PUMA References output

Author	Title	Year	Journal/Proceedings	Reftype	DOI/URL
Balasubramanyan, R., Dalvi, B. B. & Cohen, W. W.	From Topic Models to Semi-supervised Learning: Biasing Mixed-Membership Models to Exploit Topic-Indicative Features in Entity Clustering. [BibTeX]	2013	ECML/PKDD (2)	inproceedings	URL
BibTeX: @inproceedings{conf/pkdd/BalasubramanyanDC13, author = {Balasubramanyan, Ramnath and Dalvi, Bhavana Bharat and Cohen, William W.}, title = {From Topic Models to Semi-supervised Learning: Biasing Mixed-Membership Models to Exploit Topic-Indicative Features in Entity Clustering.}, booktitle = {ECML/PKDD (2)}, publisher = {Springer}, year = {2013}, volume = {8189}, pages = {628-642}, url = {http://dblp.uni-trier.de/db/conf/pkdd/pkdd2013-2.html#BalasubramanyanDC13} }
Backstrom, L. & Leskovec, J.	Supervised random walks: predicting and recommending links in social networks [Abstract] [BibTeX]	2011	Proceedings of the fourth ACM international conference on Web search and data mining	inproceedings	DOI URL
Abstract: Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Although this problem has been extensively studied, the challenge of how to effectively combine the information from the network structure with rich node and edge attribute data remains largely open.</p> <p>We develop an algorithm based on <i>Supervised Random Walks</i> that naturally combines the information from the network structure with node and edge level attributes. We achieve this by using these attributes to guide a random walk on the graph. We formulate a supervised learning task where the goal is to learn a function that assigns strengths to edges in the network such that a random walker is more likely to visit the nodes to which new links will be created in the future. We develop an efficient training algorithm to directly learn the edge strength estimation function.</p> <p>Our experiments on the Facebook social graph and large collaboration networks show that our approach outperforms state-of-the-art unsupervised approaches as well as approaches that are based on feature extraction.
BibTeX: @inproceedings{backstrom2011supervised, author = {Backstrom, Lars and Leskovec, Jure}, title = {Supervised random walks: predicting and recommending links in social networks}, booktitle = {Proceedings of the fourth ACM international conference on Web search and data mining}, publisher = {ACM}, year = {2011}, pages = {635--644}, url = {http://doi.acm.org/10.1145/1935826.1935914}, doi = {http://dx.doi.org/10.1145/1935826.1935914} }
Backstrom, L. & Leskovec, J.	Supervised Random Walks: Predicting and Recommending Links in Social Networks [Abstract] [BibTeX]	2010		misc	URL
Abstract: Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Although this problem has been extensively studied, the challenge of how to effectively combine the information from the network structure with rich node and edge attribute data remains largely open. We develop an algorithm based on Supervised Random Walks that naturally combines the information from the network structure with node and edge level attributes. We achieve this by using these attributes to guide a random walk on the graph. We formulate a supervised learning task where the goal is to learn a function that assigns strengths to edges in the network such that a random walker is more likely to visit the nodes to which new links will be created in the future. We develop an efficient training algorithm to directly learn the edge strength estimation function. Our experiments on the Facebook social graph and large collaboration networks show that our approach outperforms state-of-the-art unsupervised approaches as well as approaches that are based on feature extraction.
BibTeX: @misc{backstrom2010supervised, author = {Backstrom, L. and Leskovec, J.}, title = {Supervised Random Walks: Predicting and Recommending Links in Social Networks}, year = {2010}, note = {cite arxiv:1011.4071}, url = {http://arxiv.org/abs/1011.4071} }
Ramage, D., Hall, D., Nallapati, R. & Manning, C. D.	Labeled LDA: A Supervised Topic Model for Credit Attribution in Multi-labeled Corpora [Abstract] [BibTeX]	2009	Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1	inproceedings	URL
Abstract: A significant portion of the world's text is tagged by readers on social bookmarking websites. Credit attribution is an inherent problem in these corpora because most pages have multiple tags, but the tags do not always apply with equal specificity across the whole document. Solving the credit attribution problem requires associating each word in a document with the most appropriate tags and vice versa. This paper introduces Labeled LDA, a topic model that constrains Latent Dirichlet Allocation by defining a one-to-one correspondence between LDA's latent topics and user tags. This allows Labeled LDA to directly learn word-tag correspondences. We demonstrate Labeled LDA's improved expressiveness over traditional LDA with visualizations of a corpus of tagged web pages from del.icio.us. Labeled LDA outperforms SVMs by more than 3 to 1 when extracting tag-specific document snippets. As a multi-label text classifier, our model is competitive with a discriminative baseline on a variety of datasets.
BibTeX: @inproceedings{Ramage:2009:LLS:1699510.1699543, author = {Ramage, Daniel and Hall, David and Nallapati, Ramesh and Manning, Christopher D.}, title = {Labeled LDA: A Supervised Topic Model for Credit Attribution in Multi-labeled Corpora}, booktitle = {Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1}, publisher = {Association for Computational Linguistics}, year = {2009}, pages = {248--256}, url = {http://dl.acm.org/citation.cfm?id=1699510.1699543} }
Schmidt-Thieme, L.	Compound Classification Models for Recommender Systems. [BibTeX]	2005	Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 27-30 November 2005	inproceedings	URL
BibTeX: @inproceedings{DBLP:conf/icdm/Schmidt-Thieme05, author = {Schmidt-Thieme, Lars}, title = {Compound Classification Models for Recommender Systems.}, booktitle = {Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 27-30 November 2005}, publisher = {IEEE Computer Society}, year = {2005}, pages = {378-385}, url = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.46} }
Zhu, X.	Semi-Supervised Learning Literature Survey [BibTeX]	2005		techreport	URL
BibTeX: @techreport{zhu05survey, author = {Zhu, Xiaojin}, title = {Semi-Supervised Learning Literature Survey}, year = {2005}, number = {1530}, url = {http://pages.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf} }
Basu, S., Banerjee, A. & Mooney, R. J.	Active Semi-Supervision for Pairwise Constrained Clustering [Abstract] [BibTeX]	2004	Proceedings of the SIAM International Conference on Data Mining	article	URL
Abstract: Semi-supervised clustering uses a small amount of supervised ta to aid unsupervised learning. One typical approach ecifies a limited number of must-link and cannotlink nstraints between pairs of examples. This paper esents a pairwise constrained clustering framework and a w method for actively selecting informative pairwise constraints get improved clustering performance. The clustering d active learning methods are both easily scalable large datasets, and can handle very high dimensional data. perimental and theoretical results confirm that this active erying of pairwise constraints significantly improves the curacy of clustering when given a relatively small amount supervision.
BibTeX: @article{Basu:EtAl:04, author = {Basu, Sugato and Banerjee, Arindam and Mooney, Raymond J.}, title = {Active Semi-Supervision for Pairwise Constrained Clustering}, booktitle = {Proceedings of the SIAM International Conference on Data Mining}, year = {2004}, pages = {333--344}, url = {http://www.cs.utexas.edu/users/ml/papers/semi-sdm-04.pdf} }

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