{"paper":{"title":"Exp-Concavity of Proper Composite Losses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Parameswaran Kamalaruban, Robert C. Williamson, Xinhua Zhang","submitted_at":"2018-05-20T08:53:48Z","abstract_excerpt":"The goal of online prediction with expert advice is to find a decision strategy which will perform almost as well as the best expert in a given pool of experts, on any sequence of outcomes. This problem has been widely studied and $O(\\sqrt{T})$ and $O(\\log{T})$ regret bounds can be achieved for convex losses (\\cite{zinkevich2003online}) and strictly convex losses with bounded first and second derivatives (\\cite{hazan2007logarithmic}) respectively. In special cases like the Aggregating Algorithm (\\cite{vovk1995game}) with mixable losses and the Weighted Average Algorithm (\\cite{kivinen1999avera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.07737","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}