Reviewing activity
I have some reviewing activity for the Signal/Image Processing journals TSP, TSIPN, TIP, OJSP, IMAJNA, SIIMS, TKDE, SP, SPL, JSPS, as well as other Applied Math / Proba / Stats / Machine Learning journals such as ACHA, JMLR, JASA, VMSTA, Applied Probability, Stochastic Processes and their Applications, Bioinformatics, Constructive Approximation or PLOS One. I also have some reviewing activity for the signal processing conferences ICASSP, GLOBALSIP, CAMSAP, EUSIPCO, as well as other Applied Math / Machine Learning conferences such as ISIT, ESA, AISTATS, NeurIPS or ICLR.Preprints
| [ii] |
Hugo Jaquard, Pierre-Olivier Amblard, Simon Barthelmé, Nicolas Tremblay.
Random Multi-Type Spanning Forests for Synchronization on Sparse Graphs.
submitted, 2024. [PDF]
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| [i] |
Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter.
Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs.
submitted, 2023. [PDF]
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List of publications
Papers in refereed journals
| [20] |
Simon Barthelme, Pierre-Olivier Amblard, Nicolas Tremblay, Konstantin Usevich.
Gaussian Process Regression in the Flat Limit.
In
Annals of Statistics, 2023. [PDF]
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| [19] |
Nicolas Tremblay, Simon Barthelmé, Konstantin Usevich, Pierre-Olivier Amblard.
Extended L-ensembles: a new representation for Determinantal Point Processes.
In
Annals of Applied Probability, 2023. [PDF]
|
| [18] |
Simon Barthelmé, Nicolas Tremblay, Konstantin Usevich, Pierre-Olivier Amblard.
Determinantal Point Processes in the Flat Limit.
In
Bernoulli, 2023. [PDF]
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| [17] |
Nagham Badreddine, Gisela Zalcman, Florence Appaix, Guillaume Becq, Nicolas Trembay, Frédéric Saudou, Sophie Achard and Elodie Fino.
Spatiotemporal reorganization of corticostriatal network dynamics encodes motor skill learning.
In
Cell Reports, volume 39, 110623, 2022. [PDF]
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| [16] |
Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay.
Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs.
In
Journal of Statistical Mechanics: Theory and Experiment, 093405, 2021. [PDF] [Code]
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| [15] |
Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay.
A unified framework for spectral clustering in sparse graphs.
In
Journal in Machine Learning Research, Vol. 22, No. 217, pp. 1-56, 2021. [PDF] [Code]
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| [14] |
Yusuf Y. Pilavci, Pierre-Olivier Amblard, Simon Barthelmé, Nicolas Tremblay.
Graph Tikhonov regularization and interpolation via random spanning forests.
In IEEE Transactions on Signal and Information Processing over Networks, Vol. 7, pp. 359-374, 2021. [PDF] [Code]
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| [13] |
Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard.
Determinantal Point Processes for Coresets.
In Journal of Machine Learning Research, Vol. 20, No. 168, pp. 1-70, 2019. [PDF] [Code]
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| [12] |
Benjamin Ricaud, Pierre Borgnat, Nicolas Tremblay, Paulo Goncalves, Pierre Vandergheynst.
Fourier could be a data scientist: From graph Fourier transform to signal processing on graphs.
In CRAS (Compte-Rendus de l'Académie des Sciences), Vol. 20, No. 5, pp. 474-488, 2019. [PDF]
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| [11] |
Simon Barthelmé, Pierre-Olivier Amblard, Nicolas Tremblay.
Asymptotic Equivalence of Fixed-size and Varying-size Determinantal Point Processes.
In Bernoulli, Vol. 25, No. 4B, pp. 3555-3589, 2019. [PDF] [Code]
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| [10] |
Nicolas Tremblay.
Independent reanalysis of alleged mind-matter interaction in double-slit experimental data.
In PLOS One, Vol. 14, No. 2, pp. 1-20, journal.pone.0211511, 2019 (corrected and republished in June 2021). [PDF] [Data and code]
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| [9] |
Luc Le Magoarou, Rémi Gribonval, Nicolas Tremblay.
Approximate Fast Graph Fourier Transforms via multi-layer sparse approximations.
In IEEE Transactions on Signal and Information Processing over Networks, Vol. 4, No. 2, pp. 407-420, 2018. [PDF] [Code]
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| [8] |
Rasha Boulos, Nicolas Tremblay, Alain Arneodo, Pierre Borgnat, Benjamin Audit.
Multi-scale structural community organisation of the human genome.
In BMC Bioinformatics, Vol. 18, No. 1, 2017. [Project page] [PDF] [Sup. Mat.]
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| [7] |
Gilles Puy, Nicolas Tremblay, Rémi Gribonval, Pierre Vandergheynst.
Random sampling of bandlimited signals on graphs.
In Applied and Computational Harmonic Analysis, Vol. 44, No. 2, pp. 446-475, 2018.
[PDF] [Code]
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| [6] |
Nicolas Tremblay, Pierre Borgnat.
Subgraph-based Filterbanks for Graph Signals.
In IEEE Transactions on Signal Processing, Vol. 64, No. 15, pp. 3827-3840, 2016.
[PDF] [Code]
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| [5] |
Patrice Abry, Stéphane G. Roux, Herwig Wendt, Paul Messier, Andrew G. Klein, Nicolas Tremblay, Pierre Borgnat, Stéphane Jaffard, Béatrice Vedel, Jim Coddington, and Lee Ann Daffner.
Multiscale Anisotropic Texture Analysis and Classification of Photographic Prints
In IEEE Signal Processing Magazine, Vol. 32, No. 4, pp. 18-27, July 2015.
[PDF]
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| [4] |
Nicolas Tremblay and Pierre Borgnat.
Graph Wavelets for Multiscale Community Mining
In IEEE Transactions on Signal Processing, Vol. 62, No. 20, pp. 5227-5239, October 2014.
[PDF] [Code]
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| [3] |
Nicolas Tremblay, Alain Barrat, Cary Forest, Mark Nornberg, Jean-François Pinton and Pierre Borgnat.
Bootstrapping under constraint for the assessment of group behavior in human contact networks.
In Phys. Rev. E, Vol. 88, No. 5, pp. 052812, November 2013.
[PDF]
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| [2] |
Kévin Tse-Ve-Koon, Nicolas Tremblay, Doru Constantin and Eric Freyssingeas.
Structure, thermodynamics and dynamics of the isotropic phase of spherical non-ionic surfactant micelles.
In Journal of Colloid and Interface Science, Vol. 393, pp. 161–173, 2013.
[PDF]
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| [1] |
Nicolas Tremblay, Eric Larose and Vincent Rossetto.
Probing slow dynamics of consolidated granular multicomposite materials by Diffuse Acoustic Wave Spectroscopy (DAWS).
In Journal of the Acoustical Society of America, Vol. 127, p. 1239, 2010.
[PDF]
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Contributed chapter in collective books
| [3] |
Nicolas Tremblay, Andreas Loukas.
Approximating Spectral Clustering via Sampling: a Review
In book Sampling Techniques for Supervised or Unsupervised Tasks., pp. 129--183, 2020. [PDF]
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| [2] |
Nicolas Tremblay, Paulo Gonçalves, Pierre Borgnat.
Design of graph filters and filterbanks.
In book Cooperative and Graph Signal Processing, pp. 299--324, 2018. [PDF]
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| [1] |
Pierre Borgnat, Céline Robardet, Patrice Abry, Patrick Flandrin, Jean-Baptiste Rouquier et Nicolas Tremblay.
A Dynamical Network View of Lyon’s Vélo’v Shared Bicycle System.
In book Dynamics On and Of Complex Networks, Volume 2, pp. 267--284, 2013.
[PDF]
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Publications in conference Proceedings with review committee
| [34] |
Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter.
Convergence of Graph Neural Networks with generic aggregation functions on random graphs.
In GRETSI, 2023. [PDF]
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| [33] |
Simon Barthelmé, Nicolas Tremblay, Pierre-Olivier Amblard.
A Faster Sampler for Discrete Determinantal Point Processes.
In AISTATS, 2023. [PDF] [Code]
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| [32] |
Hugo Jaquard, Michael Fanuels, Pierre-Olivier Amblard, Rémi Bardenet, Simon Barthelmé, Nicolas Tremblay.
Smoothing Complex-Valued Signals on Graphs with Monte-Carlo.
In ICASSP, 2023. [PDF] [Code]
|
| [31] |
Yusuf Y. Pilavci, Pierre-Olivier Amblard, Simon Barthelmé, Nicolas Tremblay.
Variance Reduction for Inverse Trace Estimation via Random Spanning Forests.
In GRETSI, 2022. [PDF]
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| [30] |
Pierre-Olivier Amblard, Simon Barthelmé, Nicolas Tremblay, Konstantin Usevich.
Mesures d'indépendance dans des rkHs en limite plate.
In GRETSI, 2022. [PDF]
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| [29] |
Yusuf Y. Pilavci, Pierre-Olivier Amblard, Simon Barthelmé, Nicolas Tremblay.
Variance Reduction in Stochastic Methods for Large-scale Regularised Least-square Problems.
In EUSIPCO, 2022. [PDF]
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| [28] |
Hashem Ghanem, Nicolas Keriven, Nicolas Tremblay.
Fast graph kernel with optical random features.
In ICASSP, 2021. [PDF] [Code]
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| [27] |
Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay.
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian.
In Neurips, 2020. [PDF] [Code]
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| [26] |
Yusuf Y. Pilavci, Pierre-Olivier Amblard, Simon Barthelmé, Nicolas Tremblay.
Smoothing graph signals via random spanning forests.
In ICASSP, 2020. [PDF] [Code]
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| [25] |
Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay.
Optimal Laplacian Regularization for Sparse Spectral Community Detection.
In ICASSP, 2020. [PDF]
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| [24] |
Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay.
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs.
In NeurIPS, 2019. [PDF] [Code]
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| [23] |
Simon Barthelmé, Nicolas Tremblay, Alexandre Gaudillière, Luca Avena, Pierre-Olivier Amblard,
Estimating the inverse trace using random forests on graphs.
In GRETSI, August 2019. [PDF]
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| [22] |
Guillaume Becq, Nagham Badreddine, Nicolas Tremblay, Florence Appaix, Gisela Zalcman, Elodie Fino, Sophie ACHARD,
Classification de types de neurones à partir de signaux calciques.
In GRETSI, August 2019. [PDF]
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| [21] |
Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay,
Classification spectrale par la laplacienne déformée dans des graphes réalistes.
In GRETSI, August 2019. [PDF]
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| [20] |
Pierre-Olivier Amblard, Simon Barthelmé, Nicolas Tremblay,
Subsampling with k determinantal point processes for estimating statistics in large data sets.
In SSP, June 2018. [PDF]
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| [19] |
Luc Le Magoarou, Nicolas Tremblay, Rémi Gribonval.
Analyzing the Approximation Error of the Fast Graph Fourier Transform.
In ASILOMAR, November 2017. [PDF] [Code]
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| [18] |
Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard.
Échantillonnage de signaux sur graphes via des processus déterminantaux.
In GRETSI, September 2017. [PDF]
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| [17] |
Nicolas Tremblay, Pierre-Olivier Amblard, Simon Barthelmé.
Graph Sampling with Determinantal Processes.
In EUSIPCO, August 2017. [PDF]
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| [16] |
Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin, Rémi Gribonval.
Compressive K-means.
In ICASSP, March 2017.
[PDF] [Code]
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| [15] |
Nicolas Tremblay, Gilles Puy, Rémi Gribonval, Pierre Vandergheynst.
Compressive Spectral Clustering.
In ICML, June 2016.
[PDF] [Code]
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| [14] |
Nicolas Tremblay, Gilles Puy, Pierre Borgnat, Rémi Gribonval, Pierre Vandergheynst.
Accelerated Spectral Clustering Using Graph Filtering of Random Signals.
In ICASSP, March 2016.
[PDF]
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| [13] |
Nicolas Tremblay, Pierre Borgnat.
Joint Filtering of Graph and Graph-Signals.
In ASILOMAR Conference on Signals, Systems and Computers, November 2015.
[PDF]
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| [12] |
Pierre Borgnat, Paulo Gonçalves, Nicolas Tremblay, Nathanaël Willaime-Angonin.
Community Mining with Graph Filters for Correlation Matrices.
In ASILOMAR Conference on Signals, Systems and Computers, November 2015.
[PDF]
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| [11] |
Stéphane Roux, Nicolas Tremblay, Pierre Borgnat, Patrice Abry, Herwig Wendt, Paul Messier.
Multiscale Anisotropic Texture Unsupervised Clustering for Photographic Paper.
In WIFS (International Workshop on Information Forensics and Security), November 2015.
[PDF]
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| [10] |
Nicolas Tremblay, Stéphane Roux, Pierre Borgnat, Patrice Abry, Herwig Wendt, Paul Messier.
Texture classification of photographic papers: improving spectral clustering using filterbanks on graphs.
In GRETSI, September 2015.
[PDF]
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| [9] |
Rasha Boulos, Nicolas Tremblay, Alain Arnéodo, Pierre Borgnat, Benjamin Audit.
Applications des ondelettes sur graphe en génomique.
In GRETSI, September 2015.
[PDF]
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| [8] |
Nicolas Tremblay, Pierre Borgnat and Patrick Flandrin.
Graph Empirical Mode Decomposition.
In EUSIPCO, September 2014.
[PDF]
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| [7] |
Nicolas Tremblay et Pierre Borgnat.
Multiscale Community Mining in Networks Using
the Graph Wavelet Transform of Random Vectors.
In GlobalSIP, December 2013.
[PDF]
|
| [6] |
Nicolas Tremblay et Pierre Borgnat.
Multiscale Detection of Stable Communities
Using Wavelets on Networks.
In European Conference on Complex Systems, September 2013.
[PDF]
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| [5] |
Nicolas Tremblay et Pierre Borgnat.
Multiscale Community Mining in Networks Using Spectral Graph Wavelets.
In EUSIPCO, September 2013.
[PDF]
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| [4] |
Nicolas Tremblay et Pierre Borgnat.
Partitionnement multi-échelle d’un graphe en communautés : détection des échelles pertinentes.
In GRETSI, September 2013.
[PDF]
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| [3] |
Romain Fontugne, Nicolas Tremblay, Pierre Borgnat, Patrick Flandrin et Hiroshi Esaki.
Mining anomalous electricity consumption using ensemble empirical mode decomposition.
In ICASSP, May 2013.
[PDF]
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| [2] |
Romain Fontugne, Jorge Ortiz, Nicolas Tremblay, Pierre Borgnat,
Patrick Flandrin, Kensuke Fukuda, David Culler and Hiroshi Esaki.
Strip, Bind, and Search: A Method for Identifying Abnormal
Energy Consumption in Buildings.
In IPSN, April 2013.
[PDF]
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| [1] |
Nicolas Tremblay, Alain Barrat, Cary Forest, Mark Nornberg, Jean-François Pinton et Pierre Borgnat.
Constrained Graph Resampling for Group Assessment in Human Social Networks.
In European Conference on Complex Systems, September 2012.
[PDF]
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Unsubmitted research reports
| [1] |
Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard.
Optimized Algorithms to Sample Determinantal Point Processes.
2018. [PDF]
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Participation in workshops (non-exhaustive)
| [11] |
Nicolas Tremblay, Yusuf Yigit Pilavci, Simon Barthelmé, Pierre-Olivier Amblard.
What Can we Compute With Kirchhoff Forests?
In Graph Signal Processing workshop, Oxford, June 2023. [PDF]
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| [10] |
Hugo Jaquard, Pierre-Olivier Amblard, Simon Barthelmé, Nicolas Tremblay.
Angular Synchronization on Graphs with Monte-Carlo.
In Graph Signal Processing workshop, Oxford, June 2023. [PDF]
|
| [9] |
Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter.
Convergence of Message Passing Graph Neural Networks with Generic Aggregation on Random Graphs.
In Graph Signal Processing workshop, Oxford, June 2023. [PDF]]
|
| [8] |
Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard.
Determinantal Point Processes for Corests.
In Journées MAS, Rouen, August 2022.
|
| [7] | Nicolas Tremblay (and all my co-authors on the subject). Random Spanning Forests on Graphs for Fast Laplacian-Based Computations. In Workshop DPP-fermions, June 2022. |
| [6] |
Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard.
Processus ponctuels déterminantaux pour les coresets.
In Journées de Statistiques, 2020. [PDF]
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| [5] |
Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard.
Sampling Bandlimited Graph Signals.
In Journées MAS, Dijon, August 2018.
|
| [4] |
Nicolas Tremblay.
Graph Signal Processing and Community Detection.
In EURANDOM workshop on community detection and network reconstruction, Eindhoven, September 2017.
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| [3] |
Nicolas Tremblay, Gilles Puy, Rémi Gribonval, Pierre Vandergheynst.
Compressive Spectral Clustering.
In Graph Signal Processing Workshop, Philadelphia, May 2016.
|
| [2] |
Nicolas Tremblay, Gilles Puy, Rémi Gribonval, Pierre Vandergheynst.
Compressive Spectral Clustering.
In Data Driven Approach to Networks and Language, Lyon, May 2016.
|
| [1] |
Nicolas Tremblay et Pierre Borgnat.
Multiscale Community Mining in Networks Using
the Graph Wavelet Transform of Random Vectors.
In Analysis and inference for networks, Workshop co-organized by the Gdr ISIS and the GdR Phenix, Paris, Nov 2013.
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PhD dissertation
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Defended in October 2014, and available in French: "Réseaux et signal: des outils de traitement du signal pour l'analyse des réseaux." [Manuscrit, Présentation orale]
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HDR dissertation (habilitation)
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Defended in June 2024: "Graph signals, structures and sketches." [Manuscript, Oral presentation]
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