Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised model-free representation learning   Numerous control and learning problems face the situation where sequences of
high-dimensional highly dependent data are available but no or little feedback
is provided to the learner, which makes any inference rather challenging. To
address this challenge, we formulate the following problem. Given a series of
observations $X_0,\dots,X_n$ coming from a large (high-dimensional) space
$\mathcal X$, find a representation function $f$ mapping $\mathcal X$ to a
finite space $\mathcal Y$ such that the series $f(X_0),\dots,f(X_n)$ preserves
as much information as possible about the original time-series dependence in
$X_0,\dots,X_n$. We show that, for stationary time series, the function $f$ can
be selected as the one maximizing a certain information criterion that we call
time-series information. Some properties of this functions are investigated,
including its uniqueness and consistency of its empirical estimates.
  Implications for the problem of optimal control are presented.

8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph