Publications associées à Sparse Linear Models: Variational Approximate Inference and Bayesian Experimental Design

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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

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?

Objective Bayesian Model Selection

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

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

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

Can Multi-Channel FID Navigators Quantify Head Motion?

Online Collaborative Prediction of Regional Vote Results

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

Multiclass Latent Locally Linear Support Vector Machines