Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Thompson Sampling for Unimodal Bandits   In this paper, we propose a Thompson Sampling algorithm for \emph{unimodal}
bandits, where the expected reward is unimodal over the partially ordered arms.
To exploit the unimodal structure better, at each step, instead of exploration
from the entire decision space, our algorithm makes decision according to
posterior distribution only in the neighborhood of the arm that has the highest
empirical mean estimate. We theoretically prove that, for Bernoulli rewards,
the regret of our algorithm reaches the lower bound of unimodal bandits, thus
it is asymptotically optimal. For Gaussian rewards, the regret of our algorithm
is $\mathcal{O}(\log T)$, which is far better than standard Thompson Sampling
algorithms. Extensive experiments demonstrate the effectiveness of the proposed
algorithm on both synthetic data sets and the real-world applications.

10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom