5 papers
MatrixFormer: A Foundation Model for Matrix Completion
Dwaipayan Saha, Jacob Feitelberg, Kyuseong Choi +2
Matrix completion underlies problems from tabular imputation to causal inference, yet existing tabular foundation models treat it as entry-by-entry prediction, repeating context fo…
One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Data
Jacob Feitelberg, Dwaipayan Saha, Kyuseong Choi +3
Missing data in tabular datasets forces practitioners into a hard choice: deploy a general-purpose imputer that may perform poorly for the problem at hand, or wait for someone to d…
N: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
Caleb Chin, Aashish Khubchandani, Harshvardhan Maskara +8
Nearest neighbor (NN) methods have re-emerged as competitive tools for matrix completion, offering strong empirical performance and recent theoretical guarantees, including entry-w…
Learning Counterfactual Distributions via Kernel Nearest Neighbors
Kyuseong Choi, Jacob Feitelberg, Caleb Chin +2
Consider a setting with multiple units (e.g., individuals, cohorts, geographic locations) and outcomes (e.g., treatments, times, items), where the goal is to learn a multivariate d…
Distributional Matrix Completion via Nearest Neighbors in the Wasserstein Space
Jacob Feitelberg, Kyuseong Choi, Anish Agarwal +1
We study the problem of distributional matrix completion: Given a sparsely observed matrix of empirical distributions, we seek to impute the true distributions associated with both…