Missing data represents a fundamental and pervasive challenge in modern data science, significantly impeding analytical capabilities and decision-making processes across an exceptionally broad spectrum of disciplines, including healthcare, bioinformatics, social science, e-commerce and industrial monitoring systems. Despite decades of research and the development of numerous imputation methodologies, existing literature remains fragmented across disciplinary boundaries, creating a critical need for a comprehensive, interdisciplinary synthesis that bridges statistical foundations with contemporary machine learning advances. This work systematically covers fundamental concepts – including missingness mechanisms, single vs. multiple imputation and varying imputation goals – and explores problem characteristics across different domains. The review extensively categorizes imputation methods, spanning classical techniques (e.g. regression and expectation-maximization algorithm) to modern approaches such as low-rank and high-rank matrix completion, deep learning models (autoencoders, generative adversarial networks, diffusion models and graph neural networks) and large language models. Special consideration is given to methods tailored for complex data types, including tensor data, time series, graph-structured data, categorical data and multimodal data, acknowledging their unique challenges and solution approaches. Beyond methodological considerations, they investigate the crucial integration of imputation with downstream machine learning tasks, including classification, clustering and anomaly detection, examining both sequential pipelines and joint optimization frameworks. The review also assesses theoretical guarantees for various methods, available benchmarking resources and comprehensive evaluation metrics. Finally, they identify critical challenges and future directions, emphasizing the complexities of model selection and hyperparameter optimization, the growing importance of privacy-preserving imputation through federated learning approaches and the ambitious pursuit of generalizable or universal imputation models that can adapt across domains and data types, thereby providing a roadmap for advancing this vital field of research.
Many important problems in science and engineering involve inferring a signal from noisy and/or incomplete observations, where the observation process is known. Historically, this problem has been tackled using hand-crafted regularization (e.g., sparsity, total-variation) to obtain meaningful estimates. Recent data-driven methods often offer better solutions by directly learning a solver from examples of ground-truth signals and associated observations. However, in many real-world applications, obtaining ground-truth references for training is expensive or impossible. Self-supervised learning methods offer a promising alternative by learning a solver from measurement data alone, bypassing the need for ground-truth references. This manuscript provides a comprehensive summary of different self-supervised methods for inverse problems, with a special emphasis on their theoretical underpinnings, and presents practical applications in imaging inverse problems.
Diffusion models have emerged as powerful deep generative techniques, producing high-quality and diverse samples in applications in various domains, including audio. While existing reviews provide overviews, there remains limited in-depth discussion of these specific design choices. The audio diffusion model literature also lacks principled guidance for the implementation of these design choices and their comparisons for different applications. This survey provides a comprehensive review of diffusion model design with an emphasis on design principles for quality improvement and conditioning for audio applications. The authors adopt the score modeling perspective as a unifying framework that accommodates various interpretations, including recent approaches like flow matching. They systematically examine the training and sampling procedures of diffusion models and audio applications through different conditioning mechanisms. To provide an integrated, unified codebase and to promote reproducible research and rapid prototyping, they introduce an open-source codebase (Link to the website of github) that implements their reviewed framework for various audio applications. They demonstrate its capabilities through three case studies: audio generation, speech enhancement and text-to-speech synthesis, with benchmark evaluations on standard data sets.
Generative adversarial networks (GANs) and diffusion models have dramatically advanced deepfake technology, and its threats to digital security, media integrity, and public trust have increased rapidly. This research explored zero-shot deepfake detection, an emerging method even when the models have never seen a particular deepfake variation. In this work, we studied self-supervised learning, transformer-based zero-shot classifier, generative model fingerprinting, and meta-learning techniques that better adapt to the ever-evolving deepfake threat. In addition, we suggested AI-driven prevention strategies that mitigated the underlying generation pipeline of the deepfakes before they occurred. They consisted of adversarial perturbations for creating deepfake generators, digital watermarking for content authenticity verification, real-time AI monitoring for content creation pipelines, and blockchain-based content verification frameworks. Despite these advancements, zero-shot detection and prevention faced critical challenges such as adversarial attacks, scalability constraints, ethical dilemmas, and the absence of standardized evaluation benchmarks. These limitations were addressed by discussing future research directions on explainable AI for deepfake detection, multimodal fusion based on image, audio, and text analysis, quantum AI for enhanced security, and federated learning for privacy-preserving deepfake detection. This further highlighted the need for an integrated defense framework for digital authenticity that utilized zero-shot learning in combination with preventive deepfake mechanisms. Finally, we highlighted the important role of interdisciplinary collaboration between AI researchers, cybersecurity experts, and policymakers to create resilient defenses against the rising tide of deepfake attacks.
Graph learning has rapidly evolved into a critical subfield of machine learning and artificial intelligence (AI). Its development began with early graph-theoretic methods, gaining significant momentum with the advent of graph neural networks (GNNs). Over the past decade, progress in scalable architectures, dynamic graph modeling, multimodal learning, generative AI, explainable AI (XAI), and responsible AI has broadened the applicability of graph learning to various challenging environments. Graph learning is significant due to its ability to model complex, non-Euclidean relationships that traditional machine learning struggles to capture, thus better supporting real-world applications ranging from drug discovery and fraud detection to recommender systems and scientific reasoning. However, challenges like scalability, generalization, heterogeneity, interpretability, and trustworthiness must be addressed to unlock its full potential. This survey provides a comprehensive introduction to graph learning, focusing on key dimensions including scalable, temporal, multimodal, generative, explainable, and responsible graph learning. We review state-of-the-art techniques for efficiently handling large-scale graphs, capturing dynamic temporal dependencies, integrating heterogeneous data modalities, generating novel graph samples, and enhancing interpretability to foster trust and transparency. We also explore ethical considerations, such as privacy and fairness, to ensure responsible deployment of graph learning models. Additionally, we identify and discuss emerging topics, highlighting recent integration of graph learning and other AI paradigms and offering insights into future directions. This survey serves as a valuable resource for researchers and practitioners seeking to navigate the rapidly evolving landscape of graph learning.
