Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual
  Bandits   We study the problem of dynamic batch learning in high-dimensional sparse
linear contextual bandits, where a decision maker can only adapt decisions at a
batch level. In particular, the decision maker, only observing rewards at the
end of each batch, dynamically decides how many individuals to include in the
next batch (at the current batch's end) and what personalized action-selection
scheme to adopt within the batch. Such batch constraints are ubiquitous in a
variety of practical contexts, including personalized product offerings in
marketing and medical treatment selection in clinical trials. We characterize
the fundamental learning limit in this problem via a novel lower bound analysis
and provide a simple, exploration-free algorithm that uses the LASSO estimator,
which achieves the minimax optimal performance characterized by the lower bound
(up to log factors). To our best knowledge, our work provides the first inroad
into a rigorous understanding of dynamic batch learning with high-dimensional
covariates. We also demonstrate the efficacy of our algorithm on both synthetic
data and the Warfarin medical dosing data. The empirical results show that with
three batches (hence only two opportunities to adapt), our algorithm already
performs comparably (in terms of statistical performance) to the
state-of-the-art fully online high-dimensional linear contextual bandits
algorithm. As an added bonus, since our algorithm operates in batches, it is
orders of magnitudes faster than fully online learning algorithms. As such, our
algorithm provides a desirable candidate for practical data-driven personalized
decision making problems, where limited adaptivity is often a hard constraint.

32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th