Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Probabilistic Modeling with Bayesian Data Reweighting   Probabilistic models analyze data by relying on a set of assumptions. Data
that exhibit deviations from these assumptions can undermine inference and
prediction quality. Robust models offer protection against mismatch between a
model's assumptions and reality. We propose a way to systematically detect and
mitigate mismatch of a large class of probabilistic models. The idea is to
raise the likelihood of each observation to a weight and then to infer both the
latent variables and the weights from data. Inferring the weights allows a
model to identify observations that match its assumptions and down-weight
others. This enables robust inference and improves predictive accuracy. We
study four different forms of mismatch with reality, ranging from missing
latent groups to structure misspecification. A Poisson factorization analysis
of the Movielens 1M dataset shows the benefits of this approach in a practical
scenario.

37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio