Rémi Bardenet
Publications
Publications
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Adaptive hyperparameter tuning for time-frequency algorithms based on the zeros of the spectrogram23rd IEEE Statistical Signal Processing Workshop, IEEE, Jun 2025, Edimbourg, United Kingdom. ⟨10.1109/SSP64130.2025.11073382⟩ |
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Transformée de Kravchuk réalignée, implémentation efficace et illustration sur signaux élémentaires et réelsXXXe Colloque Francophone de Traitement du Signal et des Images (GRETSI), GRETSI, Aug 2025, Strasbourg, France |
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Intégrer plusieurs fonctions simultanément avec des mesures de GibbsXXXe Colloque Francophone de Traitement du Signal et des Images, GRETSI 2025, Aug 2025, Strasbourg (FRANCE), France |
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Small coresets via negative dependence: DPPs, linear statistics, and concentrationNeurIPS 2024 - Thirty-eigth Conference on Neural Information Processing Systems, 2024, Vancouver, BC, Canada, Canada |
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Smoothing complex-valued signals on Graphs with Monte-CarloICASSP 2023 - IEEE International Conference on Acoustics, Speech and Signal Processing, Jun 2023, Rhodes Island, Greece. ⟨10.1109/ICASSP49357.2023.10096354⟩ |
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A public benchmark for denoising and detection methodsXXVIIIème Colloque Francophone du GRETSI, GRETSI, Sep 2022, Nancy, France. pp.1-4 |
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Estimation de la fonction de structure d'un processus ponctuel pour l'étude d'hyperuniformitéXXVIIIème Colloque Francophone de Traitement du Signal et des Images GRETSI 2022, Sep 2022, Nancy, France |
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Une famille de représentations covariantes de signaux discrets et son application à la détection de signaux à partir de leurs zérosColloque Francophone de Traitement du Signal et des Images (GRETSI), Sep 2022, Nancy, France |
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Nonparametric estimation of continuous DPPs with kernel methodsNeurIPS 2021 - Thirty-fifth Conference on Neural Information Processing Systems, 2021, Virtual event, Turkmenistan |
Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGDNeurIPS 2021 - Thirty-fifth Conference on Neural Information Processing Systems,, 2021, Virtual event, Turkmenistan |
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Kernel interpolation with continuous volume samplingICML 2020 - International Conference on Machine Learning, 2020, Vienna, Austria |
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On two ways to use determinantal point processes for Monte Carlo integration -- Long versionNeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, Jun 2019, Vancouver, Canada |
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A correspondence between zeros of time-frequency transforms and Gaussian analytic functions13th International Conference on Sampling Theory and Applications, SampTA 2019, Aug 2019, Bordeaux, France |
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Kernel quadrature with DPPsNeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, Jun 2019, Vancouver, Canada |
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Un processus ponctuel déterminantal pour la sélection d'attributsGRETSI 2019, Aug 2019, Lille, France |
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From random matrices to Monte Carlo integration via Gaussian quadratureIEEE Statistical Signal processing workshop, 2018, Freiburg, Germany |
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Zonotope hit-and-run for efficient sampling from projection DPPsInternational Conference on Machine Learning, 2017, Sydney, Australia |
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Inference for determinantal point processes without spectral knowledgeNeural Information Processing Systems (NIPS), Dec 2015, Montréal, Canada |
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Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approachInternational Conference on Machine Learning (ICML), 2014, Beijing, China. pp.405-413 |
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Collaborative hyperparameter tuning30th International Conference on Machine Learning (ICML 2013), Jun 2013, Atlanta, United States. pp.199-207 |
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Highlights from the Pierre Auger Observatory33rd International Cosmic Ray Conference (ICRC2013), Jul 2013, Rio de Janeiro, Brazil |
Detection and estimation of high energy physical particles using Monte Carlo methodsColloque du GRETSI, Sep 2013, Brest, France |
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Monte Carlo methodsIN2P3 School of Statistics (SOS2012), May 2012, Autrans, France. pp.022002, ⟨10.1051/epjconf/20135502002⟩ |
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Adaptive Metropolis with online relabelingFifteenth International Conference on Artificial Intelligence and Statistics, Apr 2012, La Palma, Spain. pp.91-99 |
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An adaptive Monte-Carlo Markov chain algorithm for inference from mixture signals14th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2011), Sep 2011, Uxbridge, United Kingdom. pp.012044, ⟨10.1088/1742-6596/368/1/012044⟩ |
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The Pierre Auger Observatory IV: Operation and Monitoring32nd International Cosmic Ray Conference - ICRC 2011, 2011, Beijing, China |
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The Pierre Auger Observatory I: The Cosmic Ray Energy Spectrum and Related Measurements32nd International Cosmic Ray Conference (ICRC2011), 2011, Beijing, China |
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The Pierre Auger Observatory V: Enhancements32nd International Cosmic Ray Conference (ICRC2011), Aug 2011, Beijing, China |
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Algorithms for Hyper-Parameter Optimization25th Annual Conference on Neural Information Processing Systems (NIPS 2011), Dec 2011, Granada, Spain |
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The Pierre Auger Observatory III: Other Astrophysical Observations32nd International Cosmic Ray Conference - ICRC 2011, 2011, Beijing, China |
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The Pierre Auger Observatory II: Studies of Cosmic Ray Composition and Hadronic Interaction models32nd International Cosmic Ray Conference - ICRC 2011, 2011, Beijing, China |
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Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm27th International Conference on Machine Learning (ICML 2010), Jun 2010, Haifa, Israel. pp.55-62 |
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Relabelling MCMC Algorithms in Bayesian Mixture LearningSnowbird Learning Workshop, Apr 2011, Fort Lauderdale, United States |
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Sampling-based optimization with mixturesOPT 2009: 2nd NIPS Workshop on Optimization for Machine Learning, Dec 2009, Whistler, Canada |
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Point Processes and spatial statistics in time-frequency analysisHermine Biermé. Stochastic Geometry: Percolation, Tesselations, Gaussian Fields and Point Processes, Springer, Lecture Notes in Mathematics, 2025, 978-3-031-87263-1 |
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Towards adaptive learning and inference - Applications to hyperparameter tuning and astroparticle physicsMethodology [stat.ME]. Université Paris Sud - Paris XI, 2012. English. ⟨NNT : ⟩ |
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Turning repulsive point processes into subsampling algorithmsSignal and Image Processing. Université de Lille, 2022 |