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Varying-coefficient models with isotropic Gaussian process priors
We study learning problems in which the conditional distribution of the output given the input varies as a function of additional task variables.
Active Comparison of Prediction Models
We address the problem of comparing the risks of two given predictive models - for instance, a baseline model and a challenger - as confidently as possible on a fixed labeling budget.
Active Estimation of F-Measures
We address the problem of estimating the F-measure of a given model as accurately as possible on a fixed labeling budget.
Transfer Learning by Distribution Matching for Targeted Advertising
We address the problem of learning classifiers for several related tasks that may differ in their joint distribution of input and output variables.
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