Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Regularization Strategies for Quantile Regression   We investigate different methods for regularizing quantile regression when
predicting either a subset of quantiles or the full inverse CDF. We show that
minimizing an expected pinball loss over a continuous distribution of quantiles
is a good regularizer even when only predicting a specific quantile. For
predicting multiple quantiles, we propose achieving the classic goal of
non-crossing quantiles by using deep lattice networks that treat the quantile
as a monotonic input feature, and we discuss why monotonicity on other features
is an apt regularizer for quantile regression. We show that lattice models
enable regularizing the predicted distribution to a location-scale family.
Lastly, we propose applying rate constraints to improve the calibration of the
quantile predictions on specific subsets of interest and improve fairness
metrics. We demonstrate our contributions on simulations, benchmark datasets,
and real quantile regression problems.

23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin