6 papers
State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
Hoang-Son Tran, Pranav Gupta, Rémi Bardenet +1
Determinantal point processes (DPPs) have emerged as a kernelized alternative to vanilla independent sampling for generating efficient minibatches, coresets and other parsimonious…
On two fundamental properties of the zeros of spectrograms of noisy signals
Arnaud Poinas, Rémi Bardenet
The spatial distribution of the zeros of the spectrogram is significantly altered when a signal is added to white Gaussian noise. The zeros tend to delineate the support of the sig…
Repulsive Monte Carlo on the sphere for the sliced Wasserstein distance
Vladimir Petrovic, Rémi Bardenet, Agnès Desolneux
In this paper, we consider the problem of computing the integral of a function on the unit sphere, in any dimension, using Monte Carlo methods. Although the methods we present are…
Negative Dependence as a toolbox for machine learning : review and new developments
Hoang-Son Tran, Vladimir Petrovic, Remi Bardenet +1
Negative dependence is becoming a key driver in advancing learning capabilities beyond the limits of traditional independence. Recent developments have evidenced support towards ne…
Filtering through a topological lens: homology for point processes on the time-frequency plane
Juan Manuel Miramont, Kin Aun Tan, Soumendu Sundar Mukherjee +2
We introduce a very general approach to the analysis of signals from their noisy measurements from the perspective of Topological Data Analysis (TDA). While TDA has emerged as a po…
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…