Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm. - Rémi Bardenet; Balázs Kégl (ICML, 2010)

Algorithms for Hyper-Parameter Optimization. - James Bergstra; Rémi Bardenet; Yoshua Bengio; Balázs Kégl (NIPS, 2011)

Adaptive Metropolis with Online Relabeling. - Rémi Bardenet; Olivier Cappé; Gersende Fort; Balázs Kégl (AISTATS, 2012)

Collaborative hyperparameter tuning. - Rémi Bardenet; Mátyás Brendel; Balázs Kégl; Michèle Sebag (ICML (2), 2013)

Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach. - Rémi Bardenet; Arnaud Doucet; Christopher C. Holmes (ICML, 2014)

Inference for determinantal point processes without spectral knowledge. - Rémi Bardenet; Michalis K. Titsias (NIPS, 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; et al. (PLoS Comput. Biol., 2015)

Hierarchical Bayesian Modelling of Variability and Uncertainty in Synthetic Action Potential Traces. - Ross H. Johnstone; Rémi Bardenet; David Gavaghan; Liudmila Polonchuk; Mark Davies; Gary R. Mirams (CinC, 2016)

Zonotope Hit-and-run for Efficient Sampling from Projection DPPs. - Guillaume Gautier; Rémi Bardenet; Michal Valko (ICML, 2017)

On Markov chain Monte Carlo methods for tall data. - Rémi Bardenet; Arnaud Doucet; Christopher C. Holmes (J. Mach. Learn. Res., 2017)

From Random Matrices to Monte Carlo Integration Via Gaussian Quadrature. - Rémi Bardenet; Adrien Hardy (SSP, 2018)

DPPy: Sampling Determinantal Point Processes with Python. - Guillaume Gautier; Rémi Bardenet; Michal Valko (CoRR, 2018)

Kernel quadrature with DPPs. - Ayoub Belhadji; Rémi Bardenet; Pierre Chainais (NeurIPS, 2019)

On two ways to use determinantal point processes for Monte Carlo integration. - Guillaume Gautier; Rémi Bardenet; Michal Valko (NeurIPS, 2019)

DPPy: DPP Sampling with Python. - Guillaume Gautier; Guillermo Polito; Rémi Bardenet; Michal Valko (J. Mach. Learn. Res., 2019)

Kernel interpolation with continuous volume sampling. - Ayoub Belhadji; Rémi Bardenet; Pierre Chainais (ICML, 2020)

Learning from DPPs via Sampling: Beyond HKPV and symmetry. - Rémi Bardenet; Subhroshekhar Ghosh (CoRR, 2020)

A determinantal point process for column subset selection. - Ayoub Belhadji; Rémi Bardenet; Pierre Chainais (J. Mach. Learn. Res., 2020)

Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD. - Rémi Bardenet; Subhroshekhar Ghosh; Meixia Lin (NeurIPS, 2021)

Nonparametric estimation of continuous DPPs with kernel methods. - Michaël Fanuel; Rémi Bardenet (NeurIPS, 2021)

Fast sampling from β-ensembles. - Guillaume Gautier; Rémi Bardenet; Michal Valko (Stat. Comput., 2021)

Turning repulsive point processes into subsampling algorithms. - Rémi Bardenet (2022)

Sparsification of the regularized magnetic Laplacian with multi-type spanning forests. - Michaël Fanuel; Rémi Bardenet (CoRR, 2022)

A Covariant, Discrete Time-Frequency Representation Tailored for Zero-Based Signal Detection. - Barbara Pascal; Rémi Bardenet (IEEE Trans. Signal Process., 2022)

Smoothing Complex-Valued Signals on Graphs with Monte-Carlo. - Hugo Jaquard; Michaël Fanuel; Pierre-Olivier Amblard; Rémi Bardenet; Simon Barthelmé; Nicolas Tremblay (ICASSP, 2023)

On sampling determinantal and Pfaffian point processes on a quantum computer. - Rémi Bardenet; Michaël Fanuel; Alexandre Feller (CoRR, 2023)

Signal reconstruction using determinantal sampling. - Ayoub Belhadji; Rémi Bardenet; Pierre Chainais (CoRR, 2023)

On proportional volume sampling for experimental design in general spaces. - Arnaud Poinas; Rémi Bardenet (Stat. Comput., 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., 2023)

Small coresets via negative dependence: DPPs, linear statistics, and concentration. - Rémi Bardenet; Subhroshekhar Ghosh; Hugo Simon-Onfroy; Hoang Son Tran (NeurIPS, 2024)

Benchmarking multi-component signal processing methods in the time-frequency plane. - Juan Manuel Miramont; Rémi Bardenet; Pierre Chainais; François Auger (CoRR, 2024)

Monte Carlo with kernel-based Gibbs measures: Guarantees for probabilistic herding. - Martin Rouault; Rémi Bardenet; Mylène Maïda (CoRR, 2024)

Point Processes and spatial statistics in time-frequency analysis. - Barbara Pascal; Rémi Bardenet (CoRR, 2024)

On the Number of Steps of CyclePopping in Weakly Inconsistent U(1)-Connection Graphs. - Michaël Fanuel; Rémi Bardenet (CoRR, 2024)

Monte Carlo methods on compact complex manifolds using Bergman kernels. - Thibaut Lemoine; Rémi Bardenet (CoRR, 2024)

Adaptive hyperparameter tuning for time-frequency algorithms based on the zeros of the spectrogram. - Juan Manuel Miramont; Rémi Bardenet; Pierre Chainais; François Auger (SSP, 2025)

Negative Dependence as a toolbox for machine learning : review and new developments. - Hoang Son Tran; Vladimir Petrovic; Rémi Bardenet; Subhroshekhar Ghosh (CoRR, 2025)

Bypassing orthogonalization in the quantum DPP sampler. - Michaël Fanuel; Rémi Bardenet (CoRR, 2025)

Quenched large deviations for Monte Carlo integration with Coulomb gases. - Rémi Bardenet; Mylène Maïda; Martin Rouault (CoRR, 2025)