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Weak Aggregating Algorithm for prediction with expert advice and adversarial bandit frameworks

Yuri Kalnishkan

Information and Computation2026https://doi.org/10.1016/j.ic.2026.105447article
ABDC B
Weight
0.50

What the paper says

This paper surveys the Weak Aggregating Algorithm for prediction with expert advice under bounded convex loss functions. It shows how bounds for various scenarios can be obtained and explicates the connection with the adversarial bandit framework. The paper is aimed at practitioners wishing to apply the algorithms in real-life situations.

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https://doi.org/https://doi.org/10.1016/j.ic.2026.105447

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@article{yuri2026,
  title        = {{Weak Aggregating Algorithm for prediction with expert advice and adversarial bandit frameworks}},
  author       = {Yuri Kalnishkan},
  journal      = {Information and Computation},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.ic.2026.105447},
}

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Weak Aggregating Algorithm for prediction with expert advice and adversarial bandit frameworks

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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