Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hard Shape-Constrained Kernel Machines   Shape constraints (such as non-negativity, monotonicity, convexity) play a
central role in a large number of applications, as they usually improve
performance for small sample size and help interpretability. However enforcing
these shape requirements in a hard fashion is an extremely challenging problem.
Classically, this task is tackled (i) in a soft way (without out-of-sample
guarantees), (ii) by specialized transformation of the variables on a
case-by-case basis, or (iii) by using highly restricted function classes, such
as polynomials or polynomial splines. In this paper, we prove that hard affine
shape constraints on function derivatives can be encoded in kernel machines
which represent one of the most flexible and powerful tools in machine learning
and statistics. Particularly, we present a tightened second-order cone
constrained reformulation, that can be readily implemented in convex solvers.
We prove performance guarantees on the solution, and demonstrate the efficiency
of the approach in joint quantile regression with applications to economics and
to the analysis of aircraft trajectories, among others.

17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics