Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption

28 October 2019

Papers citing "Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption"

8 / 8 papers shown

Title
Optimal Transfer Learning for Missing Not-at-Random Matrix Completion Akhil Jalan Yassir Jedra Arya Mazumdar Soumendu Sundar Mukherjee Purnamrita Sarkar 126 0 0 28 Feb 2025
Collaborative Imputation of Urban Time Series through Cross-city Meta-learning Tong Nie Wei Ma Jian-jun Sun Yu Yang Jiannong Cao AI4TS AI4CE 36 0 0 20 Jan 2025
Exploiting Observation Bias to Improve Matrix Completion Yassir Jedra Sean Mann Charlotte Park Devavrat Shah 33 1 0 03 Jan 2025
Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions Abhineet Agarwal Anish Agarwal Suhas Vijaykumar CML 11 8 0 24 Mar 2023
Identifiable Generative Models for Missing Not at Random Data Imputation Chao Ma Cheng Zhang 28 33 0 27 Oct 2021
Correcting Exposure Bias for Link Recommendation Shantanu Gupta Hao Wang Zachary Chase Lipton Bernie Wang CML 19 34 0 13 Jun 2021
TenIPS: Inverse Propensity Sampling for Tensor Completion Chengrun Yang Lijun Ding Ziyang Wu Madeleine Udell 22 8 0 01 Jan 2021
Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System Sami Khenissi M. Boujelbene O. Nasraoui 17 23 0 21 Aug 2020