Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Collective Loss Function for Positive and Unlabeled Learning   People learn to discriminate between classes without explicit exposure to
negative examples. On the contrary, traditional machine learning algorithms
often rely on negative examples, otherwise the model would be prone to collapse
and always-true predictions. Therefore, it is crucial to design the learning
objective which leads the model to converge and to perform predictions
unbiasedly without explicit negative signals. In this paper, we propose a
Collectively loss function to learn from only Positive and Unlabeled data
(cPU). We theoretically elicit the loss function from the setting of PU
learning. We perform intensive experiments on the benchmark and real-world
datasets. The results show that cPU consistently outperforms the current
state-of-the-art PU learning methods.

23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat