Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discriminative Learning of Prediction Intervals   In this work we consider the task of constructing prediction intervals in an
inductive batch setting. We present a discriminative learning framework which
optimizes the expected error rate under a budget constraint on the interval
sizes. Most current methods for constructing prediction intervals offer
guarantees for a single new test point. Applying these methods to multiple test
points can result in a high computational overhead and degraded statistical
guarantees. By focusing on expected errors, our method allows for variability
in the per-example conditional error rates. As we demonstrate both analytically
and empirically, this flexibility can increase the overall accuracy, or
alternatively, reduce the average interval size.
  While the problem we consider is of a regressive flavor, the loss we use is
combinatorial. This allows us to provide PAC-style, finite-sample guarantees.
Computationally, we show that our original objective is NP-hard, and suggest a
tractable convex surrogate. We conclude with a series of experimental
evaluations.

27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ