Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Unbiased Risk Estimator for Learning with Augmented Classes   This paper studies the problem of learning with augmented classes (LAC),
where augmented classes unobserved in the training data might emerge in the
testing phase. Previous studies generally attempt to discover augmented classes
by exploiting geometric properties, achieving inspiring empirical performance
yet lacking theoretical understandings particularly on the generalization
ability. In this paper we show that, by using unlabeled training data to
approximate the potential distribution of augmented classes, an unbiased risk
estimator of the testing distribution can be established for the LAC problem
under mild assumptions, which paves a way to develop a sound approach with
theoretical guarantees. Moreover, the proposed approach can adapt to complex
changing environments where augmented classes may appear and the prior of known
classes may change simultaneously. Extensive experiments confirm the
effectiveness of our proposed approach.

37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat