Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online Learning with Optimism and Delay   Inspired by the demands of real-time climate and weather forecasting, we
develop optimistic online learning algorithms that require no parameter tuning
and have optimal regret guarantees under delayed feedback. Our algorithms --
DORM, DORM+, and AdaHedgeD -- arise from a novel reduction of delayed online
learning to optimistic online learning that reveals how optimistic hints can
mitigate the regret penalty caused by delay. We pair this delay-as-optimism
perspective with a new analysis of optimistic learning that exposes its
robustness to hinting errors and a new meta-algorithm for learning effective
hinting strategies in the presence of delay. We conclude by benchmarking our
algorithms on four subseasonal climate forecasting tasks, demonstrating low
regret relative to state-of-the-art forecasting models.

19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc