Overall Objectives
Application Domains
New Software and Platforms
Overall Objectives
Application Domains
New Software and Platforms


Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 2R. Cannoodt, J. Ruyssinck, J. Ramon, K. De Preter, Y. Saeys.

    IncGraph: Incremental graphlet counting for topology optimisation, in: PLoS ONE, April 2018, vol. 13, no 4. [ DOI : 10.1371/ journal.pone.0195997 ]

  • 3B. Cappelle, P. Denis, M. Keller.

    Facing the facts of fake: a distributional semantics and corpus annotation approach, in: Yearbook of the German Cognitive Linguistics Association, November 2018.

  • 4C. Pelekis, J. Ramon, Y. Wang.

    On the Bernstein-Hoeffding method, in: Bulletin of the Hellenic Mathematical Society, June 2018, vol. 62, pp. 31-43.

  • 5I. Ravkic, M. Znidaršič, J. Ramon, J. Davis.

    Graph sampling with applications to estimating the number of pattern embeddings and the parameters of a statistical relational model, in: Data Mining and Knowledge Discovery, 2018, 36 p.

  • 6L. Schietgat, C. Vens, J. Ramon, R. Cerri, C. N. Fischer, E. d. Costa, C. M. A. Carareto, H. Blockeel.

    A machine learning based framework to identify and classify long terminal repeat retrotransposons, in: PLoS Computational Biology, April 2018, vol. 14, no 4, pp. 1-21. [ DOI : 10.1371/journal.pcbi.1006097 ]

  • 7W. Zheng, A. Bellet, P. Gallinari.

    A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Norm, in: Machine Learning, 2018.


International Conferences with Proceedings

  • 8M. Ailem, B. Zhang, A. Bellet, P. Denis, F. Sha.

    A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images, in: Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), Brussels, Belgium, 2018.

  • 9A. Bellet, R. Guerraoui, M. Taziki, M. Tommasi.

    Personalized and Private Peer-to-Peer Machine Learning, in: AISTATS 2018 - 21st International Conference on Artificial Intelligence and Statistics, Lanzarote, Spain, April 2018, pp. 1-20, https://arxiv.org/abs/1705.08435.

  • 10N. Cesa-Bianchi, C. Gentile, Y. Mansour.

    Nonstochastic Bandits with Composite Anonymous Feedback, in: COLT 2018 - 31st Annual Conference on Learning Theory, Stockholm, Sweden, July 2018, vol. 75, pp. 1 - 23.

  • 11M. Dehouck, P. Denis.

    A Framework for Understanding the Role of Morphology in Universal Dependency Parsing, in: EMNLP 2018 - Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, Proceedings of EMNLP 2018, October 2018.

  • 12S. Pasteris, F. Vitale, C. Gentile, M. Herbster.

    On Similarity Prediction and Pairwise Clustering, in: ALT 2018 - 29th International Conference on Algorithmic Learning Theory, Lanzarote, Spain, April 2018, vol. 83, pp. 1 - 28.

  • 13F. Vitale, N. Parotsidis, C. Gentile.

    Online Reciprocal Recommendation with Theoretical Performance Guarantees, in: NIPS 2018 - 32nd Conference on Neural Information Processing Systems, Montreal, Canada, December 2018.

  • 14R. Vogel, A. Bellet, S. Clémençon.

    A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization, in: International Conference on Machine Learning (ICML 2018), Stockholm, Sweden, 2018.


Internal Reports

  • 15P. Dellenbach, A. Bellet, J. Ramon.

    Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries, Inria, 2018.

  • 16K. Liu, A. Bellet.

    Escaping the Curse of Dimensionality in Similarity Learning: Efficient Frank-Wolfe Algorithm and Generalization Bounds, Inria, 2018.


Other Publications

References in notes
  • 20A. 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.
  • 21M.-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.
  • 22M. 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.
  • 23A. Bellet, A. Habrard, M. Sebban.

    A Survey on Metric Learning for Feature Vectors and Structured Data, in: CoRR, 2013, vol. abs/1306.6709.
  • 24A. Bellet, A. Habrard, M. Sebban.

    Metric Learning, Morgan & Claypool Publishers, 2015.
  • 25P. 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.
  • 26P. Blau.

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

  • 27C. Braud, P. Denis.

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

  • 28H. 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.

  • 29D. 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 ]

  • 30D. Das, S. Petrov.

    Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections, in: ACL, 2011, pp. 600-609.
  • 31P. 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.

  • 32E. R. Fernandes, U. Brefeld.

    Learning from Partially Annotated Sequences, in: ECML/PKDD, 2011, pp. 407-422.
  • 33A. 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.

  • 34A. Goldenberg, A. X. Zheng, S. E. Fienberg.

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

  • 35M. Gomez-Rodriguez, J. Leskovec, A. Krause.

    Inferring networks of diffusion and influence, in: Proc. of KDD, 2010, pp. 1019-1028.
  • 36M. 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.

  • 37A. Nenkova, K. McKeown.

    A Survey of Text Summarization Techniques, in: Mining Text Data, Springer, 2012, pp. 43-76.
  • 38T. 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.

  • 39H. 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.

  • 40S. 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.
  • 41M. 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.
  • 42A. Subramanya, S. Petrov, F. C. N. Pereira.

    Efficient Graph-Based Semi-Supervised Learning of Structured Tagging Models, in: EMNLP, 2010, pp. 167-176.
  • 43F. 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.
  • 44L. 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.
  • 45K. K. Yuzong Liu.

    Graph-Based Semi-Supervised Learning for Phone and Segment Classification, in: Proceedings of Interspeech, Lyon, France, 2013.
  • 46X. Zhu, Z. Ghahramani, J. Lafferty.

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