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Romain TAVENARD

Romain TAVENARD

Projets de recherches

MATS - MAchine learning for environmental Time Series (2019 - 2023)

Publications HAL


54 documents

Journal articles14 document

  • Wassim Swaileh, Florent Imbert, Yann Soullard, Romain Tavenard, Eric Anquetil. Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Network. International Journal on Document Analysis and Recognition, 2023, ⟨10.1007/s10032-023-00430-1⟩. ⟨hal-04076399v2⟩
  • Marc Rußwurm, Nicolas Courty, Rémi Emonet, Sébastien Lefèvre, Devis Tuia, et al.. End-to-end learned early classification of time series for in-season crop type mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 196, pp.445-456. ⟨10.1016/j.isprsjprs.2022.12.016⟩. ⟨hal-04023073⟩
  • Titouan Vayer, Romain Tavenard, Laetitia Chapel, Nicolas Courty, Rémi Flamary, et al.. Time Series Alignment with Global Invariances. Transactions on Machine Learning Research Journal, 2022. ⟨hal-02473959⟩
  • Pierre Gloaguen, Laetitia Chapel, Chloé Friguet, Romain Tavenard. Scalable clustering of segmented trajectories within a continuous time framework. Application to maritime traffic data. Machine Learning, 2021, Special Issue on Machine Learning for Earth Observation Data, 112 (6), pp.1975-2001. ⟨10.1007/s10994-021-06004-8⟩. ⟨hal-02617575v3⟩
  • Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Zahdi Alaya, Aurélie Boisbunon, et al.. POT : Python Optimal Transport. Journal of Machine Learning Research, 2021. ⟨hal-03264013⟩
  • Titouan Vayer, Laetitia Chapel, Rémi Flamary, Romain Tavenard, Nicolas Courty. Fused Gromov-Wasserstein Distance for Structured Objects. Algorithms, 2020, 13 (9), pp.212. ⟨10.3390/a13090212⟩. ⟨hal-02971153⟩
  • Romain Tavenard, Johann Faouzi, Gilles Vandewiele, Felix Divo, Guillaume Androz, et al.. Tslearn, A Machine Learning Toolkit for Time Series Data. Journal of Machine Learning Research, 2020, 21, pp.1 - 6. ⟨hal-02883390⟩
  • Zheng Zhang, Romain Tavenard, Adeline Bailly, Xiaotong Tang, Ping Tang, et al.. Dynamic Time Warping Under Limited Warping Path Length. Information Sciences, 2017, 393, pp.91 - 107. ⟨10.1016/j.ins.2017.02.018⟩. ⟨hal-01470554⟩
  • Adeline Bailly, Laetitia Chapel, Romain Tavenard, Gustau Camps-Valls. Nonlinear Time-Series Adaptation for Land Cover Classification. IEEE Geoscience and Remote Sensing Letters, 2017, ⟨10.1109/LGRS.2017.2686639⟩. ⟨halshs-01515283⟩
  • Romain Tavenard, Laurent Amsaleg. Improving the Efficiency of Traditional DTW Accelerators. Knowledge and Information Systems (KAIS), 2015, 42 (1), pp.215-243. ⟨10.1007/s10115-013-0698-7⟩. ⟨hal-00862176⟩
  • Rémi Dupas, Romain Tavenard, Ophélie Fovet, Nicolas Gilliet, Catherine Grimaldi, et al.. Identifying seasonal patterns of phosphorus storm dynamics with dynamic time warping. Water Resources Research, 2015, 51 (11), pp.8868-8882. ⟨10.1002/2015WR017338⟩. ⟨halshs-01228397⟩
  • Alice Aubert, Romain Tavenard, Rémi Emonet, Alban de Lavenne, Simon Malinowski, et al.. Clustering Flood Events from Water Quality Time-Series using Latent Dirichlet Allocation Model. Water Resources Research, 2013, 49 (12), pp.8187-8199. ⟨10.1002/2013WR014086⟩. ⟨halshs-00906292⟩
  • Albert Ali Salah, Eric Pauwels, Romain Tavenard, Theo Gevers. T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data. Sensors, 2010, 10 (8), pp.7496-7513. ⟨10.3390/s100807496⟩. ⟨halshs-01138500⟩
  • Romain Tavenard, Laurent Amsaleg, Guillaume Gravier. Model-based similarity estimation of multidimensional temporal sequences. Annals of Telecommunications - annales des télécommunications, 2009, 64 (5), pp.381-390. ⟨10.1007/s12243-009-0091-4⟩. ⟨inria-00567877⟩

Conference papers29 document

  • Florent Imbert, Romain Tavenard, Yann Soullard, Eric Anquetil. Domain adaptation for handwriting trajectory reconstruction from IMU sensors. ICDAR 2024 Workshops, ADAPDA, Aug 2024, Athènes, Greece. ⟨hal-04605593⟩
  • François Painblanc, Laëtitia Chapel, Nicolas Courty, Chloé Friguet, Charlotte Pelletier, et al.. Domain Adaptation of Time Series through Optimal Transport and Temporal Alignment. 55ièmes Journées de Statistique, SFdS, May 2024, Bordeaux, France. ⟨hal-04644862⟩
  • Alexey Serdyuk, Fabian Kreß, Micha Hiegle, Tanja Harbaum, Jürgen Becker, et al.. Towards the on-device Handwriting Trajectory Reconstruction of the Sensor Enhanced Pen. IEEE 9th World Forum on Internet of Things, Oct 2023, Aveiro, Portugal. ⟨hal-04358219⟩
  • François Painblanc, Laetitia Chapel, Nicolas Courty, Chloé Friguet, Charlotte Pelletier, et al.. Match-And-Deform: Time Series Domain Adaptation through Optimal Transport and Temporal Alignment. ECML PKDD 2023, Sep 2023, Torino, Italy. ⟨hal-04189149⟩
  • Noé Zufferey, Mathias Humbert, Romain Tavenard, Kévin Huguenin. Watch your Watch: Inferring Personality Traits from Wearable Activity Trackers. USENIX Security Symposium (USENIX Security), Aug 2023, Anaheim, CA, United States. pp.18. ⟨hal-04003119⟩
  • François Painblanc, Laetitia Chapel, Nicolas Courty, Chloé Friguet, Charlotte Pelletier, et al.. MAD: Match-And-Deform for Time Series Domain Adaptation. Conférence sur l'Apprentissage automatique (CAp), Jul 2022, Vannes, France. ⟨hal-03932463⟩
  • Yichang Wang, Rémi Emonet, Elisa Fromont, Simon Malinowski, Romain Tavenard. Adversarial regularization for explainable-by-design time series classification. ICTAI 2020 - 32th International Conference on Tools with Artificial Intelligence, Nov 2020, online, Greece. pp.1-9. ⟨hal-03025671⟩
  • Titouan Vayer, Rémi Flamary, Romain Tavenard, Laetitia Chapel, Nicolas Courty. Sliced Gromov-Wasserstein. NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, Dec 2019, Vancouver, Canada. ⟨hal-02174309⟩
  • David Guijo-Rubio, Pedro A Gutiérrez, Romain Tavenard, Anthony Bagnall. A Hybrid Approach to Time Series Classification with Shapelets. Intelligent Data Engineering and Automated Learning -- IDEAL, Nov 2019, Manchester, United Kingdom. pp.137-144, ⟨10.1007/978-3-030-33607-3_16⟩. ⟨hal-02371422⟩
  • Yichang Wang, Rémi Emonet, Elisa Fromont, Simon Malinowski, Etienne Menager, et al.. Classification de séries temporelles basée sur des "shapelets" interprétables par réseaux de neurones antagonistes. CAp 2019 - Conférence sur l'Apprentissage automatique, Jul 2019, Toulouse, France. pp.1-2. ⟨hal-02268004⟩
  • Titouan Vayer, Laetitia Chapel, Rémi Flamary, Romain Tavenard, Nicolas Courty. Optimal Transport for structured data with application on graphs. ICML 2019 - 36th International Conference on Machine Learning, Jun 2019, Long Beach, United States. pp.1-16. ⟨hal-02174322⟩
  • Marc Russwurm, Romain Tavenard, Sébastien Lefèvre, Marco Körner. Early Classification for Agricultural Monitoring from Satellite Time Series. AI for Social Good Workshop at International Conference on Machine Learning (ICML), 2019, Long Beach, United States. ⟨hal-02343851⟩
  • Maël Guilleme, Simon Malinowski, Romain Tavenard, Xavier Renard. Localized Random Shapelets. International Workshop on Advanced Analysis and Learning on Temporal Data, 2019, Wurzburg, Germany. pp.85-97, ⟨10.1007/978-3-030-39098-3_7⟩. ⟨hal-02513295⟩
  • Ricardo Carlini Sperandio, Simon Malinowski, Laurent Amsaleg, Romain Tavenard. Time Series Retrieval using DTW-Preserving Shapelets. SISAP 2018 – 11th International Conference on Similarity Search and Applications, Oct 2018, Lima, Peru. pp.257-270, ⟨10.1007/978-3-030-02224-2_20⟩. ⟨hal-01841995⟩
  • Arnaud Lods, Simon Malinowski, Romain Tavenard, Laurent Amsaleg. Learning DTW-Preserving Shapelets. IDA 2017 - 16th International Symposium on Intelligent Data Analysis, Oct 2017, London, United Kingdom. pp.198-209, ⟨10.1007/978-3-319-68765-0_17⟩. ⟨hal-01565207v2⟩
  • Romain Tavenard, Simon Malinowski, Laetitia Chapel, Adeline Bailly, Heider Sanchez, et al.. Efficient Temporal Kernels between Feature Sets for Time Series Classification. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, Sep 2017, Skopje, Macedonia. ⟨halshs-01561461⟩
  • Bharath Bhushan Damodaran, Nicolas Courty, Romain Tavenard. Randomized Nonlinear Component Analysis for Dimensionality Reduction of Hyperspectral Images. IGARSS 2017 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2017, Houston, United States. pp.1-4. ⟨hal-01620604⟩
  • Arthur Le Guennec, Simon Malinowski, Romain Tavenard. Data Augmentation for Time Series Classification using Convolutional Neural Networks. ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, Sep 2016, Riva Del Garda, Italy. ⟨halshs-01357973⟩
  • Romain Tavenard, Simon Malinowski. Cost-Aware Early Classification of Time Series. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, Sep 2016, Riva del Garda, Italy. pp.632-647, ⟨10.1007/978-3-319-46128-1_40⟩. ⟨halshs-01339007⟩
  • Adeline Bailly, Damien Arvor, Laetitia Chapel, Romain Tavenard. Classification of MODIS Time Series with Dense Bag-of-Temporal-SIFT-Words: Application to Cropland Mapping in the Brazilian Amazon. IEEE International Geoscience and Remote Sensing Symposium, Jul 2016, Beijing, China. ⟨10.1109/IGARSS.2016.7729594⟩. ⟨halshs-01343211⟩
  • Adeline Bailly, Simon Malinowski, Romain Tavenard, Thomas Guyet, Laetitia Chapel. Bag-of-Temporal-SIFT-Words for Time Series Classification. ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, Sep 2015, Porto, Portugal. ⟨halshs-01184900⟩
  • Simon Malinowski, Thomas Guyet, René Quiniou, Romain Tavenard. 1d-SAX : une nouvelle représentation symbolique pour les séries temporelles. Conférence Extraction et Gestion de Connaissances, Jan 2014, Rennes, France. ⟨hal-00916970⟩
  • Romain Tavenard, Rémi Emonet, Jean-Marc Odobez. Time-Sensitive Topic Models for Action Recognition in Videos. ICIP - International Conference on Image Processing, Sep 2013, Melbourne, Australia. ⟨hal-00872048⟩
  • Simon Malinowski, Thomas Guyet, René Quiniou, Romain Tavenard. 1d-SAX: A Novel Symbolic Representation for Time Series. International Symposium on Intelligent Data Analysis, 2013, United Kingdom. pp.273-284, ⟨10.1007/978-3-642-41398-8_24⟩. ⟨halshs-00912512⟩
  • Romain Tavenard, Hervé Jégou, Laurent Amsaleg. Balancing clusters to reduce response time variability in large scale image search. International Workshop on Content-Based Multimedia Indexing (CBMI 2011), Jun 2011, Madrid, Spain. ⟨inria-00576886v2⟩
  • Hervé Jégou, Romain Tavenard, Matthijs Douze, Laurent Amsaleg. Searching in one billion vectors: re-rank with source coding. ICASSP 2011 - International Conference on Acoustics, Speech and Signal Processing, May 2011, Prague, Czech Republic. pp.861-864, ⟨10.1109/ICASSP.2011.5946540⟩. ⟨inria-00566883⟩
  • Vincent Claveau, Romain Tavenard, Laurent Amsaleg. Vectorisation des processus d'appariement document-requête. 7e conférence en recherche d'informations et applications, CORIA'10, Mar 2010, Sousse, Tunisie. ⟨inria-00561797⟩
  • Romain Tavenard, Albert Ali Salah, Eric Pauwels. Searching for Temporal Patterns in AmI Sensor Data. Constructing Ambient Intelligence, Nov 2007, Darmstadt, Germany. pp.53-62, ⟨10.1007/978-3-540-85379-4_7⟩. ⟨halshs-01138512⟩
  • Eric Pauwels, Albert Ali Salah, Romain Tavenard. Sensor Networks for Ambient Intelligence. IEEE Workshop on Multimedia Signal Processing, Oct 2007, Chania, Greece. ⟨10.1109/MMSP.2007.4412806⟩. ⟨halshs-01138508⟩

Poster communications2 document

  • Florent Imbert, Yann Soullard, Romain Tavenard, Eric Anquetil. Domain adaptation for pen trajectory reconstruction from kinematic sensors. SIFED 2023 – Symposium International Francophone sur l’Ecrit et le Document, Jun 2023, Paris, France. ⟨hal-04125711⟩
  • Alban Thomas, Thomas Corpetti, Samuel S. Corgne, Laurent Garnier, Romain Tavenard, et al.. Mapping Learning. JDEVs, les Journées du DEVeloppement logiciel, Jul 2017, Marseille, France. 2017. ⟨hal-01565586⟩

Proceedings1 document

  • Georgiana Ifrim, Romain Tavenard, Anthony Bagnall, Patrick Schaefer, Simon Malinowski, et al.. Advanced Analytics and Learning on Temporal Data. AALTD 2023 - 8th Workshop on Advanced Analytics and Learning on Temporal Data, 14343, Springer Nature Switzerland, 2023, Lecture Notes in Computer Science, ⟨10.1007/978-3-031-49896-1⟩. ⟨hal-04383684⟩

Book sections1 document

  • Adeline Bailly, Simon Malinowski, Romain Tavenard, Laetitia Chapel, Thomas Guyet. Dense Bag-of-Temporal-SIFT-Words for Time Series Classification. Advanced Analysis and Learning on Temporal Data, Springer, 2016, 978-3319444116. ⟨10.1007/978-3-319-44412-3_2⟩. ⟨hal-01252726v4⟩

Preprints, Working Papers4 document

  • Marc Russwurm, Sébastien Lefevre, Nicolas Courty, Rémi Emonet, Marco Körner, et al.. End-to-end Learning for Early Classification of Time Series. 2019. ⟨hal-02174314⟩
  • Titouan Vayer, Laetitia Chapel, Rémi Flamary, Romain Tavenard, Nicolas Courty. Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties. 2019. ⟨hal-02174316⟩
  • Adeline Bailly, Damien Arvor, Laetitia Chapel, Romain Tavenard. Classification of MODIS Time Series with Dense Bag-of-Temporal-SIFT-Words: Application to Cropland Mapping in the Brazilian Amazon. 2016. ⟨hal-01254455⟩
  • Romain Tavenard, Hervé Jégou, Mathieu Lagrange. Efficient Cover Song Identification using approximate nearest neighbors. 2013. ⟨hal-00672897⟩

Reports2 document

  • Romain Tavenard, Laurent Amsaleg. Improving the Efficiency of Traditional DTW Accelerators. [Research Report] 2011, pp.19. ⟨hal-00639215v3⟩
  • Romain Tavenard, Laurent Amsaleg, Hervé Jégou. Balancing clusters to reduce response time variability in large scale image search. [Research Report] RR-7387, INRIA. 2010. ⟨inria-00519490⟩

Theses1 document

  • Romain Tavenard. Indexation de séquences de descripteurs. Multimédia [cs.MM]. Université Rennes 1, 2011. Français. ⟨NNT : ⟩. ⟨tel-00639225⟩

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Unité mixte de recherche : Littoral - Environnement - Télédétection - Géomatique (UMR : LETG)

L’UMR LETG a été créée en 1996 par le CNRS pour regrouper les recherches menées en géographie sur différents sites universitaires. Depuis sa création, le laboratoire a engagé un processus d’intégration scientifique de plus en plus efficace afin d’amener ses membres à mieux s’organiser pour répondre aux objectifs scientifiques déterminés par les priorités nationales et internationales dans son champ d’investigation.