Andrew McCallum's Home Page (original) (raw)

Machine Learning applied to Text, Information Retrieval and Extraction. Since 1996 I have been working on statistical approaches to text classification, clustering and extraction. I was a member of the CMU Text Learning Group and Tom Mitchell's World-Wide Knowledge Base Project.

I was the leader of the project at JustResearch that created Cora, a domain-specific search engine over computer science research papers. It currently contains over 50,000 postscript papers. You can read more about our research on Cora in our IRJ journal paper or a paperpresented at the AAAI'99 Spring Symposium. The Cora team also included Kamal Nigam, Kristie Seymore, Jason Rennie, Huan Chang and Jason Reed.

I am the author of rainbow, (and its library, libbow), a LGPL'ed software package for statistical text classification written in C.

I have been invited to give a tutorial at the Neural Information Processing Systems conference (NIPS*2002). The title is "Information Extraction from the World Wide Web".
With Lillian Lee, Tony Jebara and Kamal Nigam, I co-organized IJCAI'2001 workshop titled Text Learning: Beyond Supervision.
With Thorsten Joachims, Mehran Sahamiand Lyle Ungar, I co-organized a IJCAI-99 workshop on Machine Learning for Information Filtering.
With Rich Caurana, Virginia de Sa and Michael Kearns, I co-organized a NIPS*98workshop on "Integrating Supervised and Unsupervised Learning".
With Mehran Sahami, Mark Cravenand Thorsten Joachims I co-organized a ICML/AAAI-98 workshop on Learning for Text Categorization.

Reinforcement Learning---especially with hidden state and factored representations. My thesisuses memory-based learning and a robust statistical test on reward in order to learn a structured policy representation that makes perceptual and memory distinctions only where needed for the task at hand. It can also be understood as a method of Value Function Approximation. The model learned is an order-n partially observable Markov decision process. It handles noisy observation, action and reward.

It is related to Ron, Singer and Tishby's Probabilistic Suffix Trees, Leslie Kaelbling's G-algorithm and Andrew Moore's_Parti-game_. It is distinguished from similar-era work by Michael Littman, Craig Boutilier and others in that it learns both a model and a policy, and is quite practical with infinite-horizon tasks and large state and observation spaces. Follow-on or comparison work has been done by Anders Jonsson, Andy Barto,Will Uther, Leslie Pack Kaelbling,Natalia Hernandez, and Sridhar Mahadevan.

The algorithm, called U-Tree, was demonstrated solving a highway driving task using simulated eye-movements and deictic representations. The simulated environment has about 21000 states, 2500 observations, noise and much hidden state. After about 2 1/2 hours of simulated experience, U-Tree learns a task-specific model of the environment that has only 143 states.