1 citations · 1 across the 3 of their papers we have counts for
3 papers
Small coresets via negative dependence: DPPs, linear statistics, and concentration
Rémi Bardenet, Subhroshekhar Ghosh, Hugo Simon-Onfroy +1
Determinantal point processes (DPPs) are random configurations of points with tunable negative dependence. Because sampling is tractable, DPPs are natural candidates for subsamplin…
Benchmarking multi-component signal processing methods in the time-frequency plane
Juan M. Miramont, Rémi Bardenet, Pierre Chainais +1
Signal processing in the time-frequency plane has a long history and remains a field of methodological innovation. For instance, detection and denoising based on the zeros of the s…
Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD
Remi Bardenet, Subhro Ghosh, Meixia Lin
Stochastic gradient descent (SGD) is a cornerstone of machine learning. When the number N of data items is large, SGD relies on constructing an unbiased estimator of the gradient o…