Publications related to Sparse Linear Models: Variational Approximate Inference and Bayesian Experimental Design

Shape constrained splines as transparent black-box models for bioprocess modeling

Dominique Bonvin, Julien Léo Billeter, Sriniketh Srinivasan

Empirical model identification for biological systems is a challenging task due to the combined effects of complex interactions, nonlinear effects, and lack of specific measurements. In this context, several researchers have provided tools for experimental ...

Elsevier2017

Online Collaborative Prediction of Regional Vote Results

Patrick Thiran, Matthias Grossglauser, Mohammad Emtiyaz Khan, Vincent Etter

We consider online predictions of vote results, where regions across a country vote on an issue under discussion. Such online predictions before and during the day of the vote are useful to media agencies, polling institutes, and political parties, e.g., t ...

IEEE2016

Objective Bayesian Model Selection

Thomas Lugrin

Frequentist and Bayesian approaches to statistics have long been seen as incompatible, but recent work has been done to try and unify them (Bayarri and Berger, 2004; Efron, 2005). Empirical Bayes, approximate Bayesian analysis, and the matching prior appro ...

2015

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Volkan Cevher, Jonathan Mark Scarlett, Yen-Huan Li

We consider the model selection consistency or sparsistency of a broad set of

\ell_1

-regularized

M

-estimators for linear and non-linear statistical models in a unified fashion. For this purpose, we propose the local structured smoothness condition (LSS ...

2015

Active Learning of Self-concordant like Multi-index Functions

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic

We study the problem of actively learning a multi-index function of the form f (x) = g_0 (A_0 x) from its point evaluations, where A_0 ∈ R_{k×d} with k ≪ d. We build on the assumptions and techniques of an existing approach based on low-rank matrix recover ...

2015

Kullback-Leibler Proximal Variational Inference

Pascal Fua, François Fleuret, Pierre Bruno Baqué, Mohammad Emtiyaz Khan

We propose a new variational inference method based on a proximal framework that uses the Kullback-Leibler (KL) divergence as the proximal term. We make two contributions towards exploiting the geometry and structure of the variational bound. Firstly, we p ...

2015

Estimation of Front-Crawl Energy Expenditure Using Wearable Inertial Measurement Units

Kamiar Aminian, Grégoire Millet, Farzin Dadashi

Measurement of the energy expenditure is crucial to understand the biophysics of any kind of human locomotion. Despite the promising application of inertial measurement unit (IMU) for quantification of the energy expenditure during human on-land activities ...

Institute of Electrical and Electronics Engineers2014

Towards Whole Body Fatigue Assessment of Human Movement: A Fatigue-Tracking System Based on Combined sEMG and Accelerometer Signals

Nadia Barbara Figueroa Fernandez

This paper proposes a method to assess the overall fatigue of human body movement. First of all, according to previous research regarding localized muscular fatigue, a linear relation is assumed between the mean frequency and the muscular working time when ...

Mdpi Ag2014

Multiclass Latent Locally Linear Support Vector Machines

Barbara Caputo, Marco Fornoni

Kernelized Support Vector Machines (SVM) have gained the status of o-the-shelf classiers, able to deliver state of the art performance on almost any problem. Still, their practical use is constrained by their computational and memory complexity, which grow ...

2013

Can Multi-Channel FID Navigators Quantify Head Motion?

Rolf Gruetter, Tobias Kober, Michael Herbst, Matthias Seeger, Gunnar Krüger

This work investigates the ability of free induction decay (FID) navigators to provide information on rigid head motion. FID navigators were incorporated in a gradient-echo sequence. In parallel, optical tracking data was acquired and served as the ground ...

2013

Sparse Linear Models: Variational Approximate Inference and Bayesian Experimental Design

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Shape constrained splines as transparent black-box models for bioprocess modeling

Online Collaborative Prediction of Regional Vote Results

Objective Bayesian Model Selection

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Active Learning of Self-concordant like Multi-index Functions

Kullback-Leibler Proximal Variational Inference

Estimation of Front-Crawl Energy Expenditure Using Wearable Inertial Measurement Units

Towards Whole Body Fatigue Assessment of Human Movement: A Fatigue-Tracking System Based on Combined sEMG and Accelerometer Signals

Multiclass Latent Locally Linear Support Vector Machines

Can Multi-Channel FID Navigators Quantify Head Motion?

Shape constrained splines as transparent black-box models for bioprocess modeling

Kullback-Leibler Proximal Variational Inference

Towards Whole Body Fatigue Assessment of Human Movement: A Fatigue-Tracking System Based on Combined sEMG and Accelerometer Signals

Objective Bayesian Model Selection

Active Learning of Self-concordant like Multi-index Functions

Online Collaborative Prediction of Regional Vote Results

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Estimation of Front-Crawl Energy Expenditure Using Wearable Inertial Measurement Units

Multiclass Latent Locally Linear Support Vector Machines

Can Multi-Channel FID Navigators Quantify Head Motion?