Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian leave-one-out cross-validation for large data   Model inference, such as model comparison, model checking, and model
selection, is an important part of model development. Leave-one-out
cross-validation (LOO) is a general approach for assessing the generalizability
of a model, but unfortunately, LOO does not scale well to large datasets. We
propose a combination of using approximate inference techniques and
probability-proportional-to-size-sampling (PPS) for fast LOO model evaluation
for large datasets. We provide both theoretical and empirical results showing
good properties for large data.

37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc