Major publications by the team in recent years
  • 1A. Freno, M. Keller, M. Tommasi.

    Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling, in: Neural Information Processing Systems (NIPS), Lake Tahoe, United States, Advances in Neural Information Processing Systems, MIT Press, December 2012, vol. 25.

  • 2O. Kuželka, Y. Wang, J. Ramon.

    Bounds for Learning from Evolutionary-Related Data in the Realizable Case, in: International Joint Conference on Artificial Intelligence (IJCAI), New York, United States, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) 2016, July 2016.

  • 3E. Lassalle, P. Denis.

    Improving pairwise coreference models through feature space hierarchy learning, in: ACL 2013 - Annual meeting of the Association for Computational Linguistics, Sofia, Bulgaria, Association for Computational Linguistics, August 2013.

  • 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.

  • 5G. Papa, S. Clémençon, A. Bellet.

    On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability, in: Annual Conference on Neural Information Processing Systems (NIPS 2016), Barcelone, Spain, December 2016.

  • 6T. 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.

Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 9P. Besson, N. Carrière, S. K. Bandt, M. Tommasi, X. Leclerc, P. Derambure, R. Lopes, L. Tyvaert.

    Whole-brain high-resolution structural connectome: inter-subject validation and application to the anatomical segmentation of the striatum, in: Brain Topography, May 2017, vol. 30, no 3, pp. 291-302. [ DOI : 10.1007/s10548-017-0548-0 ]

  • 10T. Le Van, S. Nijssen, M. Van Leeuwen, L. De Raedt.

    Semiring Rank Matrix Factorization, in: IEEE Transactions on Knowledge and Data Engineering, August 2017, vol. 29, no 8, pp. 1737 - 1750. [ DOI : 10.1109/TKDE.2017.2688374 ]

  • 11C. Pelekis, J. Ramon, Y. Wang.

    Hölder-type inequalities and their applications to concentration and correlation bounds, in: Indagationes Mathematicae, 2017, vol. 28, no 1, pp. 170–182. [ DOI : 10.1016/j.indag.2016.11.017 ]


International Conferences with Proceedings

  • 12M. Dehouck, P. Denis.

    Delexicalized Word Embeddings for Cross-lingual Dependency Parsing, in: EACL, Valencia, Spain, EACL 2017, April 2017, vol. 1, pp. 241 - 250. [ DOI : 10.18653/v1/E17-1023 ]

  • 13P. Denis, L. Ralaivola.

    Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm, in: European Chapter of the Association for Computational Linguistics, Valencia, Spain, April 2017, pp. 775 - 784. [ DOI : 10.18653/v1/E17-1073 ]

  • 14G. Le Falher, N. Cesa-Bianchi, C. Gentile, F. Vitale.

    On the Troll-Trust Model for Edge Sign Prediction in Social Networks, in: AISTATS 2017 - 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, United States, April 2017.

  • 15P. Vanhaesebrouck, A. Bellet, M. Tommasi.

    Decentralized Collaborative Learning of Personalized Models over Networks, in: International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, Florida., United States, April 2017, https://arxiv.org/abs/1610.05202.


Conferences without Proceedings

  • 16A. Bellet, R. Guerraoui, M. Taziki, M. Tommasi.

    Personalized and Private Peer-to-Peer Machine Learning, in: NIPS 2017 Workshop on Machine Learning on the Phone and other Consumer Devices, Long Beach, United States, 2017.

  • 17T. Le Van, A. Bellet, J. Ramon.

    Decentralised and Privacy-Aware Learning of Traversal Time Models, in: ECML PKDD 2017 - workshop DMSC - Data Mining with Secure Computation, Skopje, Macedonia, September 2017, pp. 1-5.


Internal Reports

References in notes
  • 21A. 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.
  • 22M.-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.
  • 23M. 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.
  • 24A. Bellet, A. Habrard, M. Sebban.

    A Survey on Metric Learning for Feature Vectors and Structured Data, in: CoRR, 2013, vol. abs/1306.6709.
  • 25A. Bellet, A. Habrard, M. Sebban.

    Metric Learning, Morgan & Claypool Publishers, 2015.
  • 26P. 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.
  • 27P. Blau.

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

  • 28C. Braud, P. Denis.

    Combining Natural and Artificial Examples to Improve Implicit Discourse Relation Identification, in: coling, Dublin, Ireland, August 2014.

  • 29H. 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.

  • 30D. 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 ]

  • 31D. Das, S. Petrov.

    Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections, in: ACL, 2011, pp. 600-609.
  • 32P. 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.

  • 33E. R. Fernandes, U. Brefeld.

    Learning from Partially Annotated Sequences, in: ECML/PKDD, 2011, pp. 407-422.
  • 34A. 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.

  • 35A. Goldenberg, A. X. Zheng, S. E. Fienberg.

    A Survey of Statistical Network Models, Foundations and trends in machine learning, Now Publishers, 2010.

  • 36M. Gomez-Rodriguez, J. Leskovec, A. Krause.

    Inferring networks of diffusion and influence, in: Proc. of KDD, 2010, pp. 1019-1028.
  • 37M. 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.

  • 38A. Nenkova, K. McKeown.

    A Survey of Text Summarization Techniques, in: Mining Text Data, Springer, 2012, pp. 43-76.
  • 39T. 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.

  • 40H. 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.

  • 41S. 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.
  • 42M. 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.
  • 43A. Subramanya, S. Petrov, F. C. N. Pereira.

    Efficient Graph-Based Semi-Supervised Learning of Structured Tagging Models, in: EMNLP, 2010, pp. 167-176.
  • 44F. 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.
  • 45L. 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.
  • 46K. K. Yuzong Liu.

    Graph-Based Semi-Supervised Learning for Phone and Segment Classification, in: Proceedings of Interspeech, Lyon, France, 2013.
  • 47X. Zhu, Z. Ghahramani, J. Lafferty.

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