Impugan: Learning Conditional Generative Models for Robust Data Imputation

• URL: http://arxiv.org/abs/2512.05950v1
• Date: Fri, 05 Dec 2025 18:46:33 GMT
• Title: Impugan: Learning Conditional Generative Models for Robust Data Imputation
• Authors: Zalish Mahmud, Anantaa Kotal, Aritran Piplai,
• Abstract summary: Impugan is a conditional Generative Adversarial Network (cGAN) for imputing missing values and integrating heterogeneous datasets.<n>During inference, the generator reconstructs missing entries from available features, and the discriminator enforces realism by distinguishing true from imputed data.
• Score: 0.21847754147782886
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Incomplete data are common in real-world applications. Sensors fail, records are inconsistent, and datasets collected from different sources often differ in scale, sampling rate, and quality. These differences create missing values that make it difficult to combine data and build reliable models. Standard imputation methods such as regression models, expectation-maximization, and multiple imputation rely on strong assumptions about linearity and independence. These assumptions rarely hold for complex or heterogeneous data, which can lead to biased or over-smoothed estimates. We propose Impugan, a conditional Generative Adversarial Network (cGAN) for imputing missing values and integrating heterogeneous datasets. The model is trained on complete samples to learn how missing variables depend on observed ones. During inference, the generator reconstructs missing entries from available features, and the discriminator enforces realism by distinguishing true from imputed data. This adversarial process allows Impugan to capture nonlinear and multimodal relationships that conventional methods cannot represent. In experiments on benchmark datasets and a multi-source integration task, Impugan achieves up to 82\% lower Earth Mover's Distance (EMD) and 70\% lower mutual-information deviation (MI) compared to leading baselines. These results show that adversarially trained generative models provide a scalable and principled approach for imputing and merging incomplete, heterogeneous data. Our model is available at: github.com/zalishmahmud/impuganBigData2025

Related papers

• Nonparametric Data Attribution for Diffusion Models [57.820618036556084]
  Data attribution for generative models seeks to quantify the influence of individual training examples on model outputs.<n>We propose a nonparametric attribution method that operates entirely on data, measuring influence via patch-level similarity between generated and training images.
  arXiv  Detail & Related papers  (2025-10-16T03:37:16Z)
• Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning [77.120955854093]
  We show that data diversity can be a strong predictor of generalization in language models.<n>We introduce G-Vendi, a metric that quantifies diversity via the entropy of model-induced gradients.<n>We present Prismatic Synthesis, a framework for generating diverse synthetic data.
  arXiv  Detail & Related papers  (2025-05-26T16:05:10Z)
• Learning Defect Prediction from Unrealistic Data [57.53586547895278]
  Pretrained models of code have become popular choices for code understanding and generation tasks. Such models tend to be large and require commensurate volumes of training data. It has become popular to train models with far larger but less realistic datasets, such as functions with artificially injected bugs. Models trained on such data tend to only perform well on similar data, while underperforming on real world programs.
  arXiv  Detail & Related papers  (2023-11-02T01:51:43Z)
• Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
  We show how the generative process affects the downstream ML task. We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
  arXiv  Detail & Related papers  (2023-05-16T07:30:29Z)
• Learning from aggregated data with a maximum entropy model [73.63512438583375]
  We show how a new model, similar to a logistic regression, may be learned from aggregated data only by approximating the unobserved feature distribution with a maximum entropy hypothesis. We present empirical evidence on several public datasets that the model learned this way can achieve performances comparable to those of a logistic model trained with the full unaggregated data.
  arXiv  Detail & Related papers  (2022-10-05T09:17:27Z)
• Equivariance and Invariance Inductive Bias for Learning from Insufficient Data [65.42329520528223]
  We show why insufficient data renders the model more easily biased to the limited training environments that are usually different from testing. We propose a class-wise invariant risk minimization (IRM) that efficiently tackles the challenge of missing environmental annotation in conventional IRM.
  arXiv  Detail & Related papers  (2022-07-25T15:26:19Z)
• Sharing pattern submodels for prediction with missing values [12.981974894538668]
  Missing values are unavoidable in many applications of machine learning and present challenges both during training and at test time. We propose an alternative approach, called sharing pattern submodels, which i) makes predictions robust to missing values at test time, ii) maintains or improves the predictive power of pattern submodels andiii) has a short description, enabling improved interpretability.
  arXiv  Detail & Related papers  (2022-06-22T15:09:40Z)
• Bayesian data combination model with Gaussian process latent variable model for mixed observed variables under NMAR missingness [0.0]
  It is difficult to obtain a "(quasi) single-source dataset" in which the variables of interest are simultaneously observed. It is necessary to utilize these datasets as a single-source dataset with missing variables. We propose a data fusion method that does not assume that datasets are homogenous.
  arXiv  Detail & Related papers  (2021-09-01T16:09:55Z)
• Copula Flows for Synthetic Data Generation [0.5801044612920815]
  We propose to use a probabilistic model as a synthetic data generator. We benchmark our method on both simulated and real data-sets in terms of density estimation.
  arXiv  Detail & Related papers  (2021-01-03T10:06:23Z)
• A Note on Data Biases in Generative Models [16.86600007830682]
  We investigate the impact of dataset quality on the performance of generative models. We show how societal biases of datasets are replicated by generative models. We present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes.
  arXiv  Detail & Related papers  (2020-12-04T10:46:37Z)
• Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders [28.36260646471421]
  Variational Auto-encoders (VAEs) are deep generative latent variable models. Recent work shows that traditional training methods tend to yield solutions that violate desiderata. We show that both issues stem from the fact that the global optima of the VAE training objective often correspond to undesirable solutions.
  arXiv  Detail & Related papers  (2020-03-17T15:14:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.