Synthetic data can be used in various applications, such as correcting bias datasets or replacing scarce original data for simulation purposes. Generative Adversarial Networks (GANs) are considered state-of-the-art for developing generative models. However, these deep learning models are data-driven, and it is, thus, difficult to control the generation process. It can, therefore, lead to the following issues: lack of representativity in the generated data, the introduction of bias, and the possibility of overfitting the sample's noise. This article presents the Directed Acyclic Tabular GAN (DATGAN) to address these limitations by integrating expert knowledge in deep learning models for synthetic tabular data generation. This approach allows the interactions between variables to be specified explicitly using a Directed Acyclic Graph (DAG). The DAG is then converted to a network of modified Long Short-Term Memory (LSTM) cells to accept multiple inputs. Multiple DATGAN versions are systematically tested on multiple assessment metrics. We show that the best versions of the DATGAN outperform state-of-the-art generative models on multiple case studies. Finally, we show how the DAG can create hypothetical synthetic datasets.
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Conditionality has become a core component for Generative Adversarial Networks (GANs) for generating synthetic images. GANs are usually using latent conditionality to control the generation process. However, tabular data only contains manifest variables. Thus, latent conditionality either restricts the generated data or does not produce sufficiently good results. Therefore, we propose a new methodology to include conditionality in tabular GANs inspired by image completion methods. This article presents ciDATGAN, an evolution of the Directed Acyclic Tabular GAN (DATGAN) that has already been sh…
Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure
Department of Mathematics, Informatics and Geosciences（数学、信息学与地质科学系）；University of Trieste（特里este大学）；Department of Engineering and Architecture（工程与建筑系）；InSilicoTrials Technologies BV（InSilicoTrials技术公司）；Amsterdam UMC Institute for Logic, Language and Computation（阿姆斯特丹大学医学中心逻辑、语言与计算研究所）；University of Amsterdam（阿姆斯特丹大学）
Department of Electrical and Computer Engineering, Technion IIT, Israel（技术学院电气与计算机工程系）；Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, USA（宾夕法尼亚大学沃顿商学院统计与数据科学系）；Department of Computer Science, Technion IIT, Israel（技术学院计算机科学系）
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；CSIRO’s Data61（CSIRO数据61）；Monash University（墨尔本大学）；International Center of Future Science, Jilin University（吉林大学未来科学国际中心）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China（知识驱动人机智能工程研究中心，教育部，中国）
Integrating Feature Understandability into Feature Selection for Human-Centred Tabular Explanations
School of Computer Science and Information Technology, University College Cork（科克大学计算机科学与信息技术学院）；Centre for Research Training in Artificial Intelligence, University College Cork（科克大学人工智能研究培训中心）；Department of Methodology and Statistics, Tilburg University（蒂尔堡大学方法论与统计系）；Insight Centre for Data Analytics, University College Cork（科克大学Insight数据分析中心）
West Virginia University（西弗吉尼亚大学）；Rochester Institute of Technology（罗切斯特理工学院）
A promising approach for scalable Gaussian processes (GPs) is the Karhunen-Loève (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator. Such decomposed kernels have the potential to be very fast, and do not depend on the selection of a reduced set of inducing points. However KL decompositions lead to high dimensionality, and variable selection becomes paramount. This paper reports a new method of forward variable selection, enabled by the ordered nature of the basis functions in the KL expansion of the Bayesian…
An Explainable Multi-Task Similarity Measure: Integrating Accumulated Local Effects and Weighted Fréchet Distance
Department of Computer Science, University of Alcala（阿尔卡拉大学计算机科学系）；Faculty of Law, Business and Government, Universidad Francisco de Vitoria（弗朗西斯科·维多利亚大学法学院、商业与政府学院）
A Hybrid Machine Learning Approach for Synthetic Data Generation with Post Hoc Calibration for Clinical Tabular Datasets
organization= Department of Electrical \& Electronic Engineering, Bangladesh University of Business；organization= Department of Electrical Engineering；Computer Science, University of Michigan , country= United States
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Chalmers University of Technology（查尔姆斯理工大学）；Linköping University（林雪平大学）
Observational studies require adjustment for confounding factors that are correlated with both the treatment and outcome. In the setting where the observed variables are tabular quantities such as average income in a neighborhood, tools have been developed for addressing such confounding. However, in many parts of the developing world, features about local communities may be scarce. In this context, satellite imagery can play an important role, serving as a proxy for the confounding variables otherwise unobserved. In this paper, we study confounder adjustment in this non-tabular setting, where…
Resilient Vision-Tabular Multimodal Learning under Modality Missingness
Research Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma（人工智能与计算机系统研究单位，工程系，罗马生物医学大学）；Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University（诊断与干预系，辐射物理，生物医学工程，乌梅大学）
A Sparse-Attention Deep Learning Model Integrating Heterogeneous Multimodal Features for Parkinson's Disease Severity Profiling
School of Computing, Mathematics, and Engineering, Charles Sturt University（计算、数学与工程学院，查尔斯·斯特劳特大学）；School of Dentistry and Medical Sciences, Charles Sturt University（牙科学院与医学科学学院，查尔斯·斯特劳特大学）；AI and Cyber Futures Centre (AICF)（人工智能与未来技术中心（AICF））；Hawkins Clinic General Medical Practice（霍金斯诊所普通医疗实践）
Modern approaches to supervised learning like deep neural networks (DNNs) typically implicitly assume that observed responses are statistically independent. In contrast, correlated data are prevalent in real-life large-scale applications, with typical sources of correlation including spatial, temporal and clustering structures. These correlations are either ignored by DNNs, or ad-hoc solutions are developed for specific use cases. We propose to use the mixed models framework to handle correlated data in DNNs. By treating the effects underlying the correlation structure as random effects, mixed…
Pohang University of Science and Technology (POSTECH)（浦项科技大学）
The capability of reliably detecting out-of-distribution samples is one of the key factors in deploying a good classifier, as the test distribution always does not match with the training distribution in most real-world applications. In this work, we present a deep multi-class data description, termed as Deep-MCDD, which is effective to detect out-of-distribution (OOD) samples as well as classify in-distribution (ID) samples. Unlike the softmax classifier that only focuses on the linear decision boundary partitioning its latent space into multiple regions, our Deep-MCDD aims to find a spherica…
DISCO-TAB: A Hierarchical Reinforcement Learning Framework for Privacy-Preserving Synthesis of Complex Clinical Data
Computational Science Research Center, San Diego State University（圣地亚哥州立大学计算科学研究中心）；School of Nursing and Department of Computer Science, University of California, Irvine（加州大学尔湾分校护理学院与计算机科学系）；Fowler College of Business, San Diego State University（圣地亚哥州立大学福勒商学院）；Department of Computer Science, San Diego State University（圣地亚哥州立大学计算机科学系）
A Multimodal Deep Learning Framework for Predicting ICU Deterioration: Integrating ECG Waveforms with Clinical Data and Clinician Benchmarking
AI4Health Division, Carl von Ossietzky Universität Oldenburg（AI4Health部门，奥尔登堡大学）；CAPASITS, Secretariat of Health of the State of Colima（CAPASITS，科利马州卫生秘书处）；Department of Interventional Cardiology, Hospital Puerta de Hierro Colima（介入心脏病学部，科利马医院）；Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein（医学信息学与统计研究所，基尔大学和石勒苏益格-荷尔斯泰因大学医院）；Department of Anesthesiology, Intensive Care Medicine, Emergency Medicine, and Pain Therapy, Carl von Ossietzky Universität & Klinikum Oldenburg（麻醉学、重症医学、急诊医学和疼痛治疗部，奥尔登堡大学及奥尔登堡诊所）；Department of Cardiology, Angiology and Intensive Care Medicine, Charité Campus Mitte, German Heart Center of the Charité-University Medicine Berlin（心内科、血管科和重症医学部，Charité梅尔特校区，Charité-大学医学中心柏林德国心脏中心）
Exploring Federated Deep Learning for Standardising Naming Conventions in Radiotherapy Data
University of New South Wales（新南威尔士大学）；Ingham Institute for Applied Medical Research（英汉姆应用医学研究所）；Liverpool and Macarthur Cancer Therapy Centres（利物浦和麦卡瑟癌症治疗中心）；University of Sydney（悉尼大学）；St James's Hospital（圣詹姆斯医院）；University of Leeds（利兹大学）
Recursive Learning Without Collapse: A Weighting-Based Stabilization Framework
Wang Yanan Institute for Studies in Economics, Xiamen University（厦门大学王亚南经济研究所）；Department of Statistics and Data Science, University of California, Los Angeles（加州大学洛杉矶分校统计与数据科学系）
TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration
Department of Computing, and Mathematics, Manchester Metropolitan University（计算机与数学系，曼彻斯特 Metropolitan 大学）；Blackpool Teaching Hospitals NHS Foundation Trust（布莱克浦教学医院 NHS 基础信托）；Northern Care Alliance, NHS Foundation Trust（北方护理联盟，NHS 基础信托）；Department of Engineering, Manchester Metropolitan University（工程系，曼彻斯特 Metropolitan 大学）；Faculty of Business and Law, Manchester Metropolitan University（商业与法律学院，曼彻斯特 Metropolitan 大学）；Faculty of Medical Sciences, Newcastle University（医学科学学院，纽卡斯尔大学）
On LLM-Enhanced Mixed-Type Data Imputation with High-Order Message Passing
The University of New South Wales（新南威尔士大学）；Antai College of Economics & Management, Shanghai Jiao Tong University（上海交通大学安泰经济与管理学院）；Zhejiang Gongshang University（浙江工商大学）；Data61, CSIRO（联邦科学与工业研究组织Data61研究院）
DALL-M: Context-Aware Clinical Data Augmentation with LLMs
School of Information Systems, Queensland University of Technology（昆士兰科技大学信息系统学院）；School of Computer Science, Queensland University of Technology（昆士兰科技大学计算机科学学院）；Data Science Institute, University of Technology Sydney（悉尼科技大学数据科学研究所）；Imagiology Department, Grupo Lusiadas（卢西塔达斯集团影像学系）
