Learning model uncertainty as variance-minimizing instance weights
Predictive uncertainty--a model's self-awareness regarding its accuracy on an input--is key
for both building robust models via training interventions and for test-time applications such …
for both building robust models via training interventions and for test-time applications such …
Near-Optimal Mean Estimation with Unknown, Heteroskedastic Variances
Given data drawn from a collection of Gaussian variables with a common mean but different
and unknown variances, what is the best algorithm for estimating their common mean? We …
and unknown variances, what is the best algorithm for estimating their common mean? We …
Selective classification using a robust meta-learning approach
Predictive uncertainty-a model's self awareness regarding its accuracy on an input-is key for
both building robust models via training interventions and for test-time applications such as …
both building robust models via training interventions and for test-time applications such as …