}_{#1}\brackets{#2}} % Optimization \newcommand{\minimize}{\operatorname*{minimize}} \newcommand{\maximize}{\operatorname*{maximize}} \DeclareMathOperator*{\argmin}{arg\,min} \DeclareMathOperator*{\argmax}{arg\,max} % Set Theory \newcommand{\C}{\mathbb{C}} \newcommand{\N}{\mathbb{N}} \newcommand{\Q}{\mathbb{Q}} \newcommand{\R}{\mathbb{R}} \newcommand{\Z}{\mathbb{Z}} \)

I am a doctoral student in the ShaLab supervised by Fei Sha. My research interest lies at the intersection of decision making and optimization. Those interests have brought me to work on topics related meta-learning.

I did my undergraduate at the University of Southern California, double majoring in Computer Science and Mathematics. My research focused on robotics with Francisco Valero-Cuevas, and mathematical optimization with Chunming Wang.

I also like skiing (a lot).

[Contact / Résumé / Semantic Scholar / GitHub / Twitter]

Long portrait


News

When MAML Can Adapt Fast and How to Assist When It Cannot - January 22, 2021
Our manuscript on When MAML Can Adapt Fast and How to Assist When It Cannot was accepted at AISTATS 2021. Open-source implementation in learn2learn is now available. [ArXiv, pdf]

Summer at Amazon AI - April 1, 2020
I will be spending the summer at Amazon AI in Pasadena, CA.

Decoupling Adaptation from Modeling with Meta-Optimizers - November 17, 2019
Our preprint on Decoupling Adaptation from Modeling with Meta-Optimizers for Meta-Learning is available on ArXiv. Open-source implementation in learn2learn coming soon! [ArXiv, pdf]

Variance of Policy Gradient - November 17, 2019
Our preprint on Analyzing the variance of policy gradient estimators for LQR was accepted at the OptRL NeurIPS workshop. [ArXiv, pdf]

Implicit Gradient Transport - September 5, 2019
Our paper on Reducing the variance in online optimization by transporting past gradients was accepted at NeurIPS as a spotlight contribution. [ArXiv, pdf, website, code]

Open-Sourcing learn2learn - August 20, 2019
Our submission to the PyTorch Summer Hackathon won best in show! Check out the website to learn how to easily implement meta-learning algorithms with learn2learn. [website, code]

East European Summer School - June 5, 2019
I will be attending the East-European Summer School this summer. Get in touch if you will too!
Edit: My poster got lucky and received the best theory poster award!