@inproceedings{32314ab71793405585324ea59c30b234,
title = "Structural online learning",
abstract = "We study the problem of learning ensembles in the online setting, when the hypotheses are selected out of a base family that may be a union of possibly very complex sub-families. We prove new theoretical guarantees for the online learning of such ensembles in terms of the sequential Rademacher complexities of these sub-families. We also describe an algorithm that benefits from such guarantees. We further extend our framework by proving new structural estimation error guarantees for ensembles in the batch setting through a new data-dependent online-to-batch conversion technique, thereby also devising an effective algorithm for the batch setting which does not require the estimation of the Rademacher complexities of base sub-families.",
author = "Mehryar Mohri and Scott Yang",
note = "Funding Information: This work was partly funded by the NSF awards IIS-1117591 and CCF-1535987 and was also supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE 1342536. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 27th International Conference on Algorithmic Learning Theory, ALT 2016 ; Conference date: 19-10-2016 Through 21-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46379-7_15",
language = "English (US)",
isbn = "9783319463780",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "223--237",
editor = "Simon, {Hans Ulrich} and Sandra Zilles and Ronald Ortner",
booktitle = "Algorithmic Learning Theory - 27th International Conference, ALT 2016, Proceedings",
}