2018/05/21 by Steve Hanneke, Hanneke, Steve, Aryeh Kontorovich +1
Computer Science · #Algorithms and Data Compression #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.1805.08140
openalex publication_date 2018/05/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We establish a tight characterization of the worst-case rates for the excess risk of agnostic learning with sample compression schemes and for uniform convergence for agnostic sample compression schemes. In particular, we find that the optimal rates of convergence for size-k agnostic sample compression schemes are of the form √((k log(n/k))/(n)), which contrasts with agnostic learning with classes of VC dimension k, where the optimal rates are of the form √((k)/(n)).