Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploiting the Statistics of Learning and Inference   When dealing with datasets containing a billion instances or with simulations
that require a supercomputer to execute, computational resources become part of
the equation. We can improve the efficiency of learning and inference by
exploiting their inherent statistical nature. We propose algorithms that
exploit the redundancy of data relative to a model by subsampling data-cases
for every update and reasoning about the uncertainty created in this process.
In the context of learning we propose to test for the probability that a
stochastically estimated gradient points more than 180 degrees in the wrong
direction. In the context of MCMC sampling we use stochastic gradients to
improve the efficiency of MCMC updates, and hypothesis tests based on adaptive
mini-batches to decide whether to accept or reject a proposed parameter update.
Finally, we argue that in the context of likelihood free MCMC one needs to
store all the information revealed by all simulations, for instance in a
Gaussian process. We conclude that Bayesian methods will remain to play a
crucial role in the era of big data and big simulations, but only if we
overcome a number of computational challenges.

36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas