One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data
Prometeia Spa（Prometeia公司）；Prometeia Associazione（Prometeia协会）；University of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学）；University of Padova（帕多瓦大学）；Istituto di Informatica e Telematica CNR（意大利国家研究委员会信息与远程信息处理研究所）
Simone Luetto, Fabrizio Garuti, Enver Sangineto, Lorenzo Forni, Rita Cucchiara
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Published in Machine Learning Journal. 29 pages, 2 figures, 16 tables
There is a recent growing interest in applying Deep Learning techniques to tabular data, in order to replicate the success of other Artificial Intelligence areas in this structured domain. Specifically interesting is the case in which tabular data have a time dependence, such as, for instance financial transactions. However, the heterogeneity of the tabular values, in which categorical elements are mixed with numerical items, makes this adaptation difficult. In this paper we propose a Transformer architecture to represent heterogeneous time-dependent tabular data, in which numerical features are represented using a set of frequency functions and the whole network is uniformly trained with a unique loss function.
Stanford University（斯坦福大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；University of California, Berkeley（加州大学伯克利分校）；École Polytechnique（巴黎高等理工学院）
TabKAN: Advancing Tabular Data Analysis using Kolmogorov-Arnold Network
Department of Computer Science, University of Kentucky（卡内基梅隆大学计算机科学系）；Independent Researcher（独立研究者）；Institute for Biomedical Informatics, University of Kentucky（生物医学信息学研究所）
Understanding the semantic meaning of tabular data requires Entity Linking (EL), in order to associate each cell value to a real-world entity in a Knowledge Base (KB). In this work, we focus on end-to-end solutions for EL on tabular data that do not rely on fact lookup in the target KB. Tabular data contains heterogeneous and sparse context, including column headers, cell values and table captions. We experiment with various models to generate a vector representation for each cell value to be linked. Our results show that it is critical to apply an attention mechanism as well as an attention m…
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Medicine, Zhejiang University（浙江大学医学院）；University of Notre Dame（圣母大学）
Recent development of deep neural networks (DNNs) for tabular learning has largely benefited from the capability of DNNs for automatic feature interaction. However, the heterogeneity nature of tabular features makes such features relatively independent, and developing effective methods to promote tabular feature interaction still remains an open problem. In this paper, we propose a novel Graph Estimator, which automatically estimates the relations among tabular features and builds graphs by assigning edges between related features. Such relation graphs organize independent tabular features int…
University of Cambridge（剑桥大学）；University of Oxford（牛津大学）；The Alan Turing Institute（艾伦·图灵研究所）；UCLA（加州大学洛杉矶分校）
High model performance, on average, can hide that models may systematically underperform on subgroups of the data. We consider the tabular setting, which surfaces the unique issue of outcome heterogeneity - this is prevalent in areas such as healthcare, where patients with similar features can have different outcomes, thus making reliable predictions challenging. To tackle this, we propose Data-IQ, a framework to systematically stratify examples into subgroups with respect to their outcomes. We do this by analyzing the behavior of individual examples during training, based on their predictive …
TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；RPTU Kaiserslautern-Landau（凯泽斯劳滕-兰道大学）；Hochschule Bonn-Rhein-Sieg (H-BRS)（波恩-莱茵-锡格应用技术大学）
In Novel Class Discovery (NCD), the goal is to find new classes in an unlabeled set given a labeled set of known but different classes. While NCD has recently gained attention from the community, no framework has yet been proposed for heterogeneous tabular data, despite being a very common representation of data. In this paper, we propose TabularNCD, a new method for discovering novel classes in tabular data. We show a way to extract knowledge from already known classes to guide the discovery process of novel classes in the context of tabular data which contains heterogeneous variables. A part…
TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data
Injecting gaussian noise into training features is well known to have regularization properties. This paper considers noise injections to numeric or categoric tabular features as passed to inference, which translates inference to a non-deterministic outcome and may have relevance to fairness considerations, adversarial example protection, or other use cases benefiting from non-determinism. We offer the Automunge library for tabular preprocessing as a resource for the practice, which includes options to integrate random sampling or entropy seeding with the support of quantum circuits, represent…
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；LG AI Research（LG人工智能研究院）
Learning with few labeled tabular samples is often an essential requirement for industrial machine learning applications as varieties of tabular data suffer from high annotation costs or have difficulties in collecting new samples for novel tasks. Despite the utter importance, such a problem is quite under-explored in the field of tabular learning, and existing few-shot learning schemes from other domains are not straightforward to apply, mainly due to the heterogeneous characteristics of tabular data. In this paper, we propose a simple yet effective framework for few-shot semi-supervised tabu…
CTSyn: A Foundation Model for Cross Tabular Data Generation
Department of Statistics and Data Science, University of California Los Angeles（统计与数据科学系，加州大学洛杉矶分校）；Department of Computer Science, University of California Los Angeles（计算机科学系，加州大学洛杉矶分校）
Criteo AI Lab（Criteo AI实验室）；Yandex；HSE University（高等经济大学）；MIPT（莫斯科物理技术学院）
Graph neural networks (GNNs) are powerful models that have been successful in various graph representation learning tasks. Whereas gradient boosted decision trees (GBDT) often outperform other machine learning methods when faced with heterogeneous tabular data. But what approach should be used for graphs with tabular node features? Previous GNN models have mostly focused on networks with homogeneous sparse features and, as we show, are suboptimal in the heterogeneous setting. In this work, we propose a novel architecture that trains GBDT and GNN jointly to get the best of both worlds: the GBDT…
Representation Learning for Tabular Data: A Comprehensive Survey
School of Artificial Intelligence, Nanjing University（人工智能学院，南京大学）；National Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家实验室，南京大学）
TabGift: A Context-Efficient Foundation Model for Tabular Data Generation
School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）
Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching
AIMS Research and Innovation Center（AIMS研究与创新中心）；Mila Québec AI Institute（魁北克AI研究所）；Université de Montréal（蒙特利尔大学）；SAIT Montreal AI Lab（蒙特利尔SAIT人工智能实验室）
Schema-Adaptive Tabular Representation Learning with LLMs for Generalizable Multimodal Clinical Reasoning
Beijing University of Posts and Telecommunications（北京邮电大学）；Boston University（波士顿大学）；University of Southern California（南加州大学）；GDIIST；Renyixun Health Technology Co., Ltd.（仁心迅健康科技有限公司）
Generalization Can Emerge in Tabular Foundation Models From a Single Table
University of Toronto（多伦多大学）；Polytechnique Montréal（蒙特利尔大学）；Mila – Quebec AI Institute（魁北克人工智能研究所）；Layer 6 AI（Layer 6 AI公司）；Prior Labs（Prior实验室）；ELLIS Institute（ELLIS研究所）；Tübingen University of Freiburg（弗赖堡大学）
