Publications associées à Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers

Kernel Density Estimation through Density Constrained Near Neighbor Search

In this paper we revisit the kernel density estimation problem: given a kernel K(x, y) and a dataset of n points in high dimensional Euclidean space, prepare a data structure that can quickly output, given a query q, a (1 + epsilon)-approximation to mu := ...

IEEE2020

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing

Mikhail Kapralov, Amir Zandieh, Navid Nouri

Random binning features, introduced in the seminal paper of Rahimi and Recht '07, are an efficient method for approximating a kernel matrix using locality sensitive hashing. Random binning features provide a very simple and efficient way to approximate the ...

ADDISON-WESLEY PUBL CO2020

Robust discrete choice models with t-distributed kernel errors

Michel Bierlaire, Thomas Gasos, Prateek Bansal

Models that are robust to aberrant choice behaviour have received limited attention in discrete choice analysis. In this paper, we analyse two robust alternatives to the multinomial probit (MNP) model. Both alternative models belong to the family of robit ...

EPFL2020

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Pascal Frossard, Arun Venkitaraman

In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted either with missing samples or large perturbations. The presence of sparse ...

IEEE2019

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

Benchmarking Touchscreen Biometrics for Mobile Authentication

We study user interaction with touchscreens based on swipe gestures for personal authentication. This approach has been analyzed only recently in the last few years in a series of disconnected and limited works. We summarize those recent efforts and then c ...

2018

Discovering Interaction Patterns in Online Learning Environments

Mina Shirvani Boroujeni

The increasing amount of data collected in online learning environments provides unique opportunities to better understand the learning processes in different educational settings. Learning analytics research aims at understanding and optimizing learning a ...

EPFL2018

Advancing the State of Network Switch ASIC Offloading in the Linux Kernel

Andy Roulin

Modern data-center network operating systems rely on proprietary user-space daemons wrapping SDKs from switch vendors. Linux-based variants of these operating systems have benefited from increasing and simplified dataplane offloading support in recent year ...

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

Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers

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Kernel Density Estimation through Density Constrained Near Neighbor Search

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing

Robust discrete choice models with t-distributed kernel errors

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Gaussian Process Regression for Maximum Entropy Distribution

Benchmarking Touchscreen Biometrics for Mobile Authentication

Discovering Interaction Patterns in Online Learning Environments

Advancing the State of Network Switch ASIC Offloading in the Linux Kernel

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

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

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing

Benchmarking Touchscreen Biometrics for Mobile Authentication

Discovering Interaction Patterns in Online Learning Environments

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Kernel Density Estimation through Density Constrained Near Neighbor Search

Robust discrete choice models with t-distributed kernel errors

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Gaussian Process Regression for Maximum Entropy Distribution

Advancing the State of Network Switch ASIC Offloading in the Linux Kernel

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