Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal non-asymptotic bound of the Ruppert-Polyak averaging without
  strong convexity   This paper is devoted to the non-asymptotic control of the mean-squared error
for the Ruppert-Polyak stochastic averaged gradient descent introduced in the
seminal contributions of [Rup88] and [PJ92]. In our main results, we establish
non-asymptotic tight bounds (optimal with respect to the Cramer-Rao lower
bound) in a very general framework that includes the uniformly strongly convex
case as well as the one where the function f to be minimized satisfies a weaker
Kurdyka-Lojiasewicz-type condition [Loj63, Kur98]. In particular, it makes it
possible to recover some pathological examples such as on-line learning for
logistic regression (see [Bac14]) and recursive quan- tile estimation (an even
non-convex situation).

23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con