2018/06/06 by Carlo Ciliberto, Francis Bach, Ciliberto, Carlo +3
Computer Science · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.1806.02402
openalex publication_date 2018/06/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Key to structured prediction is exploiting the problem structure to simplify\nthe learning process. A major challenge arises when data exhibit a local\nstructure (e.g., are made by "parts") that can be leveraged to better\napproximate the relation between (parts of) the input and (parts of) the\noutput. Recent literature on signal processing, and in particular computer\nvision, has shown that capturing these aspects is indeed essential to achieve\nstate-of-the-art performance. While such algorithms are typically derived on a\ncase-by-case basis, in this work we propose the first theoretical framework to\ndeal with part-based data from a general perspective. We derive a novel\napproach to deal with these problems and study its generalization properties\nwithin the setting of statistical learning theory. Our analysis is novel in\nthat it explicitly quantifies the benefits of leveraging the part-based\nstructure of the problem with respect to the learning rates of the proposed\nestimator.\n