Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Constructing Confidence Region for Model Parameters in Stochastic
  Gradient Descent via Batch Means   In this paper, we study a simple algorithm to construct asymptotically valid
confidence regions for model parameters using the batch means method. The main
idea is to cancel out the covariance matrix which is hard/costly to estimate.
In the process of developing the algorithm, we establish process-level
functional central limit theorem for Polyak-Ruppert averaging based stochastic
gradient descent estimators. We also extend the batch means method to
accommodate more general batch size specifications.

31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga