Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning the Truth From Only One Side of the Story   Learning under one-sided feedback (i.e., where we only observe the labels for
examples we predicted positively on) is a fundamental problem in machine
learning -- applications include lending and recommendation systems. Despite
this, there has been surprisingly little progress made in ways to mitigate the
effects of the sampling bias that arises. We focus on generalized linear models
and show that without adjusting for this sampling bias, the model may converge
suboptimally or even fail to converge to the optimal solution. We propose an
adaptive approach that comes with theoretical guarantees and show that it
outperforms several existing methods empirically. Our method leverages variance
estimation techniques to efficiently learn under uncertainty, offering a more
principled alternative compared to existing approaches.

27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org