no code implementations • 28 Aug 2015 • Matthias Bussas, Christoph Sawade, Tobias Scheffer, Niels Landwehr
We study learning problems in which the conditional distribution of the output given the input varies as a function of additional task variables.
no code implementations • NeurIPS 2012 • Christoph Sawade, Niels Landwehr, Tobias Scheffer
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.
no code implementations • NeurIPS 2010 • Christoph Sawade, Niels Landwehr, Tobias Scheffer
We address the problem of estimating the F-measure of a given model as accurately as possible on a fixed labeling budget.
no code implementations • NeurIPS 2008 • Steffen Bickel, Christoph Sawade, Tobias Scheffer
We address the problem of learning classifiers for several related tasks that may differ in their joint distribution of input and output variables.