Geodesic
Online algorithm for aggregating experts' predictions with unbounded quadratic loss
Trudy Matematicheskogo Instituta imeni V.A. Steklova, Tome 75 (2020) no. 5, pp. 974-977 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

@article{RM_2020_75_5_a7,
     author = {A. A. Korotin and V. V. V'yugin and E. V. Burnaev},
     title = {Online algorithm for aggregating experts' predictions with unbounded quadratic loss},
     journal = {Trudy Matematicheskogo Instituta imeni V.A. Steklova},
     pages = {974--977},
     year = {2020},
     volume = {75},
     number = {5},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/RM_2020_75_5_a7/}
}
TY  - JOUR
AU  - A. A. Korotin
AU  - V. V. V'yugin
AU  - E. V. Burnaev
TI  - Online algorithm for aggregating experts' predictions with unbounded quadratic loss
JO  - Trudy Matematicheskogo Instituta imeni V.A. Steklova
PY  - 2020
SP  - 974
EP  - 977
VL  - 75
IS  - 5
UR  - http://geodesic.mathdoc.fr/item/RM_2020_75_5_a7/
LA  - en
ID  - RM_2020_75_5_a7
ER  - 
%0 Journal Article
%A A. A. Korotin
%A V. V. V'yugin
%A E. V. Burnaev
%T Online algorithm for aggregating experts' predictions with unbounded quadratic loss
%J Trudy Matematicheskogo Instituta imeni V.A. Steklova
%D 2020
%P 974-977
%V 75
%N 5
%U http://geodesic.mathdoc.fr/item/RM_2020_75_5_a7/
%G en
%F RM_2020_75_5_a7
A. A. Korotin; V. V. V'yugin; E. V. Burnaev. Online algorithm for aggregating experts' predictions with unbounded quadratic loss. Trudy Matematicheskogo Instituta imeni V.A. Steklova, Tome 75 (2020) no. 5, pp. 974-977. http://geodesic.mathdoc.fr/item/RM_2020_75_5_a7/

[1] N. Cesa-Bianchi, G. Lugosi, Prediction, learning, and games, Cambridge Univ. Press, Cambridge, 2006, xii+394 pp. | DOI | MR | Zbl

[2] S. De Rooij, T. van Erven, P. D. Grünwald, W. M. Koolen, J. Mach. Learn. Res., 15 (2014), 1281–1316 | MR | Zbl

[3] E. Hazan, Found. Trends Optim., 2:3-4 (2016), 157–325 | DOI

[4] A. Korotin, V. V'yugin, E. Burnaev, Integral mixability: a tool for efficient online aggregation of functional and probabilistic forecasts, 2020 (v1 – 2019), 22 pp., arXiv: 1912.07048