EN FR
EN FR


previous Previous | up Home


Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 2A. Coutant, K. Roper, D. Trejo-Banos, D. Bouthinon, M. Carpenter, J. Grzebyta, G. Santini, H. Soldano, M. Elati, J. Ramon, C. Rouveirol, L. N. Soldatova, R. D. King.

    Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast, in: Proceedings of the National Academy of Sciences of the United States of America , 2019, vol. 116, no 36, pp. 18142-18147. [ DOI : 10.1073/pnas.1900548116 ]

    https://hal.sorbonne-universite.fr/hal-02297702
  • 3K. Liu, A. Bellet.

    Escaping the Curse of Dimensionality in Similarity Learning: Efficient Frank-Wolfe Algorithm and Generalization Bounds, in: Neurocomputing, 2019, vol. 333, pp. 185-199.

    https://hal.inria.fr/hal-02166425
  • 4A. May, A. Bagheri Garakani, Z. Lu, D. Guo, K. Liu, A. Bellet, L. Fan, M. Collins, D. Hsu, B. Kingsbury, M. Picheny, F. Sha.

    Kernel Approximation Methods for Speech Recognition, in: Journal of Machine Learning Research, 2019, vol. 20, pp. 1 - 36.

    https://hal.inria.fr/hal-02166422

International Conferences with Proceedings

  • 5M. Asadi, M. S. Talebi, H. Bourel, O.-A. Maillard.

    Model-Based Reinforcement Learning Exploiting State-Action Equivalence, in: ACML 2019, Proceedings of Machine Learning Research, Nagoya, Japan, 2019, vol. 101, pp. 204 - 219.

    https://hal.archives-ouvertes.fr/hal-02378887
  • 6M. Bressan, N. Cesa-Bianchi, A. Paudice, F. Vitale.

    Correlation Clustering with Adaptive Similarity Queries, in: Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019, https://arxiv.org/abs/1905.11902.

    https://hal.inria.fr/hal-02376961
  • 7M. Dehouck, P. Denis.

    Phylogenetic Multi-Lingual Dependency Parsing, in: NAACL 2019 - Annual Conference of the North American Chapter of the Association for Computational Linguistics, Minneapolis, United States, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June 2019.

    https://hal.archives-ouvertes.fr/hal-02143747
  • 8B. Li, M. Dehouck, P. Denis.

    Modal sense classification with task-specific context embeddings, in: ESANN 2019 - 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, April 2019.

    https://hal.archives-ouvertes.fr/hal-02143762
  • 9O. Pandit, P. Denis, L. Ralaivola.

    Learning Rich Event Representations and Interactions for Temporal Relation Classification, in: ESANN 2019 - 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, April 2019.

    https://hal.archives-ouvertes.fr/hal-02265061
  • 10S. Pasteris, F. Vitale, K. Chan, S. Wang, M. Herbster.

    MaxHedge: Maximising a Maximum Online, in: International Conference on Artificial Intelligence and Statistics, Naha, Okinawa, Japan, April 2019, https://arxiv.org/abs/1810.11843. [ DOI : 10.11843 ]

    https://hal.inria.fr/hal-02376987
  • 11B. M. L. Srivastava, A. Bellet, M. Tommasi, E. Vincent.

    Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?, in: INTERSPEECH 2019 - 20th Annual Conference of the International Speech Communication Association, Graz, Austria, September 2019.

    https://hal.inria.fr/hal-02166434
  • 12F. Vitale, A. Rajagopalan, C. Gentile.

    Flattening a Hierarchical Clustering through Active Learning, in: Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019.

    https://hal.inria.fr/hal-02376981
  • 13R. Vogel, A. Bellet, S. Clémençon, O. Jelassi, G. Papa.

    Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning, in: ECML PKDD 2019 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Würzburg, Germany, September 2019.

    https://hal.inria.fr/hal-02166428

Internal Reports

  • 14A. Bellet, R. Guerraoui, H. Hendrikx.

    Who started this rumor? Quantifying the natural differential privacy guarantees of gossip protocols, Inria, 2019.

    https://hal.inria.fr/hal-02166432
  • 15V. Zantedeschi, A. Bellet, M. Tommasi.

    Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs, Inria, 2019.

    https://hal.inria.fr/hal-02166433

Other Publications

  • 16J. Bell, A. Bellet, A. Gascón, T. Kulkarni.

    Private Protocols for U-Statistics in the Local Model and Beyond, October 2019, https://arxiv.org/abs/1910.03861 - working paper or preprint. [ DOI : 10.03861 ]

    https://hal.inria.fr/hal-02310236
  • 17F. Capelli, N. Crosetti, J. Niehren, J. Ramon.

    Dependency Weighted Aggregation on Factorized Databases, January 2019, https://arxiv.org/abs/1901.03633 - working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-01981553
  • 18P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, R. G. L. D'Oliveira, S. E. Rouayheb, D. Evans, J. Gardner, Z. Garrett, A. Gascón, B. Ghazi, P. B. Gibbons, M. Gruteser, Z. Harchaoui, C. He, L. He, Z. Huo, B. Hutchinson, J. Hsu, M. Jaggi, T. Javidi, G. Joshi, M. Khodak, J. Konečný, A. Korolova, F. Koushanfar, S. Koyejo, T. Lepoint, Y. Liu, P. Mittal, M. Mohri, R. Nock, A. Ozgür, R. Pagh, M. Raykova, H. Qi, D. Ramage, R. Raskar, D. Song, W. Song, S. U. Stich, Z. Sun, A. T. Suresh, F. Tramèr, P. Vepakomma, J. Wang, L. Xiong, Z. Xu, Q. Yang, F. X. Yu, H. Yu, S. Zhao.

    Advances and Open Problems in Federated Learning, December 2019, https://arxiv.org/abs/1912.04977 - working paper or preprint.

    https://hal.inria.fr/hal-02406503
  • 19B. M. L. Srivastava, N. Vauquier, M. Sahidullah, A. Bellet, M. Tommasi, E. Vincent.

    Evaluating Voice Conversion-based Privacy Protection against Informed Attackers, November 2019, working paper or preprint.

    https://hal.inria.fr/hal-02355115
  • 20W. de Vazelhes, C. Carey, Y. Tang, N. Vauquier, A. Bellet.

    metric-learn: Metric Learning Algorithms in Python, November 2019, https://arxiv.org/abs/1908.04710 - GitHub repository: https://github.com/scikit-learn-contrib/metric-learn.

    https://hal.inria.fr/hal-02376986
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.

    http://books.google.fr/books?id=jvq2AAAAIAAJ
  • 28C. 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
  • 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.

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

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

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

    http://dl.acm.org/citation.cfm?id=1654758.1654769
  • 35A. 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
  • 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.

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

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

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


previous Previous | up Home