2014/09/25 by Tofigh Naghibi, Naghibi, Tofigh, Beat Pfister +1
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Optimization and Search Problems #cs.LG
paper · pdf · doi:10.48550/arxiv.1409.7202
Accepted in NIPS 2014
openalex publication_date 2014/09/25 · arxiv created 2014/11/23 · arxiv updated 2014/11/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
By exploiting the duality between boosting and online learning, we present a boosting framework which proves to be extremely powerful thanks to employing the vast knowledge available in the online learning area. Using this framework, we develop various algorithms to address multiple practically and theoretically interesting questions including sparse boosting, smooth-distribution boosting, agnostic learning and some generalization to double-projection online learning algorithms, as a by-product.