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stat.ML2021★ 1 cited
TenIPS: Inverse Propensity Sampling for Tensor Completion
Chengrun Yang, Lijun Ding, Ziyang Wu +1
Tensors are widely used to represent multiway arrays of data. The recovery of missing entries in a tensor has been extensively studied, generally under the assumption that entries…
stat.ML2020
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula
Yuxuan Zhao, Madeleine Udell
Modern large scale datasets are often plagued with missing entries. For tabular data with missing values, a flurry of imputation algorithms solve for a complete matrix which minimi…
stat.ML2018
Causal Inference with Noisy and Missing Covariates via Matrix Factorization
Nathan Kallus, Xiaojie Mao, Madeleine Udell
Valid causal inference in observational studies often requires controlling for confounders. However, in practice measurements of confounders may be noisy, and can lead to biased es…