:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yu, Jiahao, Ying, Qizhen, Wang, Leyang, Jiang, Ziyue, Liu, Song
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2505.11749
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching
by: Williams, Daniel J., et al.
Published: (2024)

CFMI: Flow Matching for Missing Data Imputation
by: Simkus, Vaidotas, et al.
Published: (2025)

RFMI: Estimating Mutual Information on Rectified Flow for Text-to-Image Alignment
by: Wang, Chao, et al.
Published: (2025)

Zero-Flow Encoders
by: Wang, Yakun, et al.
Published: (2026)

Deep Generative Imputation Model for Missing Not At Random Data
by: Chen, Jialei, et al.
Published: (2023)

DiffPuter: Empowering Diffusion Models for Missing Data Imputation
by: Zhang, Hengrui, et al.
Published: (2024)

Missing Data Imputation using Neural Cellular Automata
by: Luu, Tin, et al.
Published: (2025)

Distribution Shift in Missing Data Imputation: A Risk-Based Perspective and Importance-Weighted Correction under MAR
by: Shannon, Luke, et al.
Published: (2026)

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion
by: He, Wenying, et al.
Published: (2025)

An Interdisciplinary and Cross-Task Review on Missing Data Imputation
by: Fan, Jicong
Published: (2025)

Efficient Imputation for Patch-based Missing Single-cell Data via Cluster-regularized Optimal Transport
by: Liu, Yuyu, et al.
Published: (2026)

Machine Learning for Missing Value Imputation
by: Ahmad, Abu Fuad, et al.
Published: (2024)

Generative Conditional Missing Imputation Networks
by: Sun, George, et al.
Published: (2026)

Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Random
by: Sim, Gyuwon, et al.
Published: (2026)

DPGAN: A Dual-Path Generative Adversarial Network for Missing Data Imputation in Graphs
by: Zheng, Xindi, et al.
Published: (2024)

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold
by: Liu, Song, et al.
Published: (2025)

Rethinking the Diffusion Models for Numerical Tabular Data Imputation from the Perspective of Wasserstein Gradient Flow
by: Chen, Zhichao, et al.
Published: (2024)

Evaluation of Missing Data Imputation for Time Series Without Ground Truth
by: Farjallah, Rania, et al.
Published: (2025)

Deep Survival Analysis for Competing Risk Modeling with Functional Covariates and Missing Data Imputation
by: Gao, Penglei, et al.
Published: (2025)

Recursive Equations For Imputation Of Missing Not At Random Data With Sparse Pattern Support
by: Phung, Trung, et al.
Published: (2025)

Pavement Missing Condition Data Imputation through Collective Learning-Based Graph Neural Networks
by: Yu, Ke, et al.
Published: (2026)

Longitudinal Missing Data Imputation for Predicting Disability Stage of Patients with Multiple Sclerosis
by: Vazifehdan, Mahin, et al.
Published: (2025)

Nonparametric End-to-End Probabilistic Forecasting of Distributed Generation Outputs Considering Missing Data Imputation
by: Chen, Minghui, et al.
Published: (2024)

Missing Data Multiple Imputation for Tabular Q-Learning in Online RL
by: Chasalow, Kyla, et al.
Published: (2025)

RefiDiff: Progressive Refinement Diffusion for Efficient Missing Data Imputation
by: Ahamed, Md Atik, et al.
Published: (2025)

MissHDD: Hybrid Deterministic Diffusion for Hetrogeneous Incomplete Data Imputation
by: Zhou, Youran, et al.
Published: (2025)

ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Data
by: Li, Mengxuan, et al.
Published: (2025)

Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking
by: Chib, Pranav Singh, et al.
Published: (2024)

Simple Imputation Rules for Prediction with Missing Data: Contrasting Theoretical Guarantees with Empirical Performance
by: Bertsimas, Dimitris, et al.
Published: (2021)

Enhancing Missing Data Imputation through Combined Bipartite Graph and Complete Directed Graph
by: Zhang, Zhaoyang, et al.
Published: (2024)

Missing Data Imputation Based on Dynamically Adaptable Structural Equation Modeling with Self-Attention
by: Deng, Ou, et al.
Published: (2023)

Improving Rectified Flow with Boundary Conditions
by: Hu, Xixi, et al.
Published: (2025)

Markov Missing Graph: A Graphical Approach for Missing Data Imputation
by: Yang, Yanjiao, et al.
Published: (2025)

Mitigating Structural Overfitting: A Distribution-Aware Rectification Framework for Missing Feature Imputation
by: Song, Yifan, et al.
Published: (2025)

Deep Neural Network-Driven Adaptive Filtering
by: Wang, Qizhen, et al.
Published: (2025)

Machine Learning Based Missing Values Imputation in Categorical Datasets
by: Ishaq, Muhammad, et al.
Published: (2023)

Imputation-free Learning of Tabular Data with Missing Values using Incremental Feature Partitions in Transformer
by: Samad, Manar D., et al.
Published: (2025)

Meta-Imputation Balanced (MIB): An Ensemble Approach for Handling Missing Data in Biomedical Machine Learning
by: Azad, Fatemeh, et al.
Published: (2025)

ITI-IQA: a Toolbox for Heterogeneous Univariate and Multivariate Missing Data Imputation Quality Assessment
by: Pons-Suñer, Pedro, et al.
Published: (2024)

On the Performance of Imputation Techniques for Missing Values on Healthcare Datasets
by: Joel, Luke Oluwaseye, et al.
Published: (2024)