Nicolas TREMBLAY

Nicolas TREMBLAY

Nicolas TREMBLAY

Personnel Autre Permanent
Graph signal processing, Statistical machine learning, Point processes

Site Ampère

Bureau : B378

Téléphone : 04 76 82 64 55

Biographie

(à compléter)

Publications / Travaux


66 documents

Articles dans une revue23 documents

  • Hugo Jaquard, Pierre-Olivier Amblard, Simon Barthelme, Nicolas Tremblay. Random Multi-Type Spanning Forests for Synchronization on Sparse Graphs. SIAM Journal on Mathematics of Data Science, 2025, 7 (3), pp.1123-1153. ⟨10.1137/24M1649563⟩. ⟨hal-04524778v2⟩
  • Lucas Chatelain, Nicolas Tremblay, Elsa Vennat, Elisabeth Dursun, David Rousseau, et al.. Cellular porosity in dentin exhibits complex network characteristics with spatio-temporal fluctuations. PLoS ONE, 2025, 20 (7), pp.e0327030. ⟨10.1371/journal.pone.0327030⟩. ⟨hal-05300938⟩
  • Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter. Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs. Journal of Machine Learning Research, 2024, 25 (406), pp.1-49. ⟨hal-04059402v3⟩
  • Simon Barthelme, Pierre-Olivier Amblard, Nicolas Tremblay, Konstantin Usevich. Gaussian process regression in the flat limit. Annals of Statistics, 2023, 51 (6), pp.2471-2505. ⟨10.1214/23-AOS2336⟩. ⟨hal-03869191⟩
  • Simon Barthelme, Nicolas Tremblay, Konstantin Usevich, Pierre-Olivier Amblard. Determinantal point processes in the flat limit. Bernoulli, 2023, 29 (2), pp.957-983. ⟨10.3150/22-BEJ1486⟩. ⟨hal-03359889⟩
  • Nicolas Tremblay, Simon Barthelme, Konstantin Usevich, Pierre-Olivier Amblard. Extended L-ensembles: A new representation for determinantal point processes. The Annals of Applied Probability, 2023, 33 (1), pp.613-640. ⟨10.1214/22-AAP1824⟩. ⟨hal-03359895⟩
  • Nagham Badreddine, Gisela Zalcman, Florence Appaix, Guillaume Jean-Paul Claude Becq, Nicolas Tremblay, et al.. Spatiotemporal reorganization of corticostriatal networks encodes motor skill learning. Cell Reports, 2022, 39 (1), pp.110623. ⟨10.1016/j.celrep.2022.110623⟩. ⟨hal-03852764⟩
  • Lorenzo Dall’Amico, Romain Couillet, Nicolas Tremblay. A Unified Framework for Spectral Clustering in Sparse Graphs. Journal of Machine Learning Research, 2021, 22 (217), pp.1-56. ⟨hal-03372407⟩
  • Lorenzo Dall’Amico, Romain Couillet, Nicolas Tremblay. Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs. Journal of Statistical Mechanics: Theory and Experiment, 2021, 2021 (9), pp.093405. ⟨10.1088/1742-5468/ac21d3⟩. ⟨hal-03354394⟩
  • Yusuf Yigit Pilavci, Pierre-Olivier Amblard, Simon Barthelme, Nicolas Tremblay. Graph Tikhonov Regularization and Interpolation via Random Spanning Forests. IEEE Transactions on Signal and Information Processing over Networks, 2021, 7, pp.359-374. ⟨10.1109/TSIPN.2021.3084879⟩. ⟨hal-03012192v2⟩
  • Nicolas Tremblay, Simon Barthelme, Pierre-Olivier Amblard. Determinantal Point Processes for Coresets. Journal of Machine Learning Research, 2019, 20 (168), pp.1-70. ⟨hal-01741533v2⟩
  • Simon Barthelme, Pierre-Olivier Amblard, Nicolas Tremblay. Asymptotic Equivalence of Fixed-size and Varying-size Determinantal Point Processes. Bernoulli, 2019, 25 (4B), pp.3555-3589. ⟨10.3150/18-BEJ1102⟩. ⟨hal-02086028⟩
  • Benjamin Ricaud, Pierre Borgnat, Nicolas Tremblay, Paulo Gonçalves, Pierre Vandergheynst. Fourier could be a Data Scientist: from Graph Fourier Transform to Signal Processing on Graphs. Comptes Rendus. Physique, 2019, 20 (5), pp.474-488. ⟨10.1016/j.crhy.2019.08.003⟩. ⟨hal-02304584⟩
  • Luc Le Magoarou, Rémi Gribonval, Nicolas Tremblay. Approximate fast graph Fourier transforms via multi-layer sparse approximations. IEEE Transactions on Signal and Information Processing over Networks, 2018, 4 (2), pp.407–420. ⟨10.1109/TSIPN.2017.2710619⟩. ⟨hal-01416110v3⟩
  • Gilles Puy, Nicolas Tremblay, Rémi Gribonval, Pierre Vandergheynst. Random sampling of bandlimited signals on graphs. Applied and Computational Harmonic Analysis, 2018, 44 (2), pp.446-475. ⟨10.1016/j.acha.2016.05.005⟩. ⟨hal-01229578v3⟩
  • Rasha E. Boulos, Nicolas Tremblay, Alain Arneodo, Pierre Borgnat, Benjamin Audit. Multi-scale structural community organisation of the human genome. BMC Bioinformatics, 2017, 18 (1), pp.209. ⟨10.1186/s12859-017-1616-x⟩. ⟨hal-01507455⟩
  • Nicolas Tremblay, Pierre Borgnat. Subgraph-based filterbanks for graph signals. IEEE Transactions on Signal Processing, 2016, 64, pp.3827-3840. ⟨10.1109/tsp.2016.2544747⟩. ⟨hal-01243889⟩
  • Patrice Abry, Stéphane Roux, Herwig Wendt, Paul Messier, Andrew Klein, et al.. Multiscale Anisotropic Texture Analysis and Classification of Photographic Prints: Art scholarship meets image processing algorithms. IEEE Signal Processing Magazine, 2015, 32 (4), pp.18-27. ⟨10.1109/MSP.2015.2402056⟩. ⟨hal-01514632⟩
  • Nicolas Tremblay, Alain Barrat, Cary Forest, Mark Nornberg, Jean-François Pinton, et al.. Bootstrapping under constraint for the assessment of group behavior in human contact networks. Physical Review E : Statistical, Nonlinear, and Soft Matter Physics [2001-2015], 2013, 88, pp.052812. ⟨10.1103/PhysRevE.88.052812⟩. ⟨hal-00909617⟩
  • K. Tse Ve Koon, Nicolas Tremblay, D. Constantin, E. Freyssingeas. Structure, thermodynamics and dynamics of the isotropic phase of spherical non-ionic surfactant micelles. Journal of Colloid and Interface Science, 2013, 393 (1), pp.161-173. ⟨10.1016/j.jcis.2012.10.039⟩. ⟨hal-00850596⟩
  • Nicolas Tremblay, Éric Larose, Vincent Rossetto. Probing slow dynamics of consolidated granular multicomposite materials by diffuse acoustic wave spectroscopy. Journal of the Acoustical Society of America, 2010, 127 (3), pp.1239. ⟨hal-00459719⟩
  • Nicolas Tremblay, Éric Larose, Vincent Rossetto. Probing slow dynamics of consolidated granular multicomposite materials by diffuse acoustic wave spectroscopy. Journal of the Acoustical Society of America, 2010, 127 (3), pp.1239-1243. ⟨10.1121/1.3294553⟩. ⟨hal-02376188⟩
  • Zoran G. Cerovic, Guy Samson, Fermín Morales, Nicolas Tremblay, Ismaël Moya. Ultraviolet-induced fluorescence for plant monitoring: present state and prospects. Agronomie, 1999, 19 (7), pp.543-578. ⟨hal-00885952⟩

Communications dans un congrès32 documents

  • Simon Barthelme, Fabienne Castell, Alexandre Gaudillière, Clothilde Melot, Matteo Quattropani, et al.. Estimating a graph’s spectrum via random Kirchhoff forests. GRETSI 2025 – XXXème Colloque Francophone de Traitement du Signal et des Images, Aug 2025, Strasbourg, France. ⟨hal-05010542⟩
  • Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter. Seeking universal approximation for continuous limits of graph neural networks on large random graphs. Asilomar 2024 – Asilomar Conference on Signals, Systems, and Computers, Oct 2024, Pacific Grove, United States. pp.1-5. ⟨hal-04831761⟩
  • Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter. Seeking universal approximation for continuous counterparts of GNNs on large random graphs. GSP 2024 – 7th Graph Signal Processing Workshop, Jun 2024, Delft, Netherlands. ⟨hal-04728922⟩
  • Hugo Jaquard, Pierre-Olivier Amblard, Simon Barthelme, Nicolas Tremblay. Estimators for Connection-Laplacian-Based Linear Algebra. GSP 2024 – 7th Graph Signal Processing Workshop, Jun 2024, Delft, Netherlands. ⟨hal-04699884⟩
  • Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter. Convergence of Graph Neural Networks with generic aggregation functions on random graphs. GRETSI 2023 – XXIXème Colloque Francophone de Traitement du Signal et des Images, GRETSI – Groupe de Recherche en Traitement du Signal et des Images, Aug 2023, Grenoble, France. pp.1-4. ⟨hal-04373554⟩
  • Nicolas Tremblay, Yusuf Yigit Pilavci, Simon Barthelme, Pierre-Olivier Amblard. What can we Compute With Kirchhoff Forests?. GSP 2023 – 6th Graph Signal Processing workshop, Jun 2023, Oxford, United Kingdom. ⟨hal-04104124⟩
  • Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter. Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Random Graphs. GSP 2023 – 6th Graph Signal Processing workshop, Jun 2023, Oxford, United Kingdom. pp.1-3. ⟨hal-04106511⟩
  • Hugo Jaquard, Pierre-Olivier Amblard, Simon Barthelme, Nicolas Tremblay. Angular Synchronization on Graphs with Monte-Carlo. GSP 2023 – 6th Graph Signal Processing workshop, Jun 2023, Oxford, United Kingdom. ⟨hal-04104630v2⟩
  • Hugo Jaquard, Michaël Fanuel, Pierre-Olivier Amblard, Rémi Bardenet, Simon Barthelme, et al.. Smoothing complex-valued signals on Graphs with Monte-Carlo. ICASSP 2023 – IEEE International Conference on Acoustics, Speech and Signal Processing, Jun 2023, Rhodes Island, Greece. ⟨10.1109/ICASSP49357.2023.10096354⟩. ⟨hal-03869873⟩
  • Simon Barthelme, Nicolas Tremblay, Pierre-Olivier Amblard. A Faster Sampler for Discrete Determinantal Point Processes. AISTATS 2023 – 26th International Conference on Artificial Intelligence and Statistics, Apr 2023, Valencia, Spain. pp.5582-5592. ⟨hal-03835312v2⟩
  • Pierre-Olivier Amblard, Simon Barthelme, Nicolas Tremblay, Konstantin Usevich. Mesures d’indépendance dans des rkHs en limite plate. GRETSI 2022 – XXVIIIème Colloque Francophone de Traitement du Signal et des Images, Sep 2022, Nancy, France. ⟨hal-03702856⟩
  • Yusuf Yigit Pilavci, Pierre-Olivier Amblard, Simon Barthelme, Nicolas Tremblay. Variance Reduction for Inverse Trace Estimation via Random Spanning Forests. GRETSI 2022 – XXVIIIème Colloque Francophone de Traitement du Signal et des Images, Sep 2022, Nancy, France. ⟨hal-03691004v4⟩
  • Yusuf Yigit Pilavci, Pierre-Olivier Amblard, Simon Barthelme, Nicolas Tremblay. Variance reduction in stochastic methods for large-scale regularised least-squares problems. EUSIPCO 2022 – 30th European Signal Processing Conference, Aug 2022, Belgrade, Serbia. ⟨10.23919/EUSIPCO55093.2022.9909520⟩. ⟨hal-03378171⟩
  • Hashem Ghanem, Nicolas Keriven, Nicolas Tremblay. Fast Graph Kernel with Optical Random Features. ICASSP 2021 – IEEE International Conference on Acoustics, Speech, and Signal Processing, Jun 2021, Toronto, Canada. ⟨10.1109/ICASSP39728.2021.9413614⟩. ⟨hal-02976716⟩
  • Lorenzo Dall’Amico, Romain Couillet, Nicolas Tremblay. Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian. NeurIPS 2020 – 34th Conference on Neural Information Processing Systems, Dec 2020, Vancouver (virtual), Canada. ⟨hal-03172642⟩
  • Yusuf Yigit Pilavci, Pierre-Olivier Amblard, Simon Barthelme, Nicolas Tremblay. Smoothing graph signals via random spanning forests. ICASSP 2020 – IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, May 2020, Barcelone (virtual), Spain. ⟨10.1109/ICASSP40776.2020.9054497⟩. ⟨hal-02319175v2⟩
  • Lorenzo Dall’Amico, Romain Couillet, Nicolas Tremblay. Optimal Laplacian Regularization for Sparse Spectral Community Detection. ICASSP 2020 – IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, May 2020, Barcelone (virtual), Spain. ⟨10.1109/ICASSP40776.2020.9053543⟩. ⟨hal-02956603⟩
  • Lorenzo Dall’Amico, Romain Couillet, Nicolas Tremblay. Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs. NeurIPS 2019 – 33rd Conference on Neural Information Processing Systems, Dec 2019, Vancouver, Canada. ⟨hal-02429525⟩
  • Lorenzo Dall’Amico, Romain Couillet, Nicolas Tremblay. Classification spectrale par la laplacienne déformée dans des graphes réalistes. GRETSI 2019 – XXVIIème Colloque Francophone de Traitement du Signal et des Images, Aug 2019, Lille, France. ⟨hal-02153901⟩
  • Guillaume Jean-Paul Claude Becq, Nagham Badreddine, Nicolas Tremblay, Florence Appaix, Gisela Zalcman, et al.. Classification de types de neurones à partir de signaux calciques. GRETSI 2019 – XXVIIème Colloque Francophone de Traitement du Signal et des Images, Aug 2019, Lille, France. pp.1-12. ⟨hal-02528364v2⟩
  • Simon Barthelme, Nicolas Tremblay, Alexandre Gaudilliere, Luca Avena, Pierre-Olivier Amblard. Estimating the inverse trace using random forests on graphs. GRETSI 2019 – XXVIIème Colloque Francophone de Traitement du Signal et des Images, Aug 2019, Lille, France. ⟨hal-02319194⟩
  • Pierre-Olivier Amblard, Simon Barthelme, Nicolas Tremblay. Subsampling with k determinantal point processes for estimating statistics in large data sets. SSP 2018 – 2018 IEEE Workshop on Statistical Signal Processing, Jun 2018, Fribourg-en-Brisgau, Germany. ⟨10.1109/SSP.2018.8450831⟩. ⟨hal-01778956⟩
  • Luc Le Magoarou, Nicolas Tremblay, Rémi Gribonval. Analyzing the Approximation Error of the Fast Graph Fourier Transform. ACSSC 2017 – 51st Annual Asilomar Conference on Signals Systems and Computers, Oct 2017, Monterey, California, United States. ⟨hal-01627434⟩
  • Nicolas Tremblay, Simon Barthelme, Pierre-Olivier Amblard. Échantillonnage de signaux sur graphes via des processus déterminantaux. GRETSI 2017 – XXVIème Colloque francophone de traitement du signal et des images, Sep 2017, Juan-Les-Pins, France. ⟨hal-01503736v2⟩
  • Nicolas Tremblay, Pierre-Olivier Amblard, Simon Barthelme. Graph sampling with determinantal processes. EUSIPCO 2017 – 25th European Signal Processing Conference, Aug 2017, Kos Island, Greece. ⟨10.23919/EUSIPCO.2017.8081494⟩. ⟨hal-01483347⟩
  • Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin, Rémi Gribonval. Compressive K-means. ICASSP 2017 – IEEE International Conference on Acoustics, Speech and Signal Processing, Mar 2017, New Orleans, United States. ⟨hal-01386077v4⟩
  • Nicolas Tremblay, Gilles Puy, Rémi Gribonval, Pierre Vandergheynst. Compressive Spectral Clustering. 33rd International Conference on Machine Learning, Jun 2016, New York, United States. ⟨hal-01320214⟩
  • Nicolas Tremblay, Gilles Puy, Pierre Borgnat, Rémi Gribonval, Pierre Vandergheynst. Accelerated spectral clustering using graph filtering of random signals. 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), Mar 2016, Shanghai, China. ⟨hal-01243682v2⟩
  • Stéphane G. Roux, Nicolas Tremblay, Pierre Borgnat, Patrice Abry, Herwig Wendt, et al.. Multiscale Anisotropic Texture Unsupervised Clustering for Photographic Paper. 7th IEEE International Workshop on Information Forensics and Security (WIFS 2015), Nov 2015, Rome, Italy. pp. 1-6. ⟨hal-01511889⟩
  • Pierre Borgnat, Paulo Gonçalves, Nicolas Tremblay, Nathanaël Willaime-Angonin. Community mining with graph filters for correlation matrices. Asilomar Conference on Signals, Systems, and Computers, Nov 2015, Monterey (CA), United States. ⟨hal-01245926v2⟩
  • Nicolas Tremblay, Stéphane G. Roux, Pierre Borgnat, Patrice Abry, Herwig Wendt, et al.. Texture classification of photographic papers: improving spectral clustering using filterbanks on graphs. 25eme Colloque Groupe de Recherche et d’Etudes du Traitement du Signal et des Images (GRETSI 2015), Sep 2015, Lyon, France. pp. 1-4. ⟨hal-01518069⟩
  • Éric Larose, Nicolas Tremblay, Cédric Payan, Vincent Garnier, Vincent Rossetto. Ultrasonic slow dynamics to probe concrete aging and damage. 39th QNDE, Jul 2012, Denver, United States. pp.1317-1324, ⟨10.1063/1.4789195⟩. ⟨hal-01295368⟩

Poster de conférence1 document

  • Lucas Chatelain, Elsa Vennat, Nicolas Tremblay, David Rousseau, Aurélien Gourrier. Graph modeling of cellular porosity in dentin. Compex Networks 2023 – 12th International Conference on Complex Networks and their Applications, Nov 2023, Menton, France. . ⟨hal-04304657⟩

Chapitres d’ouvrage3 documents

  • Nicolas Tremblay, Andreas Loukas. Approximating Spectral Clustering via Sampling: a Review. Sampling Techniques for Supervised or Unsupervised Tasks, 2020, ISBN 978-3-030-29348-2. ⟨10.1007/978-3-030-29349-9_5⟩. ⟨hal-02468312⟩
  • Nicolas Tremblay, Paulo Gonçalves, Pierre Borgnat. Design of graph filters and filterbanks. Petar M. Djurić; Cédric Richard. Cooperative and Graph Signal Processing, Academic Press, pp.299-324, 2018, 978-0-12-813677-5. ⟨10.1016/B978-0-12-813677-5.00011-0⟩. ⟨hal-01675375⟩
  • Pierre Borgnat, Céline Robardet, Patrice Abry, Patrick Flandrin, Jean-Baptiste Rouquier, et al.. A Dynamical Network View of Lyon’s Vélo’v Shared Bicycle System. A. Mukherjee, M. Choudhury, F. Peruani, N. Ganguly, B. Mitra Dynamics On and Of Complex Networks, Volume 2: Applications to Time-Varying Dynamical Systems , springer, pp.267-284, 2013. ⟨hal-01339131⟩

Pré-publications, Documents de travail3 documents

  • Lauren Anderson, Lucas Chatelain, Nicolas Tremblay, Kathryn Grandfield, David Rousseau, et al.. Biology-driven assessment of deep learning super-resolution imaging of the porosity network in dentin. 2026. ⟨hal-05545834⟩
  • Simon Barthelme, Fabienne Castell, Alexandre Gaudillière, Clothilde Mélot, Matteo Quattropani, et al.. Spectrum Estimation through Kirchhoff Random Forests. 2025. ⟨hal-05412261⟩
  • Simon Barthelme, Nicolas Tremblay, Konstantin Usevich, Pierre-Olivier Amblard. Determinantal Point Processes in the Flat Limit: Extended L-ensembles, Partial-Projection DPPs and Universality Classes. 2020. ⟨hal-03012027⟩

Thèses1 document

  • Nicolas Tremblay. Réseaux et signal : des outils de traitement du signal pour l’analyse des réseaux. Autre [cond-mat.other]. Ecole normale supérieure de lyon – ENS LYON, 2014. Français. ⟨NNT : 2014ENSL0938⟩. ⟨tel-01078956⟩

Habilitations à diriger des recherches1 document

  • Nicolas Tremblay. Graph signals, structures and sketches. Discrete Mathematics [cs.DM]. Université Grenoble Alpes, 2024. ⟨tel-04612367⟩

Logiciel2 documents


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