Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards optimally abstaining from prediction with OOD test examples   A common challenge across all areas of machine learning is that training data
is not distributed like test data, due to natural shifts, "blind spots," or
adversarial examples; such test examples are referred to as out-of-distribution
(OOD) test examples. We consider a model where one may abstain from predicting,
at a fixed cost. In particular, our transductive abstention algorithm takes
labeled training examples and unlabeled test examples as input, and provides
predictions with optimal prediction loss guarantees. The loss bounds match
standard generalization bounds when test examples are i.i.d. from the training
distribution, but add an additional term that is the cost of abstaining times
the statistical distance between the train and test distribution (or the
fraction of adversarial examples). For linear regression, we give a
polynomial-time algorithm based on Celis-Dennis-Tapia optimization algorithms.
For binary classification, we show how to efficiently implement it using a
proper agnostic learner (i.e., an Empirical Risk Minimizer) for the class of
interest. Our work builds on a recent abstention algorithm of Goldwasser,
Kalais, and Montasser (2020) for transductive binary classification.

31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con