Sue Ann Hong (original) (raw)

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 webby 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.

Resume [pdf]

Research

• Yisong Yue, Sue Ann Hong, and Carlos Guestrin (2012). Hierarchical Exploration for Accelerating Contextual Bandits. International Conference on Machine Learning (ICML), 2012.[bibtex]
• Sue Ann Hong and Geoffrey Gordon. An Accelerated 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.Decomposition-Based Optimal 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 class project.
• Linear-Time Inverse Covariance Matrix Estimation in Gaussian Processes[pdf], 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.

Courses

• Art That Learns (new media installation using machine learning)
• Optimization
• Probabilistic Graphical Models (highly recommended)
• Intermediate Statistics
• Statistical Machine Learning
• Machine Learning Theory
• Machine Learning
• Read the Web (building a system for constantly-improving information extraction using "bootstrap learning")
• Spectral Graph Theory, etc.
• Graduate Algorithms
• Optimizing Compilers (or pessimizing them while trying; our project)
• Type Systems
• Computer Networks Teaching:
• Teaching assistant for Undergraduate Artificial Intelligence, fall of 2009.
• Teaching assistant for Graduate Machine Learning, fall of 2007.

top