Rémi Bardenet

According to our database, Rémi Bardenet authored at least 32 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Point Processes and spatial statistics in time-frequency analysis.

Barbara Pascal, Rémi Bardenet

CoRR, 2024

Monte Carlo with kernel-based Gibbs measures: Guarantees for probabilistic herding.

Martin Rouault, Rémi Bardenet, Mylène Maïda

CoRR, 2024

Benchmarking multi-component signal processing methods in the time-frequency plane.

Juan Manuel Miramont, Rémi Bardenet, Pierre Chainais, François Auger

CoRR, 2024

2023

On estimating the structure factor of a point process, with applications to hyperuniformity.

Diala Hawat, Guillaume Gautier, Rémi Bardenet, Raphaël Lachièze-Rey

Stat. Comput., June, 2023

On proportional volume sampling for experimental design in general spaces.

Arnaud Poinas, Rémi Bardenet

Stat. Comput., 2023

Signal reconstruction using determinantal sampling.

Ayoub Belhadji, Rémi Bardenet, Pierre Chainais

CoRR, 2023

On sampling determinantal and Pfaffian point processes on a quantum computer.

Rémi Bardenet, Michaël Fanuel, Alexandre Feller

CoRR, 2023

Smoothing Complex-Valued Signals on Graphs with Monte-Carlo.

Hugo Jaquard, Michaël Fanuel, Pierre-Olivier Amblard, Rémi Bardenet, Simon Barthelmé, Nicolas Tremblay

Proceedings of the IEEE International Conference on Acoustics, 2023

2022

A Covariant, Discrete Time-Frequency Representation Tailored for Zero-Based Signal Detection.

Barbara Pascal, Rémi Bardenet

IEEE Trans. Signal Process., 2022

Sparsification of the regularized magnetic Laplacian with multi-type spanning forests.

Michaël Fanuel, Rémi Bardenet

CoRR, 2022

Turning repulsive point processes into subsampling algorithms.

Rémi Bardenet

, 2022

2021

Fast sampling from β-ensembles.

Guillaume Gautier, Rémi Bardenet, Michal Valko

Stat. Comput., 2021

Nonparametric estimation of continuous DPPs with kernel methods.

Michaël Fanuel, Rémi Bardenet

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD.

Rémi Bardenet, Subhroshekhar Ghosh, Meixia Lin

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

A determinantal point process for column subset selection.

Ayoub Belhadji, Rémi Bardenet, Pierre Chainais

J. Mach. Learn. Res., 2020

Learning from DPPs via Sampling: Beyond HKPV and symmetry.

Rémi Bardenet, Subhroshekhar Ghosh

CoRR, 2020

Kernel interpolation with continuous volume sampling.

Ayoub Belhadji, Rémi Bardenet, Pierre Chainais

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

DPPy: DPP Sampling with Python.

Guillaume Gautier, Guillermo Polito, Rémi Bardenet, Michal Valko

J. Mach. Learn. Res., 2019

On two ways to use determinantal point processes for Monte Carlo integration.

Guillaume Gautier, Rémi Bardenet, Michal Valko

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Kernel quadrature with DPPs.

Ayoub Belhadji, Rémi Bardenet, Pierre Chainais

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

DPPy: Sampling Determinantal Point Processes with Python.

Guillaume Gautier, Rémi Bardenet, Michal Valko

CoRR, 2018

From Random Matrices to Monte Carlo Integration Via Gaussian Quadrature.

Rémi Bardenet, Adrien Hardy

Proceedings of the 2018 IEEE Statistical Signal Processing Workshop, 2018

2017

On Markov chain Monte Carlo methods for tall data.

Rémi Bardenet, Arnaud Doucet, Christopher C. Holmes

J. Mach. Learn. Res., 2017

Zonotope Hit-and-run for Efficient Sampling from Projection DPPs.

Guillaume Gautier, Rémi Bardenet, Michal Valko

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Hierarchical Bayesian Modelling of Variability and Uncertainty in Synthetic Action Potential Traces.

Ross Johnstone, Rémi Bardenet, David Gavaghan, Liudmila Polonchuk, Mark Davies, Gary R. Mirams

Proceedings of the Computing in Cardiology, CinC 2016, Vancouver, 2016

2015

Ten Simple Rules for a Successful Cross-Disciplinary Collaboration.

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

PLoS Comput. Biol., 2015

Inference for determinantal point processes without spectral knowledge.

Rémi Bardenet, Michalis K. Titsias

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach.

Rémi Bardenet, Arnaud Doucet, Christopher C. Holmes

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Collaborative hyperparameter tuning.

Rémi Bardenet, Mátyás Brendel, Balázs Kégl, Michèle Sebag

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Adaptive Metropolis with Online Relabeling.

Rémi Bardenet, Olivier Cappé, Gersende Fort, Balázs Kégl

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

2011

Algorithms for Hyper-Parameter Optimization.

James Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm.

Rémi Bardenet, Balázs Kégl

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010