I'm a PhD student in the Computer Science Department. My research
interests lie in machine learning and planning. In particular, I'm
interested in applying fast online algorithms and structured models to
real-world problems. My thesis work is on multi-agent planning under
uncertainty with shared resources. Ultimately, it'd be nice to help save
the world with machine learning / game theory (e.g. solving
environmental problems, designing public policy, eliminating
inefficiency wherever possible).
My advisor is Geoff
Gordon. Previously I worked with
Tom Mitchell on
reading the web
by extracting facts from web text using co-training-style
semi-supervised learning algorithms.
I've been also working on a couple of cool art projects that use machine learning.
- Yisong Yue, Sue Ann Hong, and Carlos Guestrin (2012).
Exploration for Accelerating Contextual
Bandits. International Conference on Machine Learning
- Sue Ann Hong and Geoffrey Gordon.
Gradient Method for Distributed Multi-Agent Planning with
Factored MDPs. NIPS workshop on Optimization for Machine
Learning (OPT), 2011.
- Sue Ann Hong and Geoffrey J. Gordon.
Market-Based Planning for Multi-Agent Systems with Shared
Resources. In Proc. 14th Intl. Conf. on Artificial
Intelligence and Statistics (AISTATS), 2011.
- Geoffrey J. Gordon, Sue Ann Hong, Miroslav Dudík.
First-Order Mixed Integer Linear
Programming. In Proc. 25th Conf. on
Uncertainty in Artificial Intelligence (UAI), 2009.
- Read the Web. Our ultimate goal is to build an information extraction
system that learns to extract and accumulate facts from text, and uses
those facts to learn to extract better. The basis of the system builds
upon ideas from bootstrap information extraction and co-training to
classify entities and relations for predefined classes, starting with a
handful of examples each. Preliminary results and a starting system
description can be found in our position paper:
Justin Betteridge, Andrew Carlson, Sue Ann Hong, Estevam R. Hruschka Jr.,
Edith L. M. Law, Tom M. Mitchell, Sophie H. Wang.
Toward Never Ending Language Learning.
AAAI Spring Symposium on Learning by Reading and Learning to Read, 2009.
Some Interesting Class Projects
- An Experimental analysis of no-Phi-regret algorithms for online convex
games [pdf], Optimization
- Linear-Time Inverse Covariance Matrix Estimation in Gaussian Processes
Probabilistic Graphical Models class project.
- An Empirical Investigation of Active Learning for Bootstrap
Information Extraction [pdf],
Read the Web class project.
- Hot? Or Not: learning to evaluate facial attractiveness based on images
and ratings. We employed eigenfaces and fisherfaces with SVMs to classify
the attractiveness of rectified facial images given the average of many
ratings from a popular website. Unfortunately, the method did not yield
great results, most likely due to the weak feature representation.
Computer Vision class project, Caltech.