Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncertainty Characteristics Curves: A Systematic Assessment of
  Prediction Intervals   Accurate quantification of model uncertainty has long been recognized as a
fundamental requirement for trusted AI. In regression tasks, uncertainty is
typically quantified using prediction intervals calibrated to a specific
operating point, making evaluation and comparison across different studies
difficult. Our work leverages: (1) the concept of operating characteristics
curves and (2) the notion of a gain over a simple reference, to derive a novel
operating point agnostic assessment methodology for prediction intervals. The
paper describes the corresponding algorithm, provides a theoretical analysis,
and demonstrates its utility in multiple scenarios. We argue that the proposed
method addresses the current need for comprehensive assessment of prediction
intervals and thus represents a valuable addition to the uncertainty
quantification toolbox.

22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol