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Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

International Conferences with Proceedings

  • 2C. Braud, P. Denis.

    Comparing Word Representations for Implicit Discourse Relation Classification, in: Empirical Methods in Natural Language Processing (EMNLP 2015), Lisbonne, Portugal, September 2015.

    https://hal.inria.fr/hal-01185927
  • 3I. Colin, A. Bellet, J. Salmon, S. Clémençon.

    Extending Gossip Algorithms to Distributed Estimation of U-statistics, in: Annual Conference on Neural Information Processing Systems (NIPS), Montréal, Canada, December 2015.

    https://hal.inria.fr/hal-01214665
  • 4E. Lassalle, P. Denis.

    Joint Anaphoricity Detection and Coreference Resolution with Constrained Latent Structures, in: AAAI Conference on Artificial Intelligence (AAAI 2015), Austin, Texas, United States, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015), January 2015.

    https://hal.inria.fr/hal-01205189
  • 5G. Le Falher, A. Gionis, M. Mathioudakis.

    Where is the Soho of Rome? Measures and algorithms for finding similar neighborhoods in cities, in: 9th AAAI Conference on Web and Social Media - ICWSM 2015, Oxford, United Kingdom, May 2015.

    https://hal.archives-ouvertes.fr/hal-01134117
  • 6G. Papa, S. Clémençon, A. Bellet.

    SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk, in: Annual Conference on Neural Information Processing Systems (NIPS), Montréal, Canada, December 2015.

    https://hal.inria.fr/hal-01214667

Conferences without Proceedings

  • 7N. Cesa-Bianchi, C. Gentile, F. Vitale, G. Zappella.

    Efficient Link Classification in Social Networks, in: International Conference on Computational Social Science, Helsinki, Finland, June 2015.

    https://hal.inria.fr/hal-01245747

Internal Reports

  • 8T. Ricatte, R. Gilleron, M. Tommasi.

    Hypernode Graphs for Learning from Binary Relations between Groups in Networks, Inria Lille, January 2015.

    https://hal.inria.fr/hal-01247103

Scientific Popularization

  • 9P. Philippe, M. Tommasi, T. Vieville, C. De La Higuera.

    L’apprentissage automatique : le diable n’est pas dans l’algorithme, June 2015, Article sur http://binaire.blog.lemonde.fr.

    https://hal.inria.fr/hal-01246178

Other Publications

  • 10G. Laurence, A. Lemay, J. Niehren, S. Staworko, M. Tommasi.

    Sequential Tree-to-Word Transducers: Normalization, Minimization, and Learning, August 2015, Long version of Lata 14 paper.

    https://hal.archives-ouvertes.fr/hal-01186993
References in notes
  • 11I. Abraham, O. Neiman.

    Using petal-decompositions to build a low stretch spanning tree, in: Proceedings of the 44th symposium on Theory of Computing - STOC '12, 2012.

    http://dx.doi.org/10.1145/2213977.2214015
  • 12A. 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, 2007, pp. 359-364.
  • 13M.-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.
  • 14M. Belkin, P. Niyogi.

    Towards a Theoretical Foundation for Laplacian-Based Manifold Methods, in: Journal of Computer and System Sciences, 2008, vol. 74, no 8, pp. 1289-1308.
  • 15A. Bellet, A. Habrard, M. Sebban.

    A Survey on Metric Learning for Feature Vectors and Structured Data, in: CoRR, 2013, vol. abs/1306.6709.
  • 16A. Bellet, A. Habrard, M. Sebban.

    Metric Learning, Morgan & Claypool Publishers, 2015.
  • 17P. 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.
  • 18P. Blau.

    Inequality and Heterogeneity: A Primitive Theory of Social Structure, MACMILLAN Company, 1977.

    http://books.google.fr/books?id=jvq2AAAAIAAJ
  • 19C. 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
  • 20N. 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.
  • 21H. 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
  • 22D. 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
  • 23D. Das, S. Petrov.

    Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections, in: ACL, 2011, pp. 600-609.
  • 24P. Denis, P. Muller.

    Predicting globally-coherent temporal structures from texts via endpoint inference and graph decomposition, in: IJCAI-11 - International Joint Conference on Artificial Intelligence, Barcelone, Espagne, 2011.

    http://hal.inria.fr/inria-00614765
  • 25E. R. Fernandes, U. Brefeld.

    Learning from Partially Annotated Sequences, in: ECML/PKDD, 2011, pp. 407-422.
  • 26A. 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.
  • 27A. 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
  • 28A. 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
  • 29A. 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
  • 30M. Gomez-Rodriguez, J. Leskovec, A. Krause.

    Inferring networks of diffusion and influence, in: Proc. of KDD, 2010, pp. 1019-1028.
  • 31M. Herbster, S. Pasteris, F. Vitale.

    Online Sum-Product Computation Over Trees, in: Proc. of NIPS, 2012, pp. 2879-2887.
  • 32M. McPherson, L. S. Lovin, J. M. Cook.

    Birds of a Feather: Homophily in Social Networks, in: Annual Review of Sociology, 2001, vol. 27, no 1, pp. 415–444.

    http://dx.doi.org/10.1146/annurev.soc.27.1.415
  • 33A. Nenkova, K. McKeown.

    A Survey of Text Summarization Techniques, in: Mining Text Data, Springer, 2012, pp. 43-76.
  • 34T. 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.

    https://hal.inria.fr/hal-01017025
  • 35H. Shin, K. Tsuda, B. Schölkopf.

    Protein functional class prediction with a combined graph, in: Expert Syst. Appl., March 2009, vol. 36, no 2, pp. 3284–3292.

    http://dx.doi.org/10.1016/j.eswa.2008.01.006
  • 36S. 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.
  • 37M. 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.
  • 38A. Subramanya, S. Petrov, F. C. N. Pereira.

    Efficient Graph-Based Semi-Supervised Learning of Structured Tagging Models, in: EMNLP, 2010, pp. 167-176.
  • 39F. 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.
  • 40L. 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.
  • 41K. K. Yuzong Liu.

    Graph-Based Semi-Supervised Learning for Phone and Segment Classification, in: Proceedings of Interspeech, Lyon, France, 2013.
  • 42X. Zhu, Z. Ghahramani, J. Lafferty.

    Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions, in: Proc. of ICML, 2003, pp. 912-919.


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