Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adapting to Mixing Time in Stochastic Optimization with Markovian Data   We consider stochastic optimization problems where data is drawn from a
Markov chain. Existing methods for this setting crucially rely on knowing the
mixing time of the chain, which in real-world applications is usually unknown.
We propose the first optimization method that does not require the knowledge of
the mixing time, yet obtains the optimal asymptotic convergence rate when
applied to convex problems. We further show that our approach can be extended
to: (i) finding stationary points in non-convex optimization with Markovian
data, and (ii) obtaining better dependence on the mixing time in temporal
difference (TD) learning; in both cases, our method is completely oblivious to
the mixing time. Our method relies on a novel combination of multi-level Monte
Carlo (MLMC) gradient estimation together with an adaptive learning method.

22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th