Active Learning
Active learning differs from passive "learning from examples" in that the learning algorithm itself attempts to select the most informative data for training. Since supervised labeling of data is expensive, active learning attempts to reduce the human effort needed to learn an accurate result by selecting only the most informative examples for labeling. Our work has focused on diverse ensembles for active learning and applications of active learning to problems in natural-language processing and semi-supervised learning. We have also addressed the problem of actively acquiring the most useful features values of examples as well as supervised class labels.
Ayan Acharya Ph.D. Student masterayan [at] gmail com
Bishal Barman Formerly affiliated Ph.D. Student bbarman [at] apple com
Yinon Bentor Ph.D. Student yinon [at] cs utexas edu
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Active Multitask Learning Using Both Latent and Supervised Shared Topics 2014
Ayan Acharya, Raymond J. Mooney, and Joydeep Ghosh, In Proceedings of the 2014 SIAM International Conference on Data Mining (SDM14), Philadelphia, Pennsylvania, April 2014.
Infinite-Word Topic Models for Digital Media 2014
Austin Waters, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Using Active Relocation to Aid Reinforcement Learning 2006
Lilyana Mihalkova and Raymond Mooney, In Prodeedings of the 19th International FLAIRS Conference (FLAIRS-2006), pp. 580-585, Melbourne Beach, FL, May 2006.
Active Learning for Probability Estimation using Jensen-Shannon Divergence 2005
P. Melville, S. M. Yang, M. Saar-Tsechansky and Raymond J. Mooney, In Proceedings of the 16th European Conference on Machine Learning, pp. 268--279, Porto, Portugal, October 2005.
An Expected Utility Approach to Active Feature-value Acquisition 2005
P. Melville, M. Saar-Tsechansky, F. Provost and Raymond J. Mooney, In Proceedings of the International Conference on Data Mining, pp. 745-748, Houston, TX, November 2005.
Creating Diverse Ensemble Classifiers to Reduce Supervision 2005
Prem Melville, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 141 pages. Technical Report TR-05-49.
Economical Active Feature-value Acquisition through Expected Utility Estimation 2005
P. Melville, M. Saar-Tsechansky, F. Provost and Raymond J. Mooney, In Proceedings of the KDD-05 Workshop on Utility-Based Data Mining, pp. 10-16, Chicago, IL, August 2005.
Semi-supervised Clustering: Probabilistic Models, Algorithms and Experiments 2005
Sugato Basu, PhD Thesis, University of Texas at Austin.
Active Feature-Value Acquisition for Classifier Induction 2004
Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond J. Mooney, Technical Report UT-AI-TR-04-311, Artificial Intelligence Lab, University of Texas at Austin.
Active Feature-Value Acquisition for Classifier Induction 2004
Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond J. Mooney, In Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM-2004), pp. 483-486, Brighton, UK, November 2004.
Active Semi-Supervision for Pairwise Constrained Clustering 2004
Sugato Basu, Arindam Banerjee, and Raymond J. Mooney, In Proceedings of the 2004 SIAM International Conference on Data Mining (SDM-04), April 2004.
Diverse Ensembles for Active Learning 2004
Prem Melville and Raymond J. Mooney, In Proceedings of 21st International Conference on Machine Learning (ICML-2004), pp. 584-591, Banff, Canada, July 2004.
Active Learning for Natural Language Parsing and Information Extraction 1999
Cynthia A. Thompson, Mary Elaine Califf and Raymond J. Mooney, In Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99), pp. 406-414, Bled, Slovenia, June 1999.