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Found 10 papers, 1 papers with code
Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer's disease classification
We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure.
Optimal distributed composite testing in high-dimensional Gaussian models with 1-bit communication
In this paper we study the problem of signal detection in Gaussian noise in a distributed setting where the local machines in the star topology can communicate a single bit of information.
View selection in multi-view stacking: Choosing the meta-learner
The remaining four meta-learners, namely nonnegative ridge regression, nonnegative forward selection, stability selection and the interpolating predictor, show little advantages in order to be preferred over the other three.
Spike and slab variational Bayes for high dimensional logistic regression
Variational Bayes (VB) is a popular scalable alternative to Markov chain Monte Carlo for Bayesian inference.
Distributed function estimation: adaptation using minimal communication
We investigate whether in a distributed setting, adaptive estimation of a smooth function at the optimal rate is possible under minimal communication.
Debiased Bayesian inference for average treatment effects
Bayesian approaches have become increasingly popular in causal inference problems due to their conceptual simplicity, excellent performance and in-built uncertainty quantification ('posterior credible sets').
Variational Bayes for high-dimensional linear regression with sparse priors
We study a mean-field spike and slab variational Bayes (VB) approximation to Bayesian model selection priors in sparse high-dimensional linear regression.
Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning
We compare the performance of StaPLR with an existing view selection method called the group lasso and observe that, in terms of view selection, StaPLR is often more conservative and has a consistently lower false positive rate.
Adaptive distributed methods under communication constraints
We study distributed estimation methods under communication constraints in a distributed version of the nonparametric random design regression model.
An asymptotic analysis of distributed nonparametric methods
We investigate and compare the fundamental performance of several distributed learning methods that have been proposed recently.
