Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimization by gradient boosting   Gradient boosting is a state-of-the-art prediction technique that
sequentially produces a model in the form of linear combinations of simple
predictors---typically decision trees---by solving an infinite-dimensional
convex optimization problem. We provide in the present paper a thorough
analysis of two widespread versions of gradient boosting, and introduce a
general framework for studying these algorithms from the point of view of
functional optimization. We prove their convergence as the number of iterations
tends to infinity and highlight the importance of having a strongly convex risk
functional to minimize. We also present a reasonable statistical context
ensuring consistency properties of the boosting predictors as the sample size
grows. In our approach, the optimization procedures are run forever (that is,
without resorting to an early stopping strategy), and statistical
regularization is basically achieved via an appropriate $L^2$ penalization of
the loss and strong convexity arguments.

35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph