Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Boosted Density Estimation Remastered   There has recently been a steady increase in the number iterative approaches
to density estimation. However, an accompanying burst of formal convergence
guarantees has not followed; all results pay the price of heavy assumptions
which are often unrealistic or hard to check. The Generative Adversarial
Network (GAN) literature --- seemingly orthogonal to the aforementioned pursuit
--- has had the side effect of a renewed interest in variational divergence
minimisation (notably $f$-GAN). We show that by introducing a weak learning
assumption (in the sense of the classical boosting framework) we are able to
import some recent results from the GAN literature to develop an iterative
boosted density estimation algorithm, including formal convergence results with
rates, that does not suffer the shortcomings other approaches. We show that the
density fit is an exponential family, and as part of our analysis obtain an
improved variational characterisation of $f$-GAN.

11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph