Bibliography

Publications of the year

Articles in International Peer-Reviewed Journals

• 1C. Braud, P. Denis.
  
  Identifier les relations discursives implicites en combinant données naturelles et données artificielles, in: Traitement Automatique des Langues, December 2014, vol. 55, n^o 1, 31 p.
  
  https://hal.inria.fr/hal-01094346

International Conferences with Proceedings

• 2C. Braud, P. Denis.
  
  Combining Natural and Artificial Examples to Improve Implicit Discourse Relation Identification, in: coling, Dublin, Ireland, August 2014.
  
  https://hal.inria.fr/hal-01017151
• 3G. Laurence, A. Lemay, J. Niehren, S. Staworko, M. Tommasi.
  
  Learning Sequential Tree-to-Word Transducers, in: 8th International Conference on Language and Automata Theory and Applications, Madrid, Spain, Springer, March 2014.
  
  https://hal.inria.fr/hal-00912969
• 4T. Ricatte, R. Gilleron, M. Tommasi.
  
  Hypernode Graphs for Spectral Learning on Binary Relations over Sets, in: ECML/PKDD - 7th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Nancy, France, Machine Learning and Knowledge Discovery in Databases, September 2014, Paper accepted for publication at ECML/PKDD 2014.
  
  https://hal.inria.fr/hal-01017025

Conferences without Proceedings

• 5D. Chatel, P. Denis, M. Tommasi.
  
  Clustering Spectral avec Contraintes de Paires réglées par Noyaux Gaussiens, in: CAP 2014, Saint-Etienne, France, July 2014.
  
  https://hal.inria.fr/hal-01105339
• 6D. Chatel, P. Denis, M. Tommasi.
  
  Fast Gaussian Pairwise Constrained Spectral Clustering, in: ECML/PKDD - 7th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Nancy, France, September 2014, pp. 242 - 257. [ DOI : 10.1007/978-3-662-44848-9_16 ]
  
  https://hal.inria.fr/hal-01017269
• 7T. Ricatte, R. Gilleron, M. Tommasi.
  
  Hypernode Graphs for Spectral Learning on Binary Relations over Sets, in: Conférence Francophone sur l'Apprentissage Automatique (Cap 2014), Saint-Etienne, France, July 2014. [ DOI : 10.1007/978-3-662-44851-9_42 ]
  
  https://hal.inria.fr/hal-01104618

Other Publications

• 8T. Ricatte, R. Gilleron, M. Tommasi.
  
  Hypernode Graphs for Learning from Binary Relations between Groups in Networks, December 2014, Networks: From Graphs to Rich Data, NIPS Workshop.
  
  https://hal.inria.fr/hal-01088036

References in notes

• 9A. Alexandrescu, K. Kirchhoff.
  
  Graph-based learning for phonetic classification, in: IEEE Workshop on Automatic Speech Recognition & Understanding, ASRU 2007, Kyoto, Japan, December 9-13, 2007, pp. 359-364.
• 10M.-F. Balcan, A. Blum, P. P. Choi, J. Lafferty, B. Pantano, M. R. Rwebangira, X. Zhu.
  
  Person Identification in Webcam Images: An Application of Semi-Supervised Learning, in: ICML2005 Workshop on Learning with Partially Classified Training Data, 2005.
• 11M. Belkin, P. Niyogi.
  
  Towards a Theoretical Foundation for Laplacian-Based Manifold Methods, in: Journal of Computer and System Sciences, 2008, vol. 74, n^o 8, pp. 1289-1308.
• 12A. Bellet, A. Habrard, M. Sebban.
  
  A Survey on Metric Learning for Feature Vectors and Structured Data, in: CoRR, 2013, vol. abs/1306.6709.
• 13P. J. Bickel, A. Chen.
  
  A nonparametric view of network models and Newman–Girvan and other modularities, in: Proceedings of the National Academy of Sciences, 2009, vol. 106, pp. 21068–21073.
• 14P. Blau.
  
  Inequality and Heterogeneity: A Primitive Theory of Social Structure, MACMILLAN Company, 1977.
  
  http://books.google.fr/books?id=jvq2AAAAIAAJ
• 15N. Cesa-Bianchi, C. Gentile, F. Vitale, G. Zappella.
  
  A Linear Time Active Learning Algorithm for Link Classification, in: Proc of NIPS, 2012, pp. 1619-1627.
• 16H. Chang, D.-Y. Yeung.
  
  Graph Laplacian Kernels for Object Classification from a Single Example, in: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2, Washington, DC, USA, CVPR '06, IEEE Computer Society, 2006, pp. 2011–2016.
  
  http://dx.doi.org/10.1109/CVPR.2006.128
• 17D. Das, S. Petrov.
  
  Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections, in: ACL, 2011, pp. 600-609.
• 18E. R. Fernandes, U. Brefeld.
  
  Learning from Partially Annotated Sequences, in: ECML/PKDD, 2011, pp. 407-422.
• 19A. Freno, G. C. Garriga, M. Keller.
  
  Learning to Recommend Links Using Graph Structure and Node Content, in: NIPS Workshop on Choice Models and Preference Learning, 2011.
• 20A. Freno, M. Keller, G. C. Garriga, M. Tommasi.
  
  Spectral Estimation of Conditional Random Graph Models for Large-Scale Network data, in: Proc. of UAI 2012, Avalon, États-Unis, 2012.
  
  http://hal.inria.fr/hal-00714446
• 21A. B. Goldberg, X. Zhu.
  
  Seeing stars when there aren't many stars: graph-based semi-supervised learning for sentiment categorization, in: Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing, Stroudsburg, PA, USA, TextGraphs-1, Association for Computational Linguistics, 2006, pp. 45–52.
  
  http://dl.acm.org/citation.cfm?id=1654758.1654769
• 22A. Goldenberg, A. X. Zheng, S. E. Fienberg.
  
  A Survey of Statistical Network Models, Foundations and trends in machine learning, Now Publishers, 2010.
  
  http://books.google.fr/books?id=gPGgcOf95moC
• 23M. Gomez-Rodriguez, J. Leskovec, A. Krause.
  
  Inferring networks of diffusion and influence, in: Proc. of KDD, 2010, pp. 1019-1028.
• 24M. Herbster, S. Pasteris, F. Vitale.
  
  Online Sum-Product Computation Over Trees, in: Proc. of NIPS, 2012, pp. 2879-2887.
• 25M. McPherson, L. S. Lovin, J. M. Cook.
  
  Birds of a Feather: Homophily in Social Networks, in: Annual Review of Sociology, 2001, vol. 27, n^o 1, pp. 415–444.
  
  http://dx.doi.org/10.1146/annurev.soc.27.1.415
• 26A. Nenkova, K. McKeown.
  
  A Survey of Text Summarization Techniques, in: Mining Text Data, Springer, 2012, pp. 43-76.
• 27H. Shin, K. Tsuda, B. Schölkopf.
  
  Protein functional class prediction with a combined graph, in: Expert Syst. Appl., March 2009, vol. 36, n^o 2, pp. 3284–3292.
  
  http://dx.doi.org/10.1016/j.eswa.2008.01.006
• 28S. Singh, A. Subramanya, F. C. N. Pereira, A. McCallum.
  
  Large-Scale Cross-Document Coreference Using Distributed Inference and Hierarchical Models, in: ACL, 2011, pp. 793-803.
• 29M. Speriosu, N. Sudan, S. Upadhyay, J. Baldridge.
  
  Twitter Polarity Classification with Label Propagation over Lexical Links and the Follower Graph, in: Proceedings of the First Workshop on Unsupervised Methods in NLP, Edinburgh, Scotland, 2011.
• 30A. Subramanya, S. Petrov, F. C. N. Pereira.
  
  Efficient Graph-Based Semi-Supervised Learning of Structured Tagging Models, in: EMNLP, 2010, pp. 167-176.
• 31F. Vitale, N. Cesa-Bianchi, C. Gentile, G. Zappella.
  
  See the Tree Through the Lines: The Shazoo Algorithm, in: Proc of NIPS, 2011, pp. 1584-1592.
• 32L. Wang, S. N. Kim, T. Baldwin.
  
  The Utility of Discourse Structure in Identifying Resolved Threads in Technical User Forums, in: COLING, 2012, pp. 2739-2756.
• 33K. K. Yuzong Liu.
  
  Graph-Based Semi-Supervised Learning for Phone and Segment Classification, in: Proceedings of Interspeech, Lyon, France, 2013.
• 34X. Zhu, Z. Ghahramani, J. Lafferty.
  
  Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions, in: Proc. of ICML, 2003, pp. 912-919.