OpenNovelty.org

Missing Pattern Recognized Diffusion Imputation Model for Missing Not at Random

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.6Download Report PDF
ImputationGenerative Models
Abstract:

Missing data frequently arises across diverse domains, including time-series and image domains. In the real world, missing occurrences often depend on the unobservable values themselves, which are referred to as Missing Not at Random (MNAR). To address this, numerous generative models have been proposed, with diffusion models in particular demonstrating strong capabilities in out-of-sample imputation. However, most existing diffusion-based imputation approaches overlook the MNAR setting and instead rely on restrictive assumptions about the missing process, thereby limiting their applicability to practical scenarios. In this work, we introduce the Missing Pattern Recognized Diffusion Imputation Model (PRDIM), a novel framework that explicitly captures the missing pattern and precisely imputes unobserved values. PRDIM iteratively maximizes the likelihood of the joint distribution for observed values and missing mask under an Expectation-Maximization (EM) algorithm. In this sense, we first employ a pattern recognizer, which approximates the underlying missing pattern and provides guidance during every inference toward more plausible imputations with respect to the missing information. In various experimental settings, we demonstrate that PRDIM achieves the state-of-the-art performance compared to previous diffusion imputation approaches under MNAR setting.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes PRDIM, a diffusion-based imputation framework that explicitly models missing patterns under MNAR conditions using an EM algorithm with a pattern recognizer. It resides in the 'Diffusion and Probabilistic Generative Models' leaf, which contains only two papers including this one. This represents a relatively sparse research direction within the broader taxonomy of 50 papers across 36 topics, suggesting that diffusion-based approaches specifically tailored for MNAR imputation remain underexplored compared to classical statistical methods or autoencoder architectures.

The taxonomy reveals that PRDIM's immediate neighbors include autoencoder-based methods and recurrent architectures within the 'Deep Learning and Generative Model Approaches' branch, alongside statistical techniques like matrix completion and kernel methods in sibling branches. The paper's focus on diffusion processes distinguishes it from these alternatives: autoencoders emphasize reconstruction losses, while statistical methods rely on low-rank or similarity assumptions. The taxonomy's scope notes clarify that diffusion models exclude autoencoder-only or adversarial approaches, positioning PRDIM within a distinct methodological niche that leverages iterative denoising for probabilistic imputation.

Among 17 candidates examined, the core PRDIM framework shows overlap with two prior works, while the pattern recognizer component and ELBO derivation appear more novel. Specifically, the main contribution examined five candidates with two refutable matches, suggesting that diffusion-based MNAR imputation has precedent in the limited search scope. In contrast, the pattern recognizer examined ten candidates with no refutations, and the ELBO derivation examined two candidates with none refutable. These statistics indicate that while the overarching diffusion approach has prior art, the specific integration of pattern recognition and theoretical grounding may offer incremental advances within this sparse subfield.

Given the limited search scope of 17 semantically similar papers, this assessment captures novelty relative to closely related work but cannot claim exhaustive coverage. The sparse population of the diffusion-based MNAR leaf suggests potential for contribution, yet the presence of refutable candidates for the core framework indicates that the fundamental idea has been explored. The pattern recognizer and theoretical components may provide differentiation, though their novelty depends on details not fully captured by top-K semantic matching alone.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
17
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: Missing data imputation under Missing Not at Random conditions. The field addresses scenarios where the probability of missingness depends on unobserved values themselves, making imputation particularly challenging. The taxonomy reveals several complementary research directions: Methodological Frameworks establish theoretical foundations and identifiability conditions; Deep Learning and Generative Model Approaches leverage neural architectures such as autoencoders, GANs, and diffusion models to capture complex data distributions; Statistical and Machine Learning Methods apply classical techniques alongside modern algorithms; Multiple Imputation and Selection Models focus on uncertainty quantification and model-based strategies; Sensitivity Analysis examines robustness to untestable MNAR assumptions; Domain-Specific Applications tailor methods to clinical, genomic, or environmental data; and Comparative Evaluations provide benchmarking across diverse settings. Representative works like Deep Generative MNAR[42] and MNAR Benchmark[9] illustrate how these branches intersect, combining generative modeling with rigorous evaluation protocols. Recent activity highlights tensions between model flexibility and interpretability. Deep generative approaches, including variational autoencoders and diffusion-based methods, offer expressive frameworks but often lack transparency regarding MNAR assumptions. Missing Pattern Diffusion[0] exemplifies this trend by employing probabilistic generative models to learn missingness patterns jointly with data distributions, positioning itself within the diffusion and probabilistic generative models cluster alongside Deep Generative MNAR[42]. While Deep Generative MNAR[42] explores broader generative architectures for MNAR settings, Missing Pattern Diffusion[0] emphasizes diffusion processes to explicitly model missing data mechanisms. Meanwhile, sensitivity analysis methods like Sensitivity Analysis MNAR[41] and domain-specific evaluations such as Clinical Missing Data[3] stress the importance of validating imputation under varying assumptions and real-world constraints. The interplay between these lines—balancing expressive power with robustness guarantees—remains a central open question as practitioners seek reliable imputation in high-stakes applications.

Claimed Contributions

Missing Pattern Recognized Diffusion Imputation Model (PRDIM)

The authors introduce PRDIM, a novel diffusion-based imputation framework that uses an Expectation-Maximization algorithm to jointly model the observed data distribution and the missing pattern. This enables the model to infer latent missing patterns in incomplete data under the MNAR setting.

5 retrieved papers
Can Refute
Pattern recognizer with theoretical guidance for imputation

The authors provide a theoretical analysis showing that a pattern recognizer (discriminator) can supply approximate guidance during the diffusion denoising process. This guidance steers the generation toward imputations consistent with the estimated missing patterns.

10 retrieved papers
ELBO derivation for MNAR in diffusion models

The authors derive an evidence lower bound (ELBO) for the joint log-likelihood of observed data and missing mask within a diffusion framework. This formulation enables principled optimization of both the data distribution and the missing mechanism under MNAR.

2 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Missing Pattern Recognized Diffusion Imputation Model (PRDIM)

The authors introduce PRDIM, a novel diffusion-based imputation framework that uses an Expectation-Maximization algorithm to jointly model the observed data distribution and the missing pattern. This enables the model to infer latent missing patterns in incomplete data under the MNAR setting.

Contribution

Pattern recognizer with theoretical guidance for imputation

The authors provide a theoretical analysis showing that a pattern recognizer (discriminator) can supply approximate guidance during the diffusion denoising process. This guidance steers the generation toward imputations consistent with the estimated missing patterns.

Contribution

ELBO derivation for MNAR in diffusion models

The authors derive an evidence lower bound (ELBO) for the joint log-likelihood of observed data and missing mask within a diffusion framework. This formulation enables principled optimization of both the data distribution and the missing mechanism under MNAR.

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