Publications related to Kernel (statistics)

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

The efficiency of stochastic particle schemes for large scale simulations relies on the ability to preserve a uniform distribution of particles in the whole physical domain. While simple particle split and merge algorithms have been considered previously, ...

2019

Recovering covariance from functional fragments

Victor Panaretos, Marie-Hélène Descary

We consider nonparametric estimation of a covariance function on the unit square, given a sample of discretely observed fragments of functional data. When each sample path is observed only on a subinterval of length , one has no statistical information on ...

2019

Gaussian Process Regression for Maximum Entropy Distribution

Mohsen Sadr

Maximum-Entropy Distributions offer an attractive family of probability densities suitable for moment closure problems. Yet finding the Lagrange multipliers which parametrize these distributions, turns out to be a computational bottleneck for practical clo ...

2019

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit [12, 9], thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Sampling Continuous-time Sparse Signals: A Frequency-domain Perspective

Martin Vetterli, Benjamin Bejar Haro

We address the problem of sampling and reconstruction of sparse signals with finite rate of innovation. We derive general conditions under which perfect reconstruction is possible for sampling kernels satisfying Strang-Fix conditions. Previous results on t ...

2018

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic

In this paper, we consider the problem of sequentially optimizing a black-box function

f

based on noisy samples and bandit feedback. We assume that

f

is smooth in the sense of having a bounded norm in some reproducing kernel Hilbert space (RKHS), yield ...

2017

Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions

Rudolf Haraksim

This study presents a method for computing likelihood ratios (LRs) from multimodal score distributions, as the ones produced by some commercial off-the-shelf automated fingerprint identification systems (AFISs). The AFIS algorithms used to compare fingerma ...

Inst Engineering Technology-IET2017

A Sampling Theory for Non-Decaying Signals

Michaël Unser, Quy Ha Nguyen

The classical assumption in sampling and spline theories is that the input signal is square-integrable, which prevents us from applying such techniques to signals that do not decay or even grow at infinity. In this paper, we develop a sampling theory for m ...

2017

Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds

Sylvain Calinon, Noémie Laure Gwendoline Jaquier

Simultaneous and proportional control of a prosthetic hand and wrist is still a controversial issue, although giant steps have lately been made in this direction. In this paper, we study the application of a novel machine learning method to the problem, wi ...

2017

A kernel compression scheme for fractional differential equations

Jan Sickmann Hesthaven, Daniel Henri Baffet

The nonlocal nature of the fractional integral makes the numerical treatment of fractional dierential equations expensive in terms of computational eort and memory requirements. In this paper we propose a method to reduce these costs while controlling the ...

Society for Industrial and Applied Mathematics2017

Kernel (statistics)

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Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

Recovering covariance from functional fragments

Gaussian Process Regression for Maximum Entropy Distribution

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Sampling Continuous-time Sparse Signals: A Frequency-domain Perspective

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions

A Sampling Theory for Non-Decaying Signals

Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds

A kernel compression scheme for fractional differential equations

Recovering covariance from functional fragments

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

Gaussian Process Regression for Maximum Entropy Distribution

Sampling Continuous-time Sparse Signals: A Frequency-domain Perspective

Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds

Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

A Sampling Theory for Non-Decaying Signals

A kernel compression scheme for fractional differential equations