Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convergence Rates of Variational Inference in Sparse Deep Learning   Variational inference is becoming more and more popular for approximating
intractable posterior distributions in Bayesian statistics and machine
learning. Meanwhile, a few recent works have provided theoretical justification
and new insights on deep neural networks for estimating smooth functions in
usual settings such as nonparametric regression. In this paper, we show that
variational inference for sparse deep learning retains the same generalization
properties than exact Bayesian inference. In particular, we highlight the
connection between estimation and approximation theories via the classical
bias-variance trade-off and show that it leads to near-minimax rates of
convergence for H\"older smooth functions. Additionally, we show that the model
selection framework over the neural network architecture via ELBO maximization
does not overfit and adaptively achieves the optimal rate of convergence.

34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph