{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T23:02:10Z","timestamp":1709852530761},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2018,8,14]],"date-time":"2018-08-14T00:00:00Z","timestamp":1534204800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["14-50-00150"],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2019,3]]},"DOI":"10.1007\/s10994-018-5751-z","type":"journal-article","created":{"date-parts":[[2018,8,14]],"date-time":"2018-08-14T16:02:06Z","timestamp":1534262526000},"page":"425-444","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Online aggregation of unbounded losses using shifting experts with confidence"],"prefix":"10.1007","volume":"108","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-5336-206X","authenticated-orcid":false,"given":"Vladimir","family":"V\u2019yugin","sequence":"first","affiliation":[]},{"given":"Vladimir","family":"Trunov","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,8,14]]},"reference":[{"key":"5751_CR1","unstructured":"Adamskiy, D., Koolen, W. M., Chernov, A., Vovk, V. (2012). A closer look at adaptive regret. In: N. H. Bshouty, G. Stoltz , N. Vayatis, & T. Zeugmann (Eds.), Algorithmic learning theory. ALT 2012. Lecture notes in Computer Science (Vol. 7568). Berlin, Heidelberg: Springer."},{"key":"5751_CR2","first-page":"1307","volume":"8","author":"A Blum","year":"2007","unstructured":"Blum, A., & Mansour, Y. (2007). From external to internal regret. Journal of Machine Learning Research, 8, 1307\u20131324.","journal-title":"Journal of Machine Learning Research"},{"key":"5751_CR3","first-page":"363","volume":"3","author":"O Bousquet","year":"2002","unstructured":"Bousquet, O., & Warmuth, M. (2002). Tracking a small set of experts by mixing past posteriors. Journal of Machine Learning Research, 3, 363\u2013396.","journal-title":"Journal of Machine Learning Research"},{"key":"5751_CR4","unstructured":"Chernov, A., & Vovk, V. (2009). Prediction with expert evaluators\u2019 advice. In R. Gavald\u00e0, G. Lugosi, T. Zeugmann, & S. Zilles (Eds.), Proceedings of the twentieth international conference on algorithmic learning theory. Lecture notes in computer science (Vol. 5809, pp. 8\u201322). Berlin: Springer."},{"key":"5751_CR5","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511546921","volume-title":"Prediction, learning, and games","author":"N Cesa-Bianchi","year":"2006","unstructured":"Cesa-Bianchi, N., & Lugosi, G. (2006). Prediction, learning, and games. Cambridge: Cambridge University Press."},{"issue":"2\/3","key":"5751_CR6","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/s10994-006-5001-7","volume":"66","author":"N Cesa-Bianchi","year":"2007","unstructured":"Cesa-Bianchi, N., Mansour, Y., & Stoltz, G. (2007). Improved second-order bounds for prediction with expert advice. Machine Learning, 66(2\/3), 321\u2013352.","journal-title":"Machine Learning"},{"key":"5751_CR7","first-page":"1281","volume":"15","author":"S Rooij de","year":"2014","unstructured":"de Rooij, S., van Erven, T., Grunwald, P., & Koolen, W. (2014). Follow the leader. If you can, hedge if you must. Journal of Machine Learning Research, 15, 1281\u20131316.","journal-title":"Journal of Machine Learning Research"},{"issue":"2","key":"5751_CR8","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s10994-012-5314-7","volume":"90","author":"M Devaine","year":"2013","unstructured":"Devaine, M., Gaillard, P., Goude, Y., & Stoltz, G. (2013). Forecasting electricity consumption by aggregating specialized experts. Machine Learning, 90(2), 231\u2013260.","journal-title":"Machine Learning"},{"key":"5751_CR9","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119\u2013139.","journal-title":"Journal of Computer and System Sciences"},{"key":"5751_CR10","doi-asserted-by":"crossref","unstructured":"Freund, Y., Schapire, R. E., Singer, Y., & Warmuth, M. K. (1997). Using and combining predictors that specialize. In Proc. 29th Annual ACM Symposium on Theory of Computing. 334\u2013343.","DOI":"10.1145\/258533.258616"},{"key":"5751_CR11","unstructured":"Gaillard, P., Goude, Y., & Stoltz, G. (2011). A further look at the forecasting of the electricity consumption by aggregation of specialized experts. Technical report. pierre.gaillard.me\/doc\/GaGoSt-report.pdf."},{"key":"5751_CR12","first-page":"176","volume":"35","author":"P Gaillard","year":"2014","unstructured":"Gaillard, P., Stoltz, G., & van Erven, T. (2014). A second-order bound with excess losses. JMLR: Workshop and Conference Proceedings, 35, 176\u2013196.","journal-title":"JMLR: Workshop and Conference Proceedings"},{"issue":"2","key":"5751_CR13","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1023\/A:1007424614876","volume":"32","author":"M Herbster","year":"1998","unstructured":"Herbster, M., & Warmuth, M. (1998). Tracking the best expert. Machine Learning, 32(2), 151\u2013178.","journal-title":"Machine Learning"},{"issue":"2","key":"5751_CR14","doi-asserted-by":"publisher","first-page":"P.357","DOI":"10.1016\/j.ijforecast.2013.07.001","volume":"V.30","author":"T Hong","year":"2014","unstructured":"Hong, T., Pinson, P., & Fan, Shu. (2014). Global energy forecasting competition 2012. International Journal of Forecasting, V.30(2), P.357\u2013363.","journal-title":"International Journal of Forecasting"},{"key":"5751_CR15","doi-asserted-by":"crossref","unstructured":"Kalnishkan, Y., Adamskiy, D., Chernov, A., & Scarfe, T. (2015). Specialist experts for prediction with side information. IEEE international conference on data mining workshop (ICDMW). IEEE, 1470\u20131477.","DOI":"10.1109\/ICDMW.2015.161"},{"key":"5751_CR16","unstructured":"Kivinen, J., & Warmuth, M.K. (1999). Averaging expert prediction. In P. Fisher & H.U. Simon (Eds.), Computational learning theory: 4th european conference (EuroColt \u201999). 153\u2013167, Springer."},{"key":"5751_CR17","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1006\/inco.1994.1009","volume":"108","author":"N Littlestone","year":"1994","unstructured":"Littlestone, N., & Warmuth, M. (1994). The weighted majority algorithm. Information and Computation, 108, 212\u2013261.","journal-title":"Information and Computation"},{"key":"5751_CR18","doi-asserted-by":"crossref","unstructured":"Vovk, V. (1990). Aggregating strategies. In M.\u00a0Fulk and J.\u00a0Case, (Eds.), Proceedings of the 3rd annual workshop on computational learning theory, 371\u2013383, San Mateo, CA, Morgan Kaufmann.","DOI":"10.1016\/B978-1-55860-146-8.50032-1"},{"issue":"2","key":"5751_CR19","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1006\/jcss.1997.1556","volume":"56","author":"V Vovk","year":"1998","unstructured":"Vovk, V. (1998). A game of prediction with expert advice. Journal of Computer and System Sciences, 56(2), 153\u2013173.","journal-title":"Journal of Computer and System Sciences"},{"issue":"3","key":"5751_CR20","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1023\/A:1007595032382","volume":"35","author":"V Vovk","year":"1999","unstructured":"Vovk, V. (1999). Derandomizing stochastic prediction strategies. Machine Learning, 35(3), 247\u2013282.","journal-title":"Machine Learning"},{"key":"5751_CR21","unstructured":"V\u2019yugin, V. (2017). Online aggregation of unbounded signed losses using shifting experts. Proceedings of machine learning research. 60: 1\u201315. \n http:\/\/proceedings.mlr.press\/v60\/"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-018-5751-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-018-5751-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-018-5751-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,2]],"date-time":"2019-10-02T03:29:33Z","timestamp":1569986973000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-018-5751-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,14]]},"references-count":21,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,3]]}},"alternative-id":["5751"],"URL":"https:\/\/doi.org\/10.1007\/s10994-018-5751-z","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,14]]},"assertion":[{"value":"28 January 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}