DiffPuter: Empowering Diffusion Models for Missing Data Imputation

• URL: http://arxiv.org/abs/2405.20690v2
• Date: Sat, 24 May 2025 02:59:32 GMT
• Title: DiffPuter: Empowering Diffusion Models for Missing Data Imputation
• Authors: Hengrui Zhang, Liancheng Fang, Qitian Wu, Philip S. Yu,
• Abstract summary: This paper introduces DiffPuter, a tailored diffusion model combined with the Expectation-Maximization (EM) algorithm for missing data imputation.<n>Our theoretical analysis shows that DiffPuter's training step corresponds to the maximum likelihood estimation of data density.<n>Our experiments show that DiffPuter achieves an average improvement of 6.94% in MAE and 4.78% in RMSE compared to the most competitive existing method.
• Score: 56.48119008663155
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Generative models play an important role in missing data imputation in that they aim to learn the joint distribution of full data. However, applying advanced deep generative models (such as Diffusion models) to missing data imputation is challenging due to 1) the inherent incompleteness of the training data and 2) the difficulty in performing conditional inference from unconditional generative models. To deal with these challenges, this paper introduces DiffPuter, a tailored diffusion model combined with the Expectation-Maximization (EM) algorithm for missing data imputation. DiffPuter iteratively trains a diffusion model to learn the joint distribution of missing and observed data and performs an accurate conditional sampling to update the missing values using a tailored reversed sampling strategy. Our theoretical analysis shows that DiffPuter's training step corresponds to the maximum likelihood estimation of data density (M-step), and its sampling step represents the Expected A Posteriori estimation of missing values (E-step). Extensive experiments across ten diverse datasets and comparisons with 17 different imputation methods demonstrate DiffPuter's superior performance. Notably, DiffPuter achieves an average improvement of 6.94% in MAE and 4.78% in RMSE compared to the most competitive existing method.

Related papers

• Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout [62.73150122809138]
  Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices.<n>We propose the FedDHAD FL framework, which comes with two novel methods: Dynamic Heterogeneous model aggregation (FedDH) and Adaptive Dropout (FedAD)<n>The combination of these two methods makes FedDHAD significantly outperform state-of-the-art solutions in terms of accuracy (up to 6.7% higher), efficiency (up to 2.02 times faster), and cost (up to 15.0% smaller)
  arXiv  Detail & Related papers  (2025-07-14T16:19:00Z)
• Addressing Missing Data Issue for Diffusion-based Recommendation [26.605773432154518]
  We propose a novel dual-side Thompson sampling-based Diffusion Model (TDM)<n>TDM simulates extra missing data in the guidance signals and allows diffusion models to handle existing missing data through extrapolation.<n> experiments and theoretical analysis validate the effectiveness of TDM in addressing missing data in sequential recommendations.
  arXiv  Detail & Related papers  (2025-05-18T07:45:46Z)
• ItDPDM: Information-Theoretic Discrete Poisson Diffusion Model [5.24776944932192]
  We introduce the Information-Theoretic Discrete Poisson Diffusion Model (ItDPDM), inspired by photon arrival process.<n>Central to our approach is an information-theoretic Poisson Reconstruction Loss (PRL) that has a provable exact relationship with the true data likelihood.<n>ItDPDM attains superior likelihood estimates and competitive generation quality-demonstrating a proof of concept for distribution-robust discrete generative modeling.
  arXiv  Detail & Related papers  (2025-05-08T09:29:05Z)
• Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models [24.396525123797073]
  We propose a method to optimize language model pre-training data mixtures through efficient approximation of the cross-entropy loss corresponding to each candidate mixture.<n>We use this approximation as a source of additional features in a regression model, trained from observations of model loss for a small number of mixtures.
  arXiv  Detail & Related papers  (2025-02-21T21:27:48Z)
• Discrete vs. Continuous Trade-offs for Generative Models [0.0]
  This work explores the theoretical and practical foundations of denoising diffusion probabilistic models (DDPMs) DDPMs and score-based generative models, which leverage processes and Brownian motion to model complex data distributions.
  arXiv  Detail & Related papers  (2024-12-26T08:14:27Z)
• Constrained Diffusion Models via Dual Training [80.03953599062365]
  Diffusion processes are prone to generating samples that reflect biases in a training dataset. We develop constrained diffusion models by imposing diffusion constraints based on desired distributions. We show that our constrained diffusion models generate new data from a mixture data distribution that achieves the optimal trade-off among objective and constraints.
  arXiv  Detail & Related papers  (2024-08-27T14:25:42Z)
• SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training [12.745160748376794]
  We propose a soft deduplication method that maintains dataset integrity while selectively reducing the sampling weight of data with high commonness. Central to our approach is the concept of "data commonness", a metric we introduce to quantify the degree of duplication. Empirical analysis shows that this method significantly improves training efficiency, achieving comparable perplexity scores with at least a 26% reduction in required training steps.
  arXiv  Detail & Related papers  (2024-07-09T08:26:39Z)
• An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations [21.411327264448058]
  We propose an expectation-maximization (EM) approach to train diffusion models from corrupted observations. Our method alternates between reconstructing clean images from corrupted data using a known diffusion model (E-step) and refining diffusion model weights based on these reconstructions (M-step) This iterative process leads the learned diffusion model to gradually converge to the true clean data distribution.
  arXiv  Detail & Related papers  (2024-07-01T07:00:17Z)
• Amortizing intractable inference in diffusion models for vision, language, and control [89.65631572949702]
  This paper studies amortized sampling of the posterior over data, $mathbfxsim prm post(mathbfx)propto p(mathbfx)r(mathbfx)$, in a model that consists of a diffusion generative model prior $p(mathbfx)$ and a black-box constraint or function $r(mathbfx)$. We prove the correctness of a data-free learning objective, relative trajectory balance, for training a diffusion model that samples from
  arXiv  Detail & Related papers  (2024-05-31T16:18:46Z)
• Towards Theoretical Understandings of Self-Consuming Generative Models [56.84592466204185]
  This paper tackles the emerging challenge of training generative models within a self-consuming loop. We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned by future models. We present results for kernel density estimation, delivering nuanced insights such as the impact of mixed data training on error propagation.
  arXiv  Detail & Related papers  (2024-02-19T02:08:09Z)
• MissDiff: Training Diffusion Models on Tabular Data with Missing Values [29.894691645801597]
  This work presents a unified and principled diffusion-based framework for learning from data with missing values. We first observe that the widely adopted "impute-then-generate" pipeline may lead to a biased learning objective. We prove the proposed method is consistent in learning the score of data distributions, and the proposed training objective serves as an upper bound for the negative likelihood in certain cases.
  arXiv  Detail & Related papers  (2023-07-02T03:49:47Z)
• Data Augmentation for Seizure Prediction with Generative Diffusion Model [26.967247641926814]
  Seizure prediction is of great importance to improve the life of patients. The severe imbalance problem between preictal and interictal data still poses a great challenge. Data augmentation is an intuitive way to solve this problem. We propose a novel data augmentation method with diffusion model called DiffEEG.
  arXiv  Detail & Related papers  (2023-06-14T05:44:53Z)
• Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data [68.62134204367668]
  This paper studies score approximation, estimation, and distribution recovery of diffusion models, when data are supported on an unknown low-dimensional linear subspace. We show that with a properly chosen neural network architecture, the score function can be both accurately approximated and efficiently estimated. The generated distribution based on the estimated score function captures the data geometric structures and converges to a close vicinity of the data distribution.
  arXiv  Detail & Related papers  (2023-02-14T17:02:35Z)
• Score-based Continuous-time Discrete Diffusion Models [102.65769839899315]
  We extend diffusion models to discrete variables by introducing a Markov jump process where the reverse process denoises via a continuous-time Markov chain. We show that an unbiased estimator can be obtained via simple matching the conditional marginal distributions. We demonstrate the effectiveness of the proposed method on a set of synthetic and real-world music and image benchmarks.
  arXiv  Detail & Related papers  (2022-11-30T05:33:29Z)
• 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)

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.