Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fair Normalizing Flows   Fair representation learning is an attractive approach that promises fairness
of downstream predictors by encoding sensitive data. Unfortunately, recent work
has shown that strong adversarial predictors can still exhibit unfairness by
recovering sensitive attributes from these representations. In this work, we
present Fair Normalizing Flows (FNF), a new approach offering more rigorous
fairness guarantees for learned representations. Specifically, we consider a
practical setting where we can estimate the probability density for sensitive
groups. The key idea is to model the encoder as a normalizing flow trained to
minimize the statistical distance between the latent representations of
different groups. The main advantage of FNF is that its exact likelihood
computation allows us to obtain guarantees on the maximum unfairness of any
potentially adversarial downstream predictor. We experimentally demonstrate the
effectiveness of FNF in enforcing various group fairness notions, as well as
other attractive properties such as interpretability and transfer learning, on
a variety of challenging real-world datasets.

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The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: eess
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an