Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Beating Stochastic and Adversarial Semi-bandits Optimally and
  Simultaneously   We develop the first general semi-bandit algorithm that simultaneously
achieves $\mathcal{O}(\log T)$ regret for stochastic environments and
$\mathcal{O}(\sqrt{T})$ regret for adversarial environments without knowledge
of the regime or the number of rounds $T$. The leading problem-dependent
constants of our bounds are not only optimal in some worst-case sense studied
previously, but also optimal for two concrete instances of semi-bandit
problems. Our algorithm and analysis extend the recent work of (Zimmert &
Seldin, 2019) for the special case of multi-armed bandit, but importantly
requires a novel hybrid regularizer designed specifically for semi-bandit.
Experimental results on synthetic data show that our algorithm indeed performs
well uniformly over different environments. We finally provide a preliminary
extension of our results to the full bandit feedback.

7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess