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MAGNET - 2018
MAGNET - 2018
Project-Team Magnet
Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
Project-Team Magnet
Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography


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Bibliography

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 ]

    https://hal.inria.fr/hal-01814675
  • 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.

    https://hal.archives-ouvertes.fr/hal-01959609
  • 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.

    https://hal.inria.fr/hal-01814651
  • 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.

    https://hal.inria.fr/hal-01725971
  • 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 ]

    https://hal.inria.fr/hal-01814669
  • 7W. Zheng, A. Bellet, P. Gallinari.

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

    https://hal.inria.fr/hal-01922994

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.

    https://hal.inria.fr/hal-01922985
  • 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.

    https://hal.inria.fr/hal-01745796
  • 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.

    https://hal.inria.fr/hal-01916981
  • 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.

    https://hal.archives-ouvertes.fr/hal-01943934
  • 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.

    https://hal.inria.fr/hal-01916976
  • 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.

    https://hal.inria.fr/hal-01916979
  • 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.

    https://hal.inria.fr/hal-01922988

Internal Reports

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

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

    https://hal.inria.fr/hal-01923000
  • 16K. Liu, A. Bellet.

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

    https://hal.inria.fr/hal-01923006

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.

    http://books.google.fr/books?id=jvq2AAAAIAAJ
  • 27C. 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
  • 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.

    http://dx.doi.org/10.1109/CVPR.2006.128
  • 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 ]

    https://hal.inria.fr/hal-01017269
  • 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.

    http://hal.inria.fr/inria-00614765
  • 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.

    http://dl.acm.org/citation.cfm?id=1654758.1654769
  • 34A. 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
  • 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.

    http://dx.doi.org/10.1146/annurev.soc.27.1.415
  • 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.

    https://hal.inria.fr/hal-01017025
  • 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.

    http://dx.doi.org/10.1016/j.eswa.2008.01.006
  • 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.


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