Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Non-strongly-convex smooth stochastic approximation with convergence
  rate O(1/n)   We consider the stochastic approximation problem where a convex function has
to be minimized, given only the knowledge of unbiased estimates of its
gradients at certain points, a framework which includes machine learning
methods based on the minimization of the empirical risk. We focus on problems
without strong convexity, for which all previously known algorithms achieve a
convergence rate for function values of O(1/n^{1/2}). We consider and analyze
two algorithms that achieve a rate of O(1/n) for classical supervised learning
problems. For least-squares regression, we show that averaged stochastic
gradient descent with constant step-size achieves the desired rate. For
logistic regression, this is achieved by a simple novel stochastic gradient
algorithm that (a) constructs successive local quadratic approximations of the
loss functions, while (b) preserving the same running time complexity as
stochastic gradient descent. For these algorithms, we provide a non-asymptotic
analysis of the generalization error (in expectation, and also in high
probability for least-squares), and run extensive experiments on standard
machine learning benchmarks showing that they often outperform existing
approaches.

33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom