Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Invariant Representations with Missing Data   Spurious correlations allow flexible models to predict well during training
but poorly on related test populations. Recent work has shown that models that
satisfy particular independencies involving correlation-inducing
\textit{nuisance} variables have guarantees on their test performance.
Enforcing such independencies requires nuisances to be observed during
training. However, nuisances, such as demographics or image background labels,
are often missing. Enforcing independence on just the observed data does not
imply independence on the entire population. Here we derive \acrshort{mmd}
estimators used for invariance objectives under missing nuisances. On
simulations and clinical data, optimizing through these estimates achieves test
performance similar to using estimators that make use of the full data.

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The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci