6 papers
Semi-supervised Gaussian mixture modelling with a missing-data mechanism in R
Ziyang Lyu, Daniel Ahfock, Ryan Thompson +1
Semi-supervised learning is being extensively applied to estimate classifiers from training data in which not all the labels of the feature vectors are available. We present gmmssl…
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
Ryan Thompson, Amir Dezfouli, Robert Kohn
Sparse linear models are one of several core tools for interpretable machine learning, a field of emerging importance as predictive models permeate decision-making in many domains.…
Flexible global forecast combinations
Ryan Thompson, Yilin Qian, Andrey L. Vasnev
Forecast combination -- the aggregation of individual forecasts from multiple experts or models -- is a proven approach to economic forecasting. To date, research on economic forec…
Familial inference: tests for hypotheses on a family of centres
Ryan Thompson, Catherine S. Forbes, Steven N. MacEachern +1
Statistical hypotheses are translations of scientific hypotheses into statements about one or more distributions, often concerning their centre. Tests that assess statistical hypot…
Group selection and shrinkage: Structured sparsity for semiparametric additive models
Ryan Thompson, Farshid Vahid
Sparse regression and classification estimators that respect group structures have application to an assortment of statistical and machine learning problems, from multitask learnin…
Robust subset selection
Ryan Thompson
The best subset selection (or "best subsets") estimator is a classic tool for sparse regression, and developments in mathematical optimization over the past decade have made it mor…