Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Post-hoc loss-calibration for Bayesian neural networks   Bayesian decision theory provides an elegant framework for acting optimally
under uncertainty when tractable posterior distributions are available. Modern
Bayesian models, however, typically involve intractable posteriors that are
approximated with, potentially crude, surrogates. This difficulty has
engendered loss-calibrated techniques that aim to learn posterior
approximations that favor high-utility decisions. In this paper, focusing on
Bayesian neural networks, we develop methods for correcting approximate
posterior predictive distributions encouraging them to prefer high-utility
decisions. In contrast to previous work, our approach is agnostic to the choice
of the approximate inference algorithm, allows for efficient test time decision
making through amortization, and empirically produces higher quality decisions.
We demonstrate the effectiveness of our approach through controlled experiments
spanning a diversity of tasks and datasets.

13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn