Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Integral Mixability: a Tool for Efficient Online Aggregation of
  Functional and Probabilistic Forecasts   In this paper we extend the setting of the online prediction with expert
advice to function-valued forecasts. At each step of the online game several
experts predict a function, and the learner has to efficiently aggregate these
functional forecasts into a single forecast. We adapt basic mixable (and
exponentially concave) loss functions to compare functional predictions and
prove that these adaptations are also mixable (exp-concave). We call this
phenomena integral mixability (exp-concavity). As an application of our main
result, we prove that various loss functions used for probabilistic forecasting
are mixable (exp-concave). The considered losses include Sliced Continuous
Ranking Probability Score, Energy-Based Distance, Optimal Transport Costs &
Sliced Wasserstein-2 distance, Beta-2 & Kullback-Leibler divergences,
Characteristic function and Maximum Mean Discrepancies.

8
The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
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The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
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The categories of this paper are: hep-lat
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The categories of this paper are: chao-dyn