Before revision:  We propose a novel mechanism for obtaining these calibrated, uncertainty-aware classifiers based on an amortized technique for computing the normalized maximum likelihood (NML) distribution , also showing how these techniques can be made computationally tractableby leveraging tools from meta-learning.
After revision:  We propose a novel mechanism for obtaining these calibrated, uncertainty-aware classifiers based on an amortized technique for computing the normalized maximum likelihood (NML) distribution . To make this tractable, we propose a novel method for computing the NML distribution by using meta-learning.