Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Deep Ordinal Regression Under Label Noise   The real-world data is often susceptible to label noise, which might
constrict the effectiveness of the existing state of the art algorithms for
ordinal regression. Existing works on ordinal regression do not take label
noise into account. We propose a theoretically grounded approach for class
conditional label noise in ordinal regression problems. We present a deep
learning implementation of two commonly used loss functions for ordinal
regression that is both - 1) robust to label noise, and 2) rank consistent for
a good ranking rule. We verify these properties of the algorithm empirically
and show robustness to label noise on real data and rank consistency. To the
best of our knowledge, this is the first approach for robust ordinal regression
models.

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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol