Gabriel Goh

Hi!

Welcome to my website. I'm Gabe, machine learning researcher at OpenAI. I'm interested in interpretability, machine learning, data visualization and convex optimization.


Email:
Twitter: Follow @gabeeegoooh

ConicIP
This code is a pure Julia implementation of the primal-dual predictor corrector found in cvxopt, written with an emphasis on robustness, brevity, portability and numerical stability.

Examples:
LASSO, Trend Filtering, Group-LASSO, Support Vector Machines (primal, dual, kernel), Support Vector Regression, Quantile Regression, Robust Regression, Non-Negative Least Squares, Convex.jl integration, JuMP Integration

QSip [Coming Soon]
While Quasi-Netwon (QN) methods are generally the algorithms of choice for smooth, unconstrained optimization, QN methods don't generalize easily in the sharp-edged, kinky world of non-smooth optimization. This is a modest step forward - an implementation of a L-BFGS solver made for a class of problems which prove to be extremely useful. See the examples below. Not only do we achieve up to a 50x less iterations on average (no cherry picking here), it seems to finds better local minima in non-convex problems!

Examples
LASSO, Logistic Regression, Maximum-Volume inscribed Ellipsoid, Non-Negative Matrix factorization, Dictionary Learning, Tensor Factorization, Deep Learning.

Satisfying Real-world Goals with Dataset Constraints
There's more to the life of a machine learning model, I am sad to report, than optimizing for training accuracy. And when you can't have everything at once, the best thing you can do is compromise. But what better compromise than the optimal one?

Paper

Efficent Evaluation of Scaled Proximal Operators
Here I discuss discussion the linear algebraic tricks used to solve proximal subproblems efficiently. Be forewarned, the number of times I used the Woodbury Matrix Identity is nearly criminal. This project led to the development of QSip.

Paper

Tail Bounds for Stochastic Gradient Descent
My first paper in grad school. Here I investigated at the role concentration plays in randomized algorithms for optimization, in particular stochastic gradient descent.

Paper

4 Years of Advice Animals
I saw a beautiful spindle diagram in a natural history museum in montreal (it looked a little like this or this) and I wondered if I could do something similar for memes on the internet. Perhaps memes obeyed similar forces, living and dying in the microclimates of our imagination. A long shot, perhaps. But philosophical pretentions aside, I enjoy at least the visual analogy. Here it is - a D3 visualization of Jason Braughmerger's meticulously mined reddit dataset showing all posts in /r/AdviceAnimal to Dec 2014 with over 50 up-votes. Enjoy!

Site
Making of (coming soon)