Kernels for vector-valued functions: A review
MA Alvarez, L Rosasco… - Foundations and Trends …, 2012 - nowpublishers.com
Kernel methods are among the most popular techniques in machine learning. From a
regularization perspective they play a central role in regularization theory as they provide a …
regularization perspective they play a central role in regularization theory as they provide a …
[PDF][PDF] Computationally efficient convolved multiple output Gaussian processes
MA Alvarez, ND Lawrence - The Journal of Machine Learning Research, 2011 - jmlr.org
Recently there has been an increasing interest in regression methods that deal with multiple
outputs. This has been motivated partly by frameworks like multitask learning, multisensor …
outputs. This has been motivated partly by frameworks like multitask learning, multisensor …
Semiparametric regression of multidimensional genetic pathway data: least-squares kernel machines and linear mixed models
We consider a semiparametric regression model that relates a normal outcome to covariates
and a genetic pathway, where the covariate effects are modeled parametrically and the …
and a genetic pathway, where the covariate effects are modeled parametrically and the …
Predicting clinical outcomes in glioblastoma: an application of topological and functional data analysis
Glioblastoma multiforme (GBM) is an aggressive form of human brain cancer that is under
active study in the field of cancer biology. Its rapid progression and the relative time cost of …
active study in the field of cancer biology. Its rapid progression and the relative time cost of …
Nonparametric Bayes applications to biostatistics
DB Dunson - Bayesian nonparametrics, 2010 - books.google.com
This chapter provides a brief review and motivation for the use of nonparametric Bayes
methods in biostatistical applications. Clearly, the nonparametric Bayes biostatistical …
methods in biostatistical applications. Clearly, the nonparametric Bayes biostatistical …
A Bayesian approach to improved calibration and prediction of groundwater models with structural error
T Xu, AJ Valocchi - Water Resources Research, 2015 - Wiley Online Library
Numerical groundwater flow and solute transport models are usually subject to model
structural error due to simplification and/or misrepresentation of the real system, which …
structural error due to simplification and/or misrepresentation of the real system, which …
Quantifying model structural error: Efficient B ayesian calibration of a regional groundwater flow model using surrogates and a data‐driven error model
Groundwater model structural error is ubiquitous, due to simplification and/or
misrepresentation of real aquifer systems. During model calibration, the basic …
misrepresentation of real aquifer systems. During model calibration, the basic …
A topological data analytic approach for discovering biophysical signatures in protein dynamics
Identifying structural differences among proteins can be a non-trivial task. When contrasting
ensembles of protein structures obtained from molecular dynamics simulations, biologically …
ensembles of protein structures obtained from molecular dynamics simulations, biologically …
Learning fair representations for kernel models
Fair representations are a powerful tool for establishing criteria like statistical parity, proxy
non-discrimination, and equality of opportunity in learned models. Existing techniques for …
non-discrimination, and equality of opportunity in learned models. Existing techniques for …
Generalized Gaussian process regression model for non-Gaussian functional data
B Wang, JQ Shi - Journal of the American Statistical Association, 2014 - Taylor & Francis
In this article, we propose a generalized Gaussian process concurrent regression model for
functional data, where the functional response variable has a binomial, Poisson, or other …
functional data, where the functional response variable has a binomial, Poisson, or other …