Fine-grained generalization analysis of inductive matrix completion

• Main Authors: LEDENT, Antoine, ALVES, RODRIGO, LEI, Yunwen, KLOFT, Marius
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Matrix Completion; Recommender Systems; Distribution-sensitive Learning; Statistical Learning Theory; Nuclear Norm; Databases and Information Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/7201; https://ink.library.smu.edu.sg/context/sis_research/article/8204/viewcontent/IMC_NeurIPS_2021.pdf
• Institution: Singapore Management University

In this paper, we bridge the gap between the state-of-the-art theoretical results for matrix completion with the nuclear norm and their equivalent in \textit{inductive matrix completion}: (1) In the distribution-free setting, we prove bounds improving the previously best scaling of \widetilde{O}(rd2...