ICML 2025

CACTI: Leveraging Copy Masking and Contextual Information to Improve
Tabular Data Imputation
Aditya Gorla 1 2 Ryan Wang 3 Zhengtong Liu 3 Ulzee An 1 3 Sriram Sankararaman 1 3 4
Abstract
We present CACTI, a masked autoencoding ap-
proach for imputing tabular data that leverages
the structure in missingness patterns and contex-
tual information. Our approach employs a novel
median truncated copy masking training strategy
that encourages the model to learn from empirical
patterns of missingness while incorporating se-
mantic relationships between features – captured
by column names and text descriptions – to better
represent feature dependence. These dual sources
of inductive bias enable CACTI to outperform
state-of-the-art methods – an average R2 gain of
7.8% over the next best method (13.4%, 6.1%,
and 5.3% under missing not at random, at ran-
dom and completely at random, respectively) –
across a diverse range of datasets and missing-
ness conditions. Our results highlight the value
of leveraging dataset-specific contextual informa-
tion and missingness patterns to enhance imputa-
tion performance. Code is publicly available at
github.com/sriramlab/CACTI
1. Introduction
Missingness is a pervasive problem in real-world tabular
datasets with the potential to adversely affect downstream
inferential tasks (Rubin, 1987; Schafer & Graham, 2002).
While many techniques to estimate or impute missing en-
tries have been proposed (see Section 2.1), missing data
imputation remains a challenging problem.
A primary reason underlying this challenge is that missing-
1Department of Computational Medicine, David Geffen School
of Medicine, UCLA, Los Angeles, CA, USA 2Bioinformatics
Interdepartmental Program, UCLA, Los Angeles, CA, USA
3Department of Computer Science, UCLA, Los Angeles, CA,
USA 4Department of Human Genetics, UCLA, Los Angeles, CA,
USA. Correspondence to: Aditya Gorla <adityagorla@ucla.edu>,
Sriram Sankararaman <sriram@cs.ucla.edu>.
Proceedings of the 42 nd International...