CCG: Funding

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From Bits to Information: Statistical Learning Technologies for Digital Information Management

Period: 2000-2002

Modern statistical learning approaches are expected to play a key rol in providing more powerful tools to harvest informatoin from bits, a crucial and growing problem for the Internet. The goal of this project is thus to develop a new technology for the management, organization, and search of multimedia digital information by exploiting and extending new statistical learning theories and algorithms. In the process we expect to prototype key system components and to develop scientific insights. Anticipated outcomes of the research are (1) new learning algorithms and associated representation that can be applied to categorize text, images, and video, (2) new theoretical analyses of these learning algorthms and query-answering methods and (3) demonstrations and evaluations of prototype systems for classifying and routing email messages and search, categorizing, and extracting information on the Web.

Smarter classification software for multimedia data is a prerequistite to enable a second, more intelligent wave of Internet technologies. Automatic techniques to route, organize, search information are needed to help individuals and organizations exploit the sea of data that the computer networks are creating. The success of project like this will make such a step possible and accelerate the evolution of the Internet.

Relevant Projects:

Relevant Publications:

D. Roth and W. Yih, Integer Linear Programming Inference for Conditional Random Fields. Proc. of the International Conference on Machine Learning (ICML) (2005) pp. 737--744 [bibitem]
S. Agarwal and D. Roth, Learnability of Bipartite Ranking Functions. Proc. of the Annual ACM Workshop on Computational Learning Theory (COLT) (2005) pp. 16--31 (abstract) [bibitem]
S. Agarwal, S. Har-Peled, and D. Roth, A Uniform Convergence Bound for the Area Under the ROC Curve. Artificial IntelligenceSTAT (2005) pp. 1-8 (abstract) [bibitem]
S. Agarwal, T. Graepel, R. Herbrich, and D. Roth, A Large Deviation Bound for the Area Under the ROC Curve. The Conference on Advances in Neural Information Processing Systems (NIPS) (2005) pp. 9--16 (abstract) [bibitem]
S. Agarwal, T. Graepel, R. Herbrich, S. Har-Peled, and D. Roth, Generalization Bounds for the Area Under the ROC Curve. Journal of Machine Learning Research (2005) pp. 393--425 (abstract) [bibitem]
R. Khardon, D. Roth, and R. Servedio, Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms. Journal of Machine Learning Research (2005) pp. 341-356 [bibitem]
W. Shen, X. Li, and A. Doan, Constraint-Based Entity Matching. Proceedings of the National Conference on Artificial Intelligence (AAAI) (2005) [bibitem]
X. Li and D. Roth, Learning Question Classifiers: The Role of Semantic Information. Journal of Natural Language Engineering (2005) [bibitem]
V. Punyakanok and D. Roth, Inference with Classifiers: The Phrase Identification Problem. Computational Linguistics (2005) [bibitem]
X. Li, D. Roth, and K. Small, The Role of Semantic Information in Learning Question Classifiers. Proc. of the International Joint Conference on Natural Language Processing (IJCNLP) (2004) (abstract) [bibitem]
S. Agarwal, A. Awan, and D. Roth, Learning to Detect Objects in Images via a Sparse, Part-Based Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2004) pp. 1475--1490 (abstract) [bibitem]
V. Punyakanok, D. Roth, W. Yih, and D. Zimak, Learning via Inference over Structurally Constrained Output. NIPS Workshop on Learning Structured Output (2004) (abstract) [bibitem]
S. Har-Peled, D. Roth, and D. Zimak, Constraint Classification for Multiclass Classification and Ranking. The Conference on Advances in Neural Information Processing Systems (NIPS) (2003) pp. 785--792 [bibitem]
A. Garg and D. Roth, Margin Distribution and Learning Algorithms. Proc. of the International Conference on Machine Learning (ICML) (2003) pp. 210--217 (abstract) [bibitem]
R. Khardon, D. Roth, and R. Servedio, Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms. The Conference on Advances in Neural Information Processing Systems (NIPS) (2001) [bibitem]
A. Garg, S. Har-Peled, and D. Roth, On generalization bounds, projection profile, and margin distribution. ICML (2002) pp. 171--178 (abstract) [bibitem]
C. Cumby and D. Roth, Learning with Feature Description Logics. Proc. of the International Conference Inductive Logic Programming (2002) pp. 32-47 (abstract) [bibitem]
D. Roth and W. Yih, Probabilistic Reasoning for Entity and Relation Recognition. Proc. the International Conference on Computational Linguistics (COLING) (2002) pp. 835--841 (abstract) [bibitem]
X. Li and D. Roth, Learning Question Classifiers. Proc. the International Conference on Computational Linguistics (COLING) (2002) pp. 556--562 (abstract) [bibitem]
S. Har-Peled, D. Roth, and D. Zimak, Constraint Classification: a new approach to Multiclass Classification. Proc. of the International Workshop on Algorithmic Learning Theory (ALT) (2002) pp. 135--150 (abstract) [bibitem]
M. Yang, D. Roth, and N. Ahuja, A Tale of Two Classifiers: SNoW vs. SVM in Visual Recognition. Proc. of the European Conference on Computer Vision (ECCV) (2002) pp. 685--700 (abstract) [bibitem]
D. Roth, M. Yang, and N. Ahuja, Learning to Recognize 3D Objects. Neural Computation (2002) pp. 1071--1104 (abstract) [bibitem]
D. Roth, C. Cumby, X. Li, P. Morie, R. Nagarajan, V. Punyakanok, N. Rizzolo, K. Small, and W. Yih, Question-Answering via Enhanced Understanding of Questions. TREC (2002) (abstract) [bibitem]
D. Roth and W. Yih, Relational Learning via Propositional Algorithms: An Information Extraction Case Study. Proc. of the International Joint Conference on Artificial Intelligence (IJCAI) (2001) pp. 1257--1263 (abstract) [bibitem]
D. Roth, Reasoning with Classifiers. Proc. of the European Conference on Machine Learning (ECML) (2001) pp. 506--510 [bibitem]
A. Garg and D. Roth, Understanding Probabilistic Classifiers. Proc. of the European Conference on Machine Learning (ECML) (2001) pp. 179--191 (abstract) [bibitem]
A. Garg and D. Roth, Learning Coherent Concepts. Proc. of the International Workshop on Algorithmic Learning Theory (ALT) (2001) pp. 135--150 (abstract) [bibitem]
X. Li and D. Roth, Exploring Evidence for Shallow Parsing. Proc. of the Annual Conference on Computational Natural Language Learning (CoNLL) (2001) pp. 107--110 (abstract) [bibitem]
Y. Even-Zohar and D. Roth, A Sequential Model for Multi Class Classification. Proc. of the Conference on Empirical Methods for Natural Language Processing (EMNLP) (2001) pp. 10-19 (abstract) [bibitem]
D. Roth, G. Kao, X. Li, R. Nagarajan, V. Punyakanok, N. Rizzolo, W. Yih, C. Alm, and L. Moran, Learning Components for a Question Answering System. TREC (2001) pp. 539-548 (abstract) [bibitem]
M. Yang, D. Roth, and N. Ahuja, Face Detection Using Large Margin Classifiers. Proceedings of IEEE International Conference on Image Processing (2001) pp. 665--668 (abstract) [bibitem]
D. Roth, Learning in Natural Language: Theory and Algorithmic Approaches. Proc. of the Annual Conference on Computational Natural Language Learning (CoNLL) (2000) pp. 1--6 (abstract) [bibitem]
V. Punyakanok and D. Roth, Shallow Parsing by Inferencing with Classifiers. Proc. of the Annual Conference on Computational Natural Language Learning (CoNLL) (2000) pp. 107--110 (abstract) [bibitem]