Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bridging the Generalization Gap: Training Robust Models on Confounded
  Biological Data   Statistical learning on biological data can be challenging due to confounding
variables in sample collection and processing. Confounders can cause models to
generalize poorly and result in inaccurate prediction performance metrics if
models are not validated thoroughly. In this paper, we propose methods to
control for confounding factors and further improve prediction performance. We
introduce OrthoNormal basis construction In cOnfounding factor Normalization
(ONION) to remove confounding covariates and use the Domain-Adversarial Neural
Network (DANN) to penalize models for encoding confounder information. We apply
the proposed methods to simulated and empirical patient data and show
significant improvements in generalization.

17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics