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Semi-Supervised Learning
In many learning tasks, there is a large supply of unlabeled data but insufficient labeled data since it can be expensive to generate. Semi-supervised learning combines labeled and unlabeled data during training to improve performance. Semi-supervised learning is applicable to both classification and clustering. In supervised classification, there is a known, fixed set of categories and category-labeled training data is used to induce a classification function. In
semi-supervised classification
, training also exploits additional unlabeled data, frequently resulting in a more accurate classification function. In
semi-supervised clustering
, some labeled data is used along with the unlabeled data to obtain a better clustering.
People
Dan Garrette
Ph.D. Alumni
dhg [at] cs utexas edu
Elad Liebman
Ph.D. Student
eladlieb [at] cs utexas edu
Publications
[Expand to show all 33]
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Dialog as a Vehicle for Lifelong Learning of Grounded Language Understanding Systems
2020
Aishwarya Padmakumar, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Continually Improving Grounded Natural Language Understanding through Human-Robot Dialog
2018
Jesse Thomason, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Guiding Exploratory Behaviors for Multi-Modal Grounding of Linguistic Descriptions
2018
Jesse Thomason, Jivko Sinapov, Raymond Mooney, Peter Stone, In
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)
, February 2018.
Interaction and Autonomy in RoboCup@Home and Building-Wide Intelligence
2018
Justin Hart, Harel Yedidsion, Yuqian Jiang, Nick Walker, Rishi Shah, Jesse Thomason, Aishwarya Padmakumar, Rolando Fernandez, Jivko Sinapov, Raymond Mooney, Peter Stone, In
Artificial Intelligence (AI) for Human-Robot Interaction (HRI) symposium, AAAI Fall Symposium Series
, Arlington, Virginia, October 2018.
Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog
2018
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, and Raymond J. Mooney, In
RSS Workshop on Models and Representations for Natural Human-Robot Communication (MRHRC-18). Robotics: Science and Systems (RSS)
, June 2018.
Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog
2018
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, and Raymond J. Mooney, In
Late-breaking Track at the SIGDIAL Special Session on Physically Situated Dialogue (RoboDIAL-18)
, Melbourne, Australia, July 2018.
Improving Black-box Speech Recognition using Semantic Parsing
2017
Rodolfo Corona, Jesse Thomason, and Raymond J. Mooney, In
Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP-17)
, pp. 122-127, Taipei, Taiwan, November 2017.
A Supertag-Context Model for Weakly-Supervised CCG Parser Learning
2015
Dan Garrette, Chris Dyer, Jason Baldridge, and Noah A. Smith , In
Proceedings of the 2015 Conference on Computational Natural Language Learning (CoNLL-2015)
, pp. 22--31, Beijing, China 2015.
Inducing Grammars from Linguistic Universals and Realistic Amounts of Supervision
2015
Dan Garrette, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Knowledge Transfer Using Latent Variable Models
2015
Ayan Acharya, PhD Thesis, Department of Electrical and Computer Engineering, The University of Texas at Austin.
Weakly-Supervised Grammar-Informed Bayesian CCG Parser Learning
2015
Dan Garrette, Chris Dyer, Jason Baldridge, Noah A. Smith, In
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15)
, Austin, TX, January 2015.
Weakly-Supervised Bayesian Learning of a CCG Supertagger
2014
Dan Garrette, Chris Dyer, Jason Baldridge, and Noah A. Smith, In
Proceedings of the Eighteenth Conference on Computational Natural Language Learning (CoNLL-2014)
, pp. 141--150, Baltimore, MD, June 2014.
Learning a Part-of-Speech Tagger from Two Hours of Annotation
2013
Dan Garrette, Jason Baldridge ,
Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT-13)
(2013), pp. 138--147.
Real-World Semi-Supervised Learning of POS-Taggers for Low-Resource Languages
2013
Dan Garrette, Jason Mielens, and Jason Baldridge ,
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL-2013)
(2013), pp. 583--592.
Type-Supervised Hidden Markov Models for Part-of-Speech Tagging with Incomplete Tag Dictionaries
2012
Dan Garrette and Jason Baldridge, In
Proceedings of the Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2012)
, pp. 821--831, Jeju, Korea, July 2012.
Semi-supervised graph clustering: a kernel approach
2009
Brian Kulis, Sugato Basu, Inderjit Dhillon, and Raymond Mooney,
Machine Learning Journal
, Vol. 74, 1 (2009), pp. 1-22.
Watch, Listen & Learn: Co-training on Captioned Images and Videos
2008
Sonal Gupta, Joohyun Kim, Kristen Grauman and Raymond Mooney, In
Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD)
, pp. 457--472, Antwerp Belgium, September 2008.
Semi-Supervised Learning for Semantic Parsing using Support Vector Machines
2007
Rohit J. Kate and Raymond J. Mooney, In
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Short Papers (NAACL/HLT-2007)
, pp. 81--84, Rochester...
Learnable Similarity Functions and Their Application to Record Linkage and Clustering
2006
Mikhail Bilenko, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 136 pages.
Probabilistic Semi-Supervised Clustering with Constraints
2006
Sugato Basu, Mikhail Bilenko, Arindam Banerjee and Raymond J. Mooney, In
Semi-Supervised Learning
, O. Chapelle and B. Sch{"{o}}lkopf and A. Zien (Eds.), Cambridge, MA 2006. MIT Press.
Semi-supervised Clustering: Probabilistic Models, Algorithms and Experiments
2005
Sugato Basu, PhD Thesis, University of Texas at Austin.
Semi-supervised Graph Clustering: A Kernel Approach
2005
B. Kulis, S. Basu, I. Dhillon and Raymond J. Mooney, In
Proceedings of the 22nd International Conference on Machine Learning
, pp. 457--464, Bonn, Germany, August 2005. (Distinguished Student Paper Award).
A Comparison of Inference Techniques for Semi-supervised Clustering with Hidden Markov Random Fields
2004
Mikhail Bilenko and Sugato Basu, In
Proceedings of the ICML-2004 Workshop on Statistical Relational Learning and its Connections to Other Fields (SRL-2004)
, Banff, Canada, July 2004.
A Probabilistic Framework for Semi-Supervised Clustering
2004
Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney, In
Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004)
, pp. 59-68, Seattle, WA, August 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.
Integrating Constraints and Metric Learning in Semi-Supervised Clustering
2004
Mikhail Bilenko, Sugato Basu, and Raymond J. Mooney, In
Proceedings of 21st International Conference on Machine Learning (ICML-2004)
, pp. 81-88, Banff, Canada, July 2004.
Learnable Similarity Functions and Their Applications to Clustering and Record Linkage
2004
Mikhail Bilenko, In
Proceedings of the Ninth AAAI/SIGART Doctoral Consortium
, pp. 981--982, San Jose, CA, July 2004.
Semi-supervised Clustering with Limited Background Knowledge
2004
Sugato Basu, In
Proceedings of the Ninth AAAI/SIGART Doctoral Consortium
, pp. 979--980, San Jose, CA, July 2004.
Semi-supervised Clustering: Learning with Limited User Feedback
2004
Sugato Basu, Technical Report, Cornell University.
Semisupervised Clustering for Intelligent User Management
2004
Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney, In
Proceedings of the IBM Austin Center for Advanced Studies 5th Annual Austin CAS Conference
, Austin, TX, February 2004.
Comparing and Unifying Search-Based and Similarity-Based Approaches to Semi-Supervised Clustering
2003
Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney, In
Proceedings of the ICML-2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining
, pp. 42-49, Washington, DC 2003.
Learnable Similarity Functions and Their Applications to Record Linkage and Clustering
2003
Mikhail Bilenko, unpublished. Doctoral Dissertation Proposal, University of Texas at Austin.
Semi-supervised Clustering by Seeding
2002
Sugato Basu, Arindam Banerjee, and Raymond J. Mooney, In
Proceedings of 19th International Conference on Machine Learning (ICML-2002)
, pp. 19-26 2002.
Software/Data
Improving Black-box Speech Recognition using Semantic Parsing
The data used in the paper Improving Black-box Speech Recognition using Semantic Parsing, IJCNLP 2017 can be downloaded ...
2019
Labs
Machine Learning