Diffusion Models for Tabular Data Imputation and Synthetic Data Generation
Fuente:
arXiv
Guardado en:
| Autores principales: | Villaizán-Vallelado, Mario, Salvatori, Matteo, Segura, Carlos, Arapakis, Ioannis |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Graph Neural Network contextual embedding for Deep Learning on Tabular Data
por: Villaizán-Vallelado, Mario, et al.
Publicado: (2023)
por: Villaizán-Vallelado, Mario, et al.
Publicado: (2023)
AdSight: Scalable and Accurate Quantification of User Attention in Multi-Slot Sponsored Search
por: Villaizán-Vallelado, Mario, et al.
Publicado: (2025)
por: Villaizán-Vallelado, Mario, et al.
Publicado: (2025)
Self-Supervision Improves Diffusion Models for Tabular Data Imputation
por: Liu, Yixin, et al.
Publicado: (2024)
por: Liu, Yixin, et al.
Publicado: (2024)
DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic Model
por: Wen, Yizhu, et al.
Publicado: (2024)
por: Wen, Yizhu, et al.
Publicado: (2024)
Rethinking the Diffusion Models for Numerical Tabular Data Imputation from the Perspective of Wasserstein Gradient Flow
por: Chen, Zhichao, et al.
Publicado: (2024)
por: Chen, Zhichao, et al.
Publicado: (2024)
TAEGAN: Generating Synthetic Tabular Data For Data Augmentation
por: Li, Jiayu, et al.
Publicado: (2024)
por: Li, Jiayu, et al.
Publicado: (2024)
Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGN
por: Sidorenko, Andrey, et al.
Publicado: (2025)
por: Sidorenko, Andrey, et al.
Publicado: (2025)
TabImpute: Universal Zero-Shot Imputation for Tabular Data
por: Feitelberg, Jacob, et al.
Publicado: (2025)
por: Feitelberg, Jacob, et al.
Publicado: (2025)
Hierarchical Conditional Tabular GAN for Multi-Tabular Synthetic Data Generation
por: Ågren, Wilhelm, et al.
Publicado: (2024)
por: Ågren, Wilhelm, et al.
Publicado: (2024)
How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data
por: Stoian, Mihaela Cătălina, et al.
Publicado: (2024)
por: Stoian, Mihaela Cătălina, et al.
Publicado: (2024)
A Comprehensive Survey of Synthetic Tabular Data Generation
por: Shi, Ruxue, et al.
Publicado: (2025)
por: Shi, Ruxue, et al.
Publicado: (2025)
Dual-Graph Multi-Agent Reinforcement Learning for Handover Optimization
por: Salvatori, Matteo, et al.
Publicado: (2026)
por: Salvatori, Matteo, et al.
Publicado: (2026)
To Predict or Not To Predict? Proportionally Masked Autoencoders for Tabular Data Imputation
por: Kim, Jungkyu, et al.
Publicado: (2024)
por: Kim, Jungkyu, et al.
Publicado: (2024)
Structured Evaluation of Synthetic Tabular Data
por: Yang, Scott Cheng-Hsin, et al.
Publicado: (2024)
por: Yang, Scott Cheng-Hsin, et al.
Publicado: (2024)
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation
por: Sattarov, Timur, et al.
Publicado: (2024)
por: Sattarov, Timur, et al.
Publicado: (2024)
Filling in the Blanks: Applying Data Imputation in incomplete Water Metering Data
por: Amaxilatis, Dimitrios, et al.
Publicado: (2025)
por: Amaxilatis, Dimitrios, et al.
Publicado: (2025)
CARTGen-IR: Synthetic Tabular Data Generation for Imbalanced Regression
por: Pinheiro, António Pedro, et al.
Publicado: (2025)
por: Pinheiro, António Pedro, et al.
Publicado: (2025)
CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation
por: Gorla, Aditya, et al.
Publicado: (2025)
por: Gorla, Aditya, et al.
Publicado: (2025)
Missing Data Multiple Imputation for Tabular Q-Learning in Online RL
por: Chasalow, Kyla, et al.
Publicado: (2025)
por: Chasalow, Kyla, et al.
Publicado: (2025)
TabINR: An Implicit Neural Representation Framework for Tabular Data Imputation
por: Ochs, Vincent, et al.
Publicado: (2025)
por: Ochs, Vincent, et al.
Publicado: (2025)
CuTS: Customizable Tabular Synthetic Data Generation
por: Vero, Mark, et al.
Publicado: (2023)
por: Vero, Mark, et al.
Publicado: (2023)
Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation
por: Byun, Jessup, et al.
Publicado: (2025)
por: Byun, Jessup, et al.
Publicado: (2025)
Generating Synthetic Relational Tabular Data via Structural Causal Models
por: Hoppe, Frederik, et al.
Publicado: (2025)
por: Hoppe, Frederik, et al.
Publicado: (2025)
Evaluating Inter-Column Logical Relationships in Synthetic Tabular Data Generation
por: Long, Yunbo, et al.
Publicado: (2025)
por: Long, Yunbo, et al.
Publicado: (2025)
Synthetic Tabular Data Generation: A Comparative Survey for Modern Techniques
por: Challagundla, Raju, et al.
Publicado: (2025)
por: Challagundla, Raju, et al.
Publicado: (2025)
Evaluating Synthetic Tabular Data Generated To Augment Small Sample Datasets
por: Marin, Javier
Publicado: (2022)
por: Marin, Javier
Publicado: (2022)
SiloFuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Models
por: Shankar, Aditya, et al.
Publicado: (2024)
por: Shankar, Aditya, et al.
Publicado: (2024)
Privacy Preserving Diffusion Models for Mixed-Type Tabular Data Generation
por: Sattarov, Timur, et al.
Publicado: (2025)
por: Sattarov, Timur, et al.
Publicado: (2025)
DiffPuter: Empowering Diffusion Models for Missing Data Imputation
por: Zhang, Hengrui, et al.
Publicado: (2024)
por: Zhang, Hengrui, et al.
Publicado: (2024)
TabDDPM: Modelling Tabular Data with Diffusion Models
por: Kotelnikov, Akim, et al.
Publicado: (2022)
por: Kotelnikov, Akim, et al.
Publicado: (2022)
Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework
por: Sidorenko, Andrey, et al.
Publicado: (2025)
por: Sidorenko, Andrey, et al.
Publicado: (2025)
TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data
por: Tiwald, Paul, et al.
Publicado: (2025)
por: Tiwald, Paul, et al.
Publicado: (2025)
Diffusion-Driven Synthetic Tabular Data Generation for Enhanced DoS/DDoS Attack Classification
por: B, Aravind, et al.
Publicado: (2026)
por: B, Aravind, et al.
Publicado: (2026)
Tabular Data Generation using Binary Diffusion
por: Kinakh, Vitaliy, et al.
Publicado: (2024)
por: Kinakh, Vitaliy, et al.
Publicado: (2024)
Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching
por: Akazan, Ange-Clément, et al.
Publicado: (2024)
por: Akazan, Ange-Clément, et al.
Publicado: (2024)
What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models
por: Kapar, Jan, et al.
Publicado: (2025)
por: Kapar, Jan, et al.
Publicado: (2025)
Deep Generative Imputation Model for Missing Not At Random Data
por: Chen, Jialei, et al.
Publicado: (2023)
por: Chen, Jialei, et al.
Publicado: (2023)
Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class
por: D'souza, Annie, et al.
Publicado: (2024)
por: D'souza, Annie, et al.
Publicado: (2024)
Understanding and Mitigating Memorization in Diffusion Models for Tabular Data
por: Fang, Zhengyu, et al.
Publicado: (2024)
por: Fang, Zhengyu, et al.
Publicado: (2024)
TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation
por: Shi, Juntong, et al.
Publicado: (2024)
por: Shi, Juntong, et al.
Publicado: (2024)
Ejemplares similares
-
Graph Neural Network contextual embedding for Deep Learning on Tabular Data
por: Villaizán-Vallelado, Mario, et al.
Publicado: (2023) -
AdSight: Scalable and Accurate Quantification of User Attention in Multi-Slot Sponsored Search
por: Villaizán-Vallelado, Mario, et al.
Publicado: (2025) -
Self-Supervision Improves Diffusion Models for Tabular Data Imputation
por: Liu, Yixin, et al.
Publicado: (2024) -
DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic Model
por: Wen, Yizhu, et al.
Publicado: (2024) -
Rethinking the Diffusion Models for Numerical Tabular Data Imputation from the Perspective of Wasserstein Gradient Flow
por: Chen, Zhichao, et al.
Publicado: (2024)