Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Making learning more transparent using conformalized performance
  prediction   In this work, we study some novel applications of conformal inference
techniques to the problem of providing machine learning procedures with more
transparent, accurate, and practical performance guarantees. We provide a
natural extension of the traditional conformal prediction framework, done in
such a way that we can make valid and well-calibrated predictive statements
about the future performance of arbitrary learning algorithms, when passed an
as-yet unseen training set. In addition, we include some nascent empirical
examples to illustrate potential applications.

22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
9
The categories of this paper are: nlin