Soumya Ray

(Ph. D., University of Wisconsin-Madison, 2005)
Assistant Professor
Department of Electrical Engineering and Computer Science
Case Western Reserve University
Office: Olin 516
Email: sray AT eecs_case_edu
Mailing Address: Department of EECS, Glennan 320, 10900 Euclid Ave, Cleveland OH 44106-7071

Teaching

Fall 2009: EECS 440 (Machine Learning)

Spring 2009: EECS 391/491 (Introduction to Artificial Intelligence)

Fall 2008: EECS 600 (special topics: Machine Learning)

Machine Learning Reading Group

Research

Multi-criteria scheduling for large multiprocessor systems
We are designing fast methods that learn how to distribute load among processors in a multiprocessor system according to different criteria, such as throughput and power requirements. With Swarup Bhunia.
Validating complex systems
Many modern robotic systems are used in situations where reliability is critical, but how to estimate reliability of these complex systems under various circumstances is not well understood. We are designing processes to validate and estimate the reliability of these systems. With Andy Podgurski and Cenk Cavusoglu.
Machine Learning for spam filtering
We are working on adaptive methods for spam detection. Our goal is to develop methods that can detect these messages as early as possible in network traffic, thereby saving bandwidth and decreasing congestion. With Michael Rabinovich and Mark Allman.
Knowledge Transfer in Reinforcement Learning
"Transfer Learning" focuses on methods that can effectively transfer knowledge acquired about one task to help in solving another, different task. We are developing techniques that transfer knowledge between different sequential decision processes, using real-time strategy games as our testbeds. With Alan Fern, Prasad Tadepalli, Tom Dietterich, Neville Mehta and Aaron Wilson.
Efficient learning for hard Boolean functions
Certain Boolean functions, such as parity, are known to be hard to learn efficiently. In this work, we demonstrated that the hardness of learning these functions is linked to the input distribution of the data; if the input distribution is "significantly different" from the uniform distribution, these functions may be efficiently learnable. Based on this observation, we developed a method called Skewing that is often able to learn such functions efficiently, given enough observations. With David Page, Lisa Hellerstein, Bard Rosell, Eric Lantz and Eric Bach.
Learning from Multiple-Instance Data
In standard supervised learning, examples are described by a tuple of attribute-value pairs. In some problems, such as predicting the binding affinities of small molecules to a target protein, examples are described by sets of such tuples. We have developed new algorithms for classification and regression from such "multiple-instance" data, and shown that the straightforward extension of linear regression to this setting is NP-complete. With Mark Craven, David Page and Burr Settles.
Information Extraction from Free Text
Information extraction is the task of creating structured relations out of free text. In our work, we have developed statistical methods for doing this that also incorporate grammatical information about sentences obtained using an automated parser, Sundance. With Mark Craven and Marios Skounakis.
Machine Learning for Question Answering
Question answering systems are designed to provide accurate responses to short factual questions asked in natural language. In our work, we have developed a method that the system can use to learn from past questions to improve accuracy on future questions. With Eric Brill. (This work is not publicly available)

Publications

N. Mehta, S. Ray, P. Tadepalli and T. Dietterich (2008).
Automatic Discovery and Transfer of MAXQ Hierarchies. pdf
Appears in the Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.
L. Hellerstein, B. Rosell, E. Bach, S. Ray and D. Page (2008).
Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions. (submitted) Tech report version
Eric Bach's paper on improved bounds for the number of correlation-immune functions.
B. Settles, M. Craven and S. Ray (2007).
Multiple-Instance Active Learning. pdf
Appears in the Proceedings of the 21st Conference on Neural Information Processing Systems, Vancouver, BC, Canada.
H. Chan, A. Fern, S. Ray, N. Wilson and C. Ventura (2007).
Online Planning for Resource Production in Real-Time Strategy Games. pdf
Appears in the Proceedings of the 17th International Conference on Automated Planning & Scheduling, Providence, RI, USA.
E. Lantz, S. Ray and D. Page (2007).
Learning Bayesian Network Structure from Correlation-Immune Data. pdf
Appears in the Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, Vancouver, BC, Canada.
A. Wilson, A. Fern, S. Ray and P. Tadepalli (2007).
Multi-task Reinforcement Learning: A Hierarchical Bayesian Approach. pdf ps.gz
Appears in the Proceedings of the 24th International Conference on Machine Learning, Corvallis, OR, USA.
J. Davis, V. S. Costa, S. Ray and D. Page (2007).
An Integrated Approach to Feature Invention and Model Construction for Drug Activity Prediction. pdf ps.gz
Appears in the Proceedings of the 24th International Conference on Machine Learning, Corvallis, OR, USA.
S. Ray (2005).
Learning from Data with Complex Interactions and Ambiguous Labels. ps pdf ps.gz
PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA.
S. Ray & M. Craven (2005).
Supervised versus Multiple-Instance Learning: An Empirical Comparison. ps pdf ps.gz
Appears in the Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany.
B. Rosell, L. Hellerstein, S. Ray & D. Page (2005).
Why Skewing works: Learning Difficult Boolean Functions with Greedy Tree Learners. ps pdf ps.gz
Appears in the Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany.
S. Ray & D. Page (2005).
Generalized Skewing for Functions with Continuous and Nominal Attributes. ps pdf ps.gz
Appears in the Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany.
S. Ray & M. Craven (2005).
Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text.
Appears in BMC Bioinformatics, Vol 6(Suppl 1). online ps pdf ps.gz
S. Ray & D. Page (2004).
Sequential Skewing: An Improved Skewing Algorithm. ps pdf ps.gz
Appears in the Proceedings of the 21st International Conference on Machine Learning, Banff, Canada.
D. Page & S. Ray (2003).
Skewing: An Efficient Alternative to Lookahead for Decision Tree Induction. ps pdf ps.gz
Appears in the Proceedings of the 18th International Joint Conference on Artificial Intelligence, Acapulco, Mexico.
M. Skounakis, M. Craven & S. Ray (2003).
Hierarchical Hidden Markov Models for Information Extraction. pdf
Appears in the Proceedings of the 18th International Joint Conference on Artificial Intelligence, Acapulco, Mexico.

S. Ray & M. Craven (2001).
Representing Sentence Structure in Hidden Markov Models for Information Extraction. ps pdf ps.gz
Appears in the Proceedings of the 17th International Joint Conference on Artificial Intelligence, Seattle, WA, USA.

S. Ray & D. Page (2001).
Multiple Instance Regression. ps pdf ps.gz
Appears in the Proceedings of the 18th International Conference on Machine Learning, Williamstown, MA, USA.

Workshop Publications

A. Wilson, A. Fern, S. Ray, P. Tadepalli (2008).
Learning and Transferring Roles in Multi-Agent MDPs. pdf
Transfer Learning for Complex Tasks Workshop, 23rd AAAI Conference on Artificial Intelligence, Chicago, USA.
N. Mehta, M. Wynkoop, S. Ray, P. Tadepalli and T. Dietterich (2007).
Automatic Induction of MAXQ Hierarchies. pdf
Hierarchical Organization of Behavior Workshop, 21st Conference on Neural Information Processing Systems, Vancouver, BC, Canada.
H. Chan, A. Fern, S. Ray, N. Wilson and C. Ventura (2007).
Extending Online Planning for Resource Production in Real-Time Strategy Games with Search. pdf
Workshop on Planning in Games, ICAPS 2007, Providence, RI, USA.

Contributed Chapters

S. Ray, S. Scott and H. Blockeel (2009). Multiple Instance Learning.
Encyclopedia of Machine Learning, Springer.
S. Ray and P. Tadepalli (2009). Model-based Reinforcement Learning.
Encyclopedia of Machine Learning, Springer.

Miscellaneous Activities

I was Registration Chair for the International Conference on Machine Learning (ICML) 2007 held at Oregon State University .
I recently served on the Program Committees of ICML 2008, AAAI 2008, and SBIA 2008.
I am serving on the Program Committees of ICML 2009 and IJCAI 2009.
I was the webmaster for the website of the International Machine Learning Society till December 2008. The new webmaster is Sebastian Stark.
I am the current moderator for the ML-news Google group.

Information Extraction Datasets

Literature and Datasets for the Multiple Instance Problem

Machine Learning News

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Last update 7/2/2009 by Soumya Ray