Probabilistic Expert Knowledge Elicitation of Feature Relevances in Sparse Linear Regression

Powered by TCPDF (www.tcpdf.org) This material is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. Daee, Pedram; Peltola, Tomi; Soare, Marta; Kaski, Samuel

[1]  Don R. Hush,et al.  Interactive Machine Learning in Data Exploitation , 2013, Computing in Science & Engineering.

[2]  K. Chaloner,et al.  Bayesian Experimental Design: A Review , 1995 .

[3]  Saleema Amershi,et al.  Designing for effective end-user interaction with machine learning , 2011, UIST '11 Adjunct.

[4]  Paul H. Garthwaite,et al.  Quantifying Expert Opinion in Linear Regression Problems , 1988 .

[5]  Daniel Hernández-Lobato,et al.  Expectation propagation in linear regression models with spike-and-slab priors , 2015, Machine Learning.

[6]  Tom Minka,et al.  Expectation Propagation for approximate Bayesian inference , 2001, UAI.

[7]  Florian Steinke,et al.  Bayesian Inference and Optimal Design in the Sparse Linear Model , 2007, AISTATS.

[8]  E. George,et al.  Journal of the American Statistical Association is currently published by American Statistical Association. , 2007 .

[9]  Wayne S. Smith,et al.  Interactive Elicitation of Opinion for a Normal Linear Model , 1980 .

[10]  Jeremy E. Oakley,et al.  Uncertain Judgements: Eliciting Experts' Probabilities , 2006 .

[11]  Samuel Kaski,et al.  Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction , 2016, Machine Learning.

[12]  Burr Settles,et al.  Active Learning Literature Survey , 2009 .

[13]  Daniel Hernández-Lobato,et al.  Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation , 2013, J. Mach. Learn. Res..