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<!DOCTYPE html>
<html>
<head>
<meta name="google-site-verification" content="QdLFd6Y9R48MK0F1vzILOn6BBut4pndjRk5RZqi3oK8" />
<style>
@font-face { font-family: Computer Modern; src: url('cmunorm.ttf'); }
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</style>
</head>
<body>
<div id="header">
<h1> Gabriel Goh </h1>
</div>
<h2>About</h2>
<script type="text/javascript"><!--
function gen_mail_to_link(lhs,rhs,subject)
{
document.write("<A HREF=\"mailto");
document.write(":" + lhs + "@");
document.write(rhs + "?subject=" + subject + "\">" + lhs + "@" + rhs + "<\/A>"); }
</script>
<script async src="http://platform.twitter.com/widgets.js" charset="utf-8"></script>
<div id="section">
<div id="left">
<center>
<img src="face3.png" width="150px", height = "150px">
</center>
</div>
<div id = "right">
<b>Hi!</b><p>
Welcome to my website. I'm Gabe, machine learning researcher at OpenAI. I'm interested in interpretability, machine learning, data visualization and convex optimization.
<!-- <a href = "https://www.math.ucdavis.edu/~mpf/"> Michael
Friedlander</a>. I am interested in Convex Optimization, Machine Learning
and Probability.
-->
<!--
UC Davis working under
the supervision of <a href = "https://www.math.ucdavis.edu/~mpf/"> Michael
Friedlander</a>. I am interested in Convex Optimization, Machine Learning
and Probability.
<p> Check out my <a href="http://gabgoh.github.io/blog/">blog</a>. There I
speak my mind on deep learning, nonconvex optimization, and other dark
and forbidden arts.
<p> I contribute to the <a href="http://www.julialang.org">Julia</a>
programming language, my favorite language by far for all things technical.
It fully supports type checking, unicode variables, and has all the creature
comforts of a modern language. Check out my code and notebooks below.
<p>
In my free time, I tinker with Computer Graphics, Data Visualization,
Virtual Reality, and think deep thoughts about the internet and our place in
it.
-->
<br><br>
<br>
<b>Email:</b>
<script> gen_mail_to_link("gabgohjh", "gmail.com","Hi") </script>
<br>
<b> Twitter: </b>
<a href="https://twitter.com/gabeeegoooh" class="twitter-follow-button" data-show-count="false">Follow @gabeeegoooh</a>
</div>
</div>
</p>
<p>
<h2>Code</h2>
<div id="sectionmid">
<div id = "left2">
<b><a href = "https://github.com/MPF-Optimization-Laboratory/ConicIP.jl">ConicIP</a></b><br>
This code
is a pure Julia implementation of the primal-dual predictor corrector found in
<a href = "http://www.cvxopt.org">cvxopt</a>, written with an emphasis on
robustness, brevity, portability and numerical stability.
<p><b>Examples:</b><br> 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 </div>
<div id="right2">
<img src="soc.svg" width="150px", height = "150px">
</div>
</div>
<div id="section">
<div id="left">
<img src="qsip.svg" width="150px", height = "150px">
</div>
<div id = "right">
<b><a href = "">QSip [Coming Soon]</a></b><br>
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! <p>
<b>Examples</b> <br> LASSO, Logistic Regression, Maximum-Volume
inscribed Ellipsoid, Non-Negative Matrix
factorization, Dictionary Learning, Tensor Factorization, Deep Learning.
</div>
</div>
<h2>Publications</h2>
<div id="section">
<div id = "left2">
<b><a href = "http://papers.nips.cc/paper/6316-satisfying-real-world-goals-with-dataset-constraints.pdf">Satisfying Real-world Goals with Dataset Constraints</a></b><br>
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?
<br><br> <b>
<a href = "http://papers.nips.cc/paper/6316-satisfying-real-world-goals-with-dataset-constraints.pdf"> Paper</a> <br> </b>
</div>
<div id="right2">
<img src="hinge.svg" width="150px", height = "150px">
</div>
</div>
<div id="sectionmid">
<div id = "left">
<img src="envelope.svg" width="150px", height = "150px">
</div>
<div id="right"><a href="https://arxiv.org/abs/1603.05719"> <b>Efficent Evaluation of Scaled Proximal Operators</b></a><br>
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.
<br><br>
<b>
<a href="https://arxiv.org/abs/1603.05719"> Paper </a>
<br>
</b>
</div>
</div>
<div id="section">
<div id = "left2">
<b><a href = "http://arxiv.org/abs/1304.5586">Tail Bounds for Stochastic
Gradient Descent</a></b><br>
My first paper in grad school. Here I investigated at the role concentration
plays in randomized algorithms for optimization, in particular stochastic
gradient descent. <br><br> <b>
<a href = "http://arxiv.org/abs/1304.5586"> Paper</a> <br> </b>
</div>
<div id="right2">
<img src="tail.svg" width="150px", height = "150px">
</div>
</div>
<div id="clear"></div>
<h2><center>Personal Projects</center></h2>
<div id="sectionmid">
<div id = "left">
<center>
<img src="memes.png" width="150px", height = "150px">
</center>
</div>
<div id="right"> <p> <b> <a href =
"http://gabgoh.github.io/adviceanimals/html/"> 4 Years of Advice Animals</a>
</b> <br>
I saw a beautiful spindle diagram in a natural history museum in montreal
(it looked a little like <a href =
"https://www2.estrellamountain.edu/faculty/farabee/biobk/pro_plfr.gif">this</a>
or <a href =
"https://upload.wikimedia.org/wikipedia/commons/7/76/Spindle_diagram.jpg">this</a>)
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 <a
href="https://d3js.org/"> D3 </a> visualization of <a href =
"http://www.pushshift.io">Jason Braughmerger's</a> meticulously mined reddit
dataset showing all posts in /r/AdviceAnimal to Dec 2014 with over 50
up-votes. Enjoy!
<br><br> <a href =
"http://gabgoh.github.io/adviceanimals/html/"> Site</a> <br> <b>Making of
(coming soon) </b> <br> </div>
</div>
<h2><center>Press</center></h2>
<div id="sectionmid">
<table>
<tr>
<td>
<a href="http://www.theverge.com/2016/10/24/13379208/ai-nsfw-neural-nets-deep-dream-genitals"><img src="presslogos/verge.png" width="110" class="imgp"></a>
</td>
<td>
<a href="http://www.vice.com/alps/read/hangover-news-vierundzwanzig-oktober"><img src="presslogos/vice.png" width="110"></a>
</td>
<td>
<a href="http://www.gizmodo.co.uk/2016/10/combined-super-ai-turns-everything-into-a-penis/"><img src="presslogos/gizmodo.svg" width="110"></a>
</td>
<td>
<a href = "http://www.gizmodo.com.au/2016/10/porn-images-synthesised-by-yahoos-nsfw-neural-network-is-lovecraftian-erotica"><img src="presslogos/gizmodoau.png" width="110"></a>
</td>
</tr>
<tr>
<td>
<a href = "http://gizmodo.com/after-looking-at-deep-dream-genitalia-internet-outage-1788089762"><img src="presslogos/gizmodo.png" width="110" class="imgp"></a>
</td>
<td>
<a href = "https://www.oreilly.com/ideas/four-short-links-21-october-2016">
<img src="presslogos/OR.png" width="110">
</a>
</td>
<td>
<a href = "http://www.theinquirer.net/inquirer/news/2474925/yahoo-is-machine-learning-the-difference-between-a-dirty-and-a-not-dirty-picture">
<img src="presslogos/The-Inquirer-logo.jpg" width="110" class="imgp">
</a>
</td>
<td>
<a href = "https://www.fastcodesign.com/3064924/yahoos-nsfw-neural-network-can-spot-penises-in-pretty-much-any-picture">
<img src="presslogos/fast-company-logo.png" width="110">
</a>
</td>
</tr>
<tr>
<td>
<a href = "https://www.fayerwayer.com/2016/10/esta-app-de-inteligencia-artificial-puede-crear-imagenes-de-genitales-a-partir-de-fotos-reales-nsfw/">
<img src="presslogos/fayerwayer.png" width="110">
</a>
</td>
<td>
<a href = "http://www.wired.co.uk/article/wired-awake-241016">
<img src="presslogos/Wired_logo.svg" width="110" class="imgp">
</a>
</td>
<td>
<a href = "http://boingboing.net/2016/10/21/using-machine-learning-to-synt.html">
<img src="presslogos/boing.png" width="110" class="imgp">
</a>
</td>
<td>
<a href = "http://uproxx.com/technology/yahoo-image-blocker/#">
<img src="presslogos/uproxx.png" width="110" class="imgp">
</a>
</td>
</tr>
</table>
</div>
<!--
<div id="section">
<div id = "left2"> <b> <a href = "http://gfycat.com/CheerfulAptBuffalo">Some
</a> <a href = "http://gfycat.com/RedCandidGrayling">Deep </a>
Dreams</a></b> </b> <br> To the true believers, <a href =
"https://github.com/google/deepdream"> Deep Dreaming </a> is merely a
playful exercise in visualizing a neural net, but to me, it was the vector
which destroyed my skepticism on the dark art of deep learning. How else
could a dense matrix of weights see eyes in the clouds and doggy tails on the
grooves of bicycle helmets? I couldn't wait to try the code for myself; here
are some deep dreams I made using code available online.
</div>
<div id="right2">
<img src="dreams.png" width="150px", height = "150px">
</div>
</div> -->
</p>
</p>
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