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Rémi Bardenet
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2020 – today
- 2024
- [i16]Juan Manuel Miramont, Rémi Bardenet, Pierre Chainais, François Auger:
Benchmarking multi-component signal processing methods in the time-frequency plane. CoRR abs/2402.08521 (2024) - [i15]Martin Rouault, Rémi Bardenet, Mylène Maïda:
Monte Carlo with kernel-based Gibbs measures: Guarantees for probabilistic herding. CoRR abs/2402.11736 (2024) - [i14]Barbara Pascal, Rémi Bardenet:
Point Processes and spatial statistics in time-frequency analysis. CoRR abs/2402.19172 (2024) - [i13]Michaël Fanuel, Rémi Bardenet:
On the Number of Steps of CyclePopping in Weakly Inconsistent U(1)-Connection Graphs. CoRR abs/2404.14803 (2024) - [i12]Thibaut Lemoine, Rémi Bardenet:
Monte Carlo methods on compact complex manifolds using Bergman kernels. CoRR abs/2405.09203 (2024) - 2023
- [j8]Arnaud Poinas
, Rémi Bardenet:
On proportional volume sampling for experimental design in general spaces. Stat. Comput. 33(1): 29 (2023) - [j7]Diala Hawat
, Guillaume Gautier, Rémi Bardenet, Raphaël Lachièze-Rey:
On estimating the structure factor of a point process, with applications to hyperuniformity. Stat. Comput. 33(3): 61 (2023) - [c15]Hugo Jaquard, Michaël Fanuel, Pierre-Olivier Amblard, Rémi Bardenet, Simon Barthelmé, Nicolas Tremblay:
Smoothing Complex-Valued Signals on Graphs with Monte-Carlo. ICASSP 2023: 1-5 - [i11]Rémi Bardenet, Michaël Fanuel, Alexandre Feller:
On sampling determinantal and Pfaffian point processes on a quantum computer. CoRR abs/2305.15851 (2023) - [i10]Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
Signal reconstruction using determinantal sampling. CoRR abs/2310.09437 (2023) - 2022
- [b1]Rémi Bardenet:
Turning repulsive point processes into subsampling algorithms. University of Lille, France, 2022 - [j6]Barbara Pascal
, Rémi Bardenet:
A Covariant, Discrete Time-Frequency Representation Tailored for Zero-Based Signal Detection. IEEE Trans. Signal Process. 70: 2950-2961 (2022) - [i9]Michaël Fanuel, Rémi Bardenet:
Sparsification of the regularized magnetic Laplacian with multi-type spanning forests. CoRR abs/2208.14797 (2022) - 2021
- [j5]Guillaume Gautier
, Rémi Bardenet, Michal Valko:
Fast sampling from β-ensembles. Stat. Comput. 31(1): 7 (2021) - [c14]Rémi Bardenet, Subhroshekhar Ghosh, Meixia Lin:
Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD. NeurIPS 2021: 16226-16237 - [c13]Michaël Fanuel, Rémi Bardenet:
Nonparametric estimation of continuous DPPs with kernel methods. NeurIPS 2021: 24124-24136 - [i8]Michaël Fanuel, Rémi Bardenet:
Nonparametric estimation of continuous DPPs with kernel methods. CoRR abs/2106.14210 (2021) - [i7]Rémi Bardenet, Subhroshekhar Ghosh, Meixia Lin:
Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD. CoRR abs/2112.06007 (2021) - 2020
- [j4]Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
A determinantal point process for column subset selection. J. Mach. Learn. Res. 21: 197:1-197:62 (2020) - [c12]Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
Kernel interpolation with continuous volume sampling. ICML 2020: 725-735 - [i6]Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
Kernel interpolation with continuous volume sampling. CoRR abs/2002.09677 (2020) - [i5]Rémi Bardenet, Subhroshekhar Ghosh:
Learning from DPPs via Sampling: Beyond HKPV and symmetry. CoRR abs/2007.04287 (2020)
2010 – 2019
- 2019
- [j3]Guillaume Gautier, Guillermo Polito, Rémi Bardenet, Michal Valko:
DPPy: DPP Sampling with Python. J. Mach. Learn. Res. 20: 180:1-180:7 (2019) - [c11]Guillaume Gautier, Rémi Bardenet, Michal Valko:
On two ways to use determinantal point processes for Monte Carlo integration. NeurIPS 2019: 7768-7777 - [c10]Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
Kernel quadrature with DPPs. NeurIPS 2019: 12907-12917 - [i4]Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
Kernel quadrature with DPPs. CoRR abs/1906.07832 (2019) - 2018
- [c9]Rémi Bardenet, Adrien Hardy:
From Random Matrices to Monte Carlo Integration Via Gaussian Quadrature. SSP 2018: 468-472 - [i3]Guillaume Gautier, Rémi Bardenet, Michal Valko:
DPPy: Sampling Determinantal Point Processes with Python. CoRR abs/1809.07258 (2018) - [i2]Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
A determinantal point process for column subset selection. CoRR abs/1812.09771 (2018) - 2017
- [j2]Rémi Bardenet, Arnaud Doucet, Christopher C. Holmes:
On Markov chain Monte Carlo methods for tall data. J. Mach. Learn. Res. 18: 47:1-47:43 (2017) - [c8]Guillaume Gautier, Rémi Bardenet, Michal Valko:
Zonotope Hit-and-run for Efficient Sampling from Projection DPPs. ICML 2017: 1223-1232 - [i1]Guillaume Gautier, Rémi Bardenet, Michal Valko:
Zonotope hit-and-run for efficient sampling from projection DPPs. CoRR abs/1705.10498 (2017) - 2016
- [c7]Ross Johnstone, Rémi Bardenet, David Gavaghan, Liudmila Polonchuk, Mark Davies, Gary R. Mirams:
Hierarchical Bayesian Modelling of Variability and Uncertainty in Synthetic Action Potential Traces. CinC 2016 - 2015
- [j1]Bernhard Knapp, Rémi Bardenet, Miguel O. Bernabeu, Rafel Bordas
, Maria Bruna
, Ben Calderhead, Jonathan Cooper
, Alexander G. Fletcher
, Derek Groen, Bram Kuijper
, Joanna Lewis
, Greg J. McInerny, Timo Minssen
, James M. Osborne
, Verena Paulitschke, Joe Pitt-Francis
, Jelena Todoric, Christian A. Yates
, David Gavaghan
, Charlotte M. Deane
:
Ten Simple Rules for a Successful Cross-Disciplinary Collaboration. PLoS Comput. Biol. 11(4) (2015) - [c6]Rémi Bardenet, Michalis K. Titsias:
Inference for determinantal point processes without spectral knowledge. NIPS 2015: 3393-3401 - 2014
- [c5]Rémi Bardenet, Arnaud Doucet, Christopher C. Holmes:
Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach. ICML 2014: 405-413 - 2013
- [c4]Rémi Bardenet, Mátyás Brendel, Balázs Kégl, Michèle Sebag:
Collaborative hyperparameter tuning. ICML (2) 2013: 199-207 - 2012
- [c3]Rémi Bardenet, Olivier Cappé, Gersende Fort, Balázs Kégl:
Adaptive Metropolis with Online Relabeling. AISTATS 2012: 91-99 - 2011
- [c2]James Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl:
Algorithms for Hyper-Parameter Optimization. NIPS 2011: 2546-2554 - 2010
- [c1]Rémi Bardenet, Balázs Kégl:
Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm. ICML 2010: 55-62
Coauthor Index
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last updated on 2024-06-25 20:58 CEST by the dblp team
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