Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalized Huber Loss for Robust Learning and its Efficient
  Minimization for a Robust Statistics   We propose a generalized formulation of the Huber loss. We show that with a
suitable function of choice, specifically the log-exp transform; we can achieve
a loss function which combines the desirable properties of both the absolute
and the quadratic loss. We provide an algorithm to find the minimizer of such
loss functions and show that finding a centralizing metric is not that much
harder than the traditional mean and median.

37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys