Generalization bounds for inductive matrix completion in low-noise settings

• Main Authors: LEDENT, Antoine, ALVES, Rodrigo, LEI, Yunwen, GUERMEUR, Yann, KLOFT, Marius
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Recommender Systems; Matrix Completion; Learning Theory; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/7951; https://ink.library.smu.edu.sg/context/sis_research/article/8954/viewcontent/26018_Article_Text_30081_1_2_20230626.pdf
• Institution: Singapore Management University

We study inductive matrix completion (matrix completion with side information) under an i.i.d. subgaussian noise assumption at a low noise regime, with uniform sampling of the entries. We obtain for the first time generalization bounds with the following three properties: (1) they scale like the sta...