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

Policy Learning and Evaluation with Randomized Quasi-Monte Carlo
Poster for our work on reducing the variance with RQMC in reinforcement learning.
AISTATS, 2022.

Uniform Sampling Over Episode Difficulty
Poster for our work on sampling episodes in few-shot learning.
NeurIPS, 2021.

When MAML Can Adapt Fast and How to Assist When it Cannot
Poster for our work on helping MAML learn to adapt.
AISTATS, 2021.

Reducing the variance in online optimization by transporting past gradients
Poster for our work on implicit gradient transport.
NeurIPS, 2019.

cherry: A Reinforcement Learning Framework for Researchers
An overview of cherry.
PyTorch Dev Conference, 2019.

learn2learn: A Meta-Learning Framework for Researchers
An overview of learn2learn.
PyTorch Dev Conference, 2019.

Managing Machine Learning Experiments
How to use randopt to manage machine learning experiments.

Accelerating SGD for Distributed Deep Learning Using Approximated Hessian Matrix
Approximating the Hessian via finite differences in the distributed setting.
ICLR17 Workshop.