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Journal articles on the topic 'Tabular data'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Altman, Naomi, and Martin Krzywinski. "Tabular data." Nature Methods 14, no. 4 (2017): 329–30. http://dx.doi.org/10.1038/nmeth.4239.

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Eken, Süleyman, Ahmet Sayar, and Kürşat Topçuoğlu. "AutoTest: Automation to Test Tabular Data Quality." International Journal of Computer and Electrical Engineering 6, no. 4 (2014): 365–68. http://dx.doi.org/10.7763/ijcee.2014.v6.854.

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Altman, Naomi, and Martin Krzywinski. "Author Correction: Tabular data." Nature Methods 16, no. 7 (2019): 658. http://dx.doi.org/10.1038/s41592-019-0474-z.

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Badaro, Gilbert, and Paolo Papotti. "Transformers for tabular data representation." Proceedings of the VLDB Endowment 15, no. 12 (2022): 3746–49. http://dx.doi.org/10.14778/3554821.3554890.

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In the last few years, the natural language processing community witnessed advances in neural representations of free texts with transformer-based language models (LMs). Given the importance of knowledge available in relational tables, recent research efforts extend LMs by developing neural representations for tabular data. In this tutorial, we present these proposals with two main goals. First, we introduce to a database audience the potentials and the limitations of current models. Second, we demonstrate the large variety of data applications that benefit from the transformer architecture. T

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Kelly, James P., Bruce L. Golden, Arjang A. Assad, and Edward K. Baker. "Controlled Rounding of Tabular Data." Operations Research 38, no. 5 (1990): 760–72. http://dx.doi.org/10.1287/opre.38.5.760.

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Menon, Anil, and Nadhamuni Nerella. "Communicating Tabular Data Using ORACLE." Pharmaceutical Development and Technology 5, no. 3 (2000): 423–31. http://dx.doi.org/10.1081/pdt-100100559.

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Humpal, John J. "Numbers, Statistics, and Tabular Data." Radiology 218, no. 1 (2001): 12. http://dx.doi.org/10.1148/radiology.218.1.r01ja6812.

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MORRISON, PHILIP S. "Symbolic Representation of Tabular Data." New Zealand Journal of Geography 79, no. 1 (2008): 11–18. http://dx.doi.org/10.1111/j.0028-8292.1985.tb00199.x.

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Gonzalez, Joe Fred, and Lawrence H. Cox. "Software for tabular data protection." Statistics in Medicine 24, no. 4 (2005): 659–69. http://dx.doi.org/10.1002/sim.2043.

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Zhou, Zhi, Kun-Yang Yu, Lan-Zhe Guo, and Yu-Feng Li. "Fully Test-time Adaptation for Tabular Data." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 23027–35. https://doi.org/10.1609/aaai.v39i21.34466.

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Tabular data plays a vital role in various real-world scenarios and finds extensive applications. Although recent deep tabular models have shown remarkable success, they still struggle to handle data distribution shifts, leading to performance degradation when testing distributions change. To remedy this, a robust tabular model must adapt to generalize to unknown distributions during testing. In this paper, we investigate the problem of fully test-time adaptation (FTTA) for tabular data, where the model is adapted using only the testing data. We identify three key challenges: the existence of

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C., Keerthy, and Sabitha S. "Privacy Preserved Data Publishing Techniques for Tabular Data." International Journal of Computer Applications 151, no. 9 (2016): 1–6. http://dx.doi.org/10.5120/ijca2016911874.

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DOBRA, ADRIAN, ALAN F. KARR, ASHISH P. SANIL, and STEPHEN E. FIENBERG. "SOFTWARE SYSTEMS FOR TABULAR DATA RELEASES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, no. 05 (2002): 529–44. http://dx.doi.org/10.1142/s0218488502001624.

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We describe two classes of software systems that release tabular summaries of an underlying database. Table servers respond to user queries for (marginal) sub-tables of the "full" table summarizing the entire database, and are characterized by dynamic assessment of disclosure risk, in light of previously answered queries. Optimal tabular releases are static releases of sets of sub-tables that are characterized by maximizing the amount of information released, as given by a measure of data utility, subject to a constraint on disclosure risk. Underlying abstractions — primarily associated with t

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Sargent, Philip M. "Technical data interchange using tabular formats." Journal of Chemical Information and Modeling 31, no. 2 (1991): 297–300. http://dx.doi.org/10.1021/ci00002a016.

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14

Ozturk, Ovunc. "OPPCAT: Ontology population from tabular data." Journal of Information Science 46, no. 2 (2019): 161–75. http://dx.doi.org/10.1177/0165551519827892.

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In order to present large amount of information on the Web to both users and machines, it is urgently needed to structure Web data. E-commerce is one of the areas where increasing data bottlenecks on the Web inhibit data access. Ontological display of the product information enables better product comparison and search applications using the semantics of the product specifications and their corresponding values. In this article, we present a framework called OPPCAT, which is used for semi-automatic ontology population from tabular data in e-commerce stores and product catalogues. As a result,

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Bragilovski, Maxim, Zahi Kapri, Lior Rokach, and Shelly Levy-Tzedek. "TLTD: Transfer Learning for Tabular Data." Applied Soft Computing 147 (November 2023): 110748. http://dx.doi.org/10.1016/j.asoc.2023.110748.

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Bonet, David, Daniel Mas Montserrat, Xavier Giró-i-Nieto, and Alexander G. Ioannidis. "HyperFast: Instant Classification for Tabular Data." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 11114–23. http://dx.doi.org/10.1609/aaai.v38i10.28988.

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Training deep learning models and performing hyperparameter tuning can be computationally demanding and time-consuming. Meanwhile, traditional machine learning methods like gradient-boosting algorithms remain the preferred choice for most tabular data applications, while neural network alternatives require extensive hyperparameter tuning or work only in toy datasets under limited settings. In this paper, we introduce HyperFast, a meta-trained hypernetwork designed for instant classification of tabular data in a single forward pass. HyperFast generates a task-specific neural network tailored to

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Rauf, Hafiz Tayyab, André Freitas, and Norman William Paton. "TableDC: Deep Clustering for Tabular Data." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–28. https://doi.org/10.1145/3725366.

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Deep clustering (DC), a fusion of deep representation learning and clustering, has recently demonstrated positive results in data science, particularly text processing and computer vision. However, joint optimization of feature learning and data distribution in the multi-dimensional space is domain-specific, so existing DC methods struggle to generalize to other application domains (such as data integration). In data management tasks, where high-density embeddings and overlapping clusters dominate, a data management-specific DC algorithm should be able to interact better with the data properti

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O’Brien Quinn, Helen, Mohamed Sedky, Janet Francis, and Michael Streeton. "Literature Review of Explainable Tabular Data Analysis." Electronics 13, no. 19 (2024): 3806. http://dx.doi.org/10.3390/electronics13193806.

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Explainable artificial intelligence (XAI) is crucial for enhancing transparency and trust in machine learning models, especially for tabular data used in finance, healthcare, and marketing. This paper surveys XAI techniques for tabular data, building on] previous work done, specifically a survey of explainable artificial intelligence for tabular data, and analyzes recent advancements. It categorizes and describes XAI methods relevant to tabular data, identifies domain-specific challenges and gaps, and examines potential applications and trends. Future research directions emphasize clarifying t

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Kumari, Chandrani, and Rahul Siddharthan. "MMM and MMMSynth: Clustering of heterogeneous tabular data, and synthetic data generation." PLOS ONE 19, no. 4 (2024): e0302271. http://dx.doi.org/10.1371/journal.pone.0302271.

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We provide new algorithms for two tasks relating to heterogeneous tabular datasets: clustering, and synthetic data generation. Tabular datasets typically consist of heterogeneous data types (numerical, ordinal, categorical) in columns, but may also have hidden cluster structure in their rows: for example, they may be drawn from heterogeneous (geographical, socioeconomic, methodological) sources, such that the outcome variable they describe (such as the presence of a disease) may depend not only on the other variables but on the cluster context. Moreover, sharing of biomedical data is often hin

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Chen, Jintai, Kuanlun Liao, Yao Wan, Danny Z. Chen, and Jian Wu. "DANets: Deep Abstract Networks for Tabular Data Classification and Regression." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 3930–38. http://dx.doi.org/10.1609/aaai.v36i4.20309.

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Tabular data are ubiquitous in real world applications. Although many commonly-used neural components (e.g., convolution) and extensible neural networks (e.g., ResNet) have been developed by the machine learning community, few of them were effective for tabular data and few designs were adequately tailored for tabular data structures. In this paper, we propose a novel and flexible neural component for tabular data, called Abstract Layer (AbstLay), which learns to explicitly group correlative input features and generate higher-level features for semantics abstraction. Also, we design a structur

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Azizi, Ilia, and Iegor Rudnytskyi. "Improving Real Estate Rental Estimations with Visual Data." Big Data and Cognitive Computing 6, no. 3 (2022): 96. http://dx.doi.org/10.3390/bdcc6030096.

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Multi-modal data are widely available for online real estate listings. Announcements can contain various forms of data, including visual data and unstructured textual descriptions. Nonetheless, many traditional real estate pricing models rely solely on well-structured tabular features. This work investigates whether it is possible to improve the performance of the pricing model using additional unstructured data, namely images of the property and satellite images. We compare four models based on the type of input data they use: (1) tabular data only, (2) tabular data and property images, (3) t

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Reis, Eduardo, Mohamed Abdelaal, and Carsten Binnig. "Generalizable Data Cleaning of Tabular Data in Latent Space." Proceedings of the VLDB Endowment 17, no. 13 (2024): 4786–98. https://doi.org/10.14778/3704965.3704983.

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In this paper, we present a new method for learned data cleaning. In contrast to existing methods, our method learns to clean data in the latent space. The main idea is that we (1) shape the latent space such that we know the area where clean data resides and (2) learn latent operators trained on error repair (Lopster) which shift erroneous data (e.g., table rows with noise, outliers, or missing values) in their latent representation back to a "clean" region, thus abstracting the complexities of the input domain. When formulating data cleaning as a simple shift operation in latent space, we ca

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Mao, Yuchen. "TabTranSELU: A transformer adaptation for solving tabular data." Applied and Computational Engineering 51, no. 1 (2024): 81–88. http://dx.doi.org/10.54254/2755-2721/51/20241174.

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Tabular data are most prevalent datasets in real world, yet the integration of deep learning algorithms in tabular data often garners less attention despite their widespread utilization in other field. This phenomenon could be attributed to the dominance of the classical algorithms in their simplicity and interpretability, and the superior performance of the gradient boosting tree models in tabular data. In this paper, a simple yet affective adaptation of the Transformer architecture tailored specifically for tabular data is presented, not only achieving good performance but also retains a hig

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Gayle, Vernon. "Modelling Tabular Data with an Ordered Outcome." Sociological Research Online 1, no. 3 (1996): 1–10. http://dx.doi.org/10.5153/sro.22.

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A large amount of data that is considered within sociological studies consists of categorical variables that lend themselves to tabular analysis. In the sociological analysis of data regarding social class and educational attainment, for example, the variables of interest can often plausibly be considered as having a substantively interesting order. Standard log-linear models do not take ordinality into account, thereby potentially they may disregard useful information. Analyzing tables where the response variable has ordered categories through model building has been problematic in software p

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Shan, Caihua, Nikos Mamoulis, Guoliang Li, Reynold Cheng, Zhipeng Huang, and Yudian Zheng. "A Crowdsourcing Framework for Collecting Tabular Data." IEEE Transactions on Knowledge and Data Engineering 32, no. 11 (2020): 2060–74. http://dx.doi.org/10.1109/tkde.2019.2914903.

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Barsnes, Harald, Marc Vaudel, and Lennart Martens. "JSparklines: Making tabular proteomics data come alive." PROTEOMICS 15, no. 8 (2015): 1428–31. http://dx.doi.org/10.1002/pmic.201400356.

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Kolb, Samuel, Sergey Paramonov, Tias Guns, and Luc De Raedt. "Learning constraints in spreadsheets and tabular data." Machine Learning 106, no. 9-10 (2017): 1441–68. http://dx.doi.org/10.1007/s10994-017-5640-x.

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28

Hwang, Yejin, and Jongwoo Song. "Recent deep learning methods for tabular data." Communications for Statistical Applications and Methods 30, no. 2 (2023): 215–26. http://dx.doi.org/10.29220/csam.2023.30.2.215.

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29

Khedkar, Sanskruti, Shilpa Lambor, Yogita Narule, and Prathamesh Berad. "Categorical Embeddings for Tabular Data using PyTorch." ITM Web of Conferences 56 (2023): 02002. http://dx.doi.org/10.1051/itmconf/20235602002.

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Deep learning has received much attention for computer vision and natural language processing, but less for tabular data, which is the most prevalent type of data used in industry. Embeddings offer a solution by representing categorical variables as continuous vectors in lowdimensional space. PyTorch provides excellent support for GPU acceleration and pre-built functions and modules, making it easier to work with embeddings and categorical variables. In this research paper, we apply a feedforward neural network model in PyTorch to a multiclass classification problem using the Shelter Animal Ou

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Liu, Tongyu, Ju Fan, Nan Tang, Guoliang Li, and Xiaoyong Du. "Controllable Tabular Data Synthesis Using Diffusion Models." Proceedings of the ACM on Management of Data 2, no. 1 (2024): 1–29. http://dx.doi.org/10.1145/3639283.

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Controllable tabular data synthesis plays a crucial role in numerous applications by allowing users to generate synthetic data with specific conditions. These conditions can include synthesizing tuples with predefined attribute values or creating tuples that exhibit a particular correlation with an external table. However, existing approaches lack the flexibility to support new conditions and can be time-consuming when dealing with multiple conditions. To overcome these limitations, we propose a novel approach that leverages diffusion models to first learn an unconditional generative model. Su

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Гарев, К. В., and K. V. Garev,. "Compression methods for tabular data: Comparative analysis." Международный журнал "Программные продукты и системы" 19 (June 5, 2024): 170–77. http://dx.doi.org/10.15827/0236-235x.146.170-177.

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Kakaraparthy, Aarati, and Jignesh M. Patel. "SplitDF: Splitting Dataframes for Memory-Efficient Data Analysis." Proceedings of the VLDB Endowment 17, no. 9 (2024): 2175–84. http://dx.doi.org/10.14778/3665844.3665849.

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Dataframe is a popular construct in data analysis libraries that offers a tabular view of the data. However, data within a dataframe often has redundancy, which can lead to high memory utilization of data analysis libraries. Inspired by the process of normalization in relational database systems, we propose a technique called splitting that can be applied to tabular data to reduce redundancy. Splitting involves performing lossless join decomposition by explicitly adding joining keys, and unlike normalization, splitting can be applied to tabular data without the need to perform functional depen

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Kang, Inwon, Parikshit Ram, Yi Zhou, Horst Samulowitz, and Oshani Seneviratne. "Effective Data Distillation for Tabular Datasets (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23533–34. http://dx.doi.org/10.1609/aaai.v38i21.30460.

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Data distillation is a technique of reducing a large dataset into a smaller dataset. The smaller dataset can then be used to train a model which can perform comparably to a model trained on the full dataset. Past works have examined this approach for image datasets, focusing on neural networks as target models. However, tabular datasets pose new challenges not seen in images. A sample in tabular dataset is a one dimensional vector unlike the two (or three) dimensional pixel grid of images, and Non-NN models such as XGBoost can often outperform neural network (NN) based models. Our contribution

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Tekin, Ender, Qian You, Devin M. Conathan, Glenn M. Fung, and Thomas S. Kneubuehl. "Harvest – a System for Creating Structured Rate Filing Data from Filing PDFs." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12414–22. http://dx.doi.org/10.1609/aaai.v36i11.21507.

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We present a machine-learning-guided process that can efficiently extract factor tables from unstructured rate filing documents. Our approach combines multiple deep-learning-based models that work in tandem to create structured representations of tabular data present in unstructured documents such as pdf files. This process combines CNN's to detect tables, language-based models to extract table metadata and conventional computer vision techniques to improve the accuracy of tabular data on the machine-learning side. The extracted tabular data is validated through an intuitive user interface. Th

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Toth, Daniell. "Data Smearing: An Approach to Disclosure Limitation for Tabular Data." Journal of Official Statistics 30, no. 4 (2014): 839–57. http://dx.doi.org/10.2478/jos-2014-0050.

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Abstract Statistical agencies often collect sensitive data for release to the public at aggregated levels in the form of tables. To protect confidential data, some cells are suppressed in the publicly released data. One problem with this method is that many cells of interest must be suppressed in order to protect a much smaller number of sensitive cells. Another problem is that the covariates used to aggregate and level of aggregation must be fixed before the data is released. Both of these restrictions can severely limit the utility of the data. We propose a new disclosure limitation method t

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Strong, Mieke, and Piers Higgs. "Mapping between Darwin Core and the Australian Biodiversity Information Standard: A linked data example." Biodiversity Information Science and Standards 7 (September 15, 2023): e112722. https://doi.org/10.3897/biss.7.112722.

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The Australian Biodiversity Information Standard (ABIS) is a data standard that has been developed to represent and exchange biodiversity data expressed using the Resource Description Framework (RDF). ABIS has the TERN ontology at its core, which is a conceptual information model that represents plot-based ecological surveys. The RDF-linked data structure is self-describing, composed of "triples". This format is quite different from tabular data. During the Australian federal government Biodiversity Data Repository pilot project, occurrence data in tabular Darwin Core format was converted into

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Eo, Moonjung, Kyungeun Lee, Hye-Seung Cho, Dongmin Kim, Ye Seul Sim, and Woohyung Lim. "Representation Space Augmentation for Effective Self-Supervised Learning on Tabular Data." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11625–33. https://doi.org/10.1609/aaai.v39i11.33265.

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Tabular data, widely used across industries, remains underexplored in deep learning. Self-supervised learning (SSL) shows promise for pre-training deep neural networks (DNNs) on tabular data, but its potential is hindered by challenges in designing suitable augmentations. Unlike image and text data, where SSL leverages inherent spatial or semantic structures, tabular data lacks such explicit structure. This makes traditional input-level augmentations, like modifying or removing features, less effective due to difficulties in balancing critical information preservation with variability. To addr

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Lee, Seungcheol, and Moohong Min. "CG-TGAN: Conditional Generative Adversarial Networks with Graph Neural Networks for Tabular Data Synthesizing." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 18145–53. https://doi.org/10.1609/aaai.v39i17.33996.

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Data sharing is necessary for AI to be widely used, but sharing sensitive data with others with privacy is risky. To solve these problems, it is necessary to synthesize realistic tabular data. In many cases, tabular data contains a mixture of continuous, mixed, categorical columns. Moreover, columns of the same type may have multimodal distribution or be highly imbalanced. These issues make it challenging to synthesize tabular data. The synthesized tabular data should reflect the relational meaning between columns of tabular data, so modeling the probability distribution of tabular data is a n

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Setiawan, Yohanes, Mohammad Hamim Zajuli Al Faroby, Mochamad Nizar Palefi Ma’ady, I. Made Wisnu Adi Sanjaya, and Cisa Valentino Cahya Ramadhani. "Modality-based Modeling with Data Balancing and Dimensionality Reduction for Early Stunting Detection." Jurnal Online Informatika 10, no. 1 (2025): 53–65. https://doi.org/10.15575/join.v10i1.1495.

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In Indonesia, the stunting rate has reached 36%, significantly higher than the World Health Organization's (WHO) standard of 20%. This high prevalence underscores the urgent need for effective early detection methods. Traditional data mining approaches for stunting detection have primarily focused on unimodal data, either tabular or image data alone, limiting the comprehensiveness and accuracy of the detection models. Modality-based modeling, which integrates image and tabular data, can provide a more holistic view and improve detection accuracy. This research aims to analyze modality-based mo

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Ye, Jianan, Zhaorui Tan, Yijie Hu, Xi Yang, Guangliang Cheng, and Kaizhu Huang. "Disentangling Tabular Data Towards Better One-Class Anomaly Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 13061–68. https://doi.org/10.1609/aaai.v39i12.33425.

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Tabular anomaly detection under the one-class classification setting poses a significant challenge, as it involves accurately conceptualizing "normal" derived exclusively from a single category to discern anomalies from normal data variations. Capturing the intrinsic correlation among attributes within normal samples presents one promising method for learning the concept. To do so, the most recent effort relies on a learnable mask strategy with a reconstruction task. However, this wisdom may suffer from the risk of producing uniform masks, i.e., essentially nothing is masked, leading to less e

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Gicic, Adaleta, Dženana Đonko, and Abdulhamit Subasi. "Time Sequence Deep Learning Model for Ubiquitous Tabular Data with Unique 3D Tensors Manipulation." Entropy 26, no. 9 (2024): 783. http://dx.doi.org/10.3390/e26090783.

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Although deep learning (DL) algorithms have been proved to be effective in diverse research domains, their application in developing models for tabular data remains limited. Models trained on tabular data demonstrate higher efficacy using traditional machine learning models than DL models, which are largely attributed to the size and structure of tabular datasets and the specific application contexts in which they are utilized. Thus, the primary objective of this paper is to propose a method to use the supremacy of Stacked Bidirectional LSTM (Long Short-Term Memory) deep learning algorithms in

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Luo, Haoran, Fan Cheng, Heng Yu, and Yuqi Yi. "SDTR: Soft Decision Tree Regressor for Tabular Data." IEEE Access 9 (2021): 55999–6011. http://dx.doi.org/10.1109/access.2021.3070575.

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Nazdryukhin, A. S., A. M. Fedrak, and N. A. Radeev. "Neural networks for classification problem on tabular data." Journal of Physics: Conference Series 2142, no. 1 (2021): 012013. http://dx.doi.org/10.1088/1742-6596/2142/1/012013.

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Abstract This work presents the results of using self-normalizing neural networks with automatic selection of hyperparameters, TabNet and NODE to solve the problem of tabular data classification. The method of automatic selection of hyperparameters was realised. Testing was carried out with the open source framework OpenML AutoML Benchmark. As part of the work, a comparative analysis was carried out with seven classification methods, experiments were carried out for 39 datasets with 5 methods. NODE shows the best results among the following methods and overperformed standard methods for four d

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Sahakyan, Maria, Zeyar Aung, and Talal Rahwan. "Explainable Artificial Intelligence for Tabular Data: A Survey." IEEE Access 9 (2021): 135392–422. http://dx.doi.org/10.1109/access.2021.3116481.

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Alobaid, Ahmad, Emilia Kacprzak, and Oscar Corcho. "Typology-based semantic labeling of numeric tabular data." Semantic Web 12, no. 1 (2020): 5–20. http://dx.doi.org/10.3233/sw-200397.

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A lot of tabular data are being published on the Web. Semantic labeling of such data may help in their understanding and exploitation. However, many challenges need to be addressed to do this automatically. With numbers, it can be even harder due to the possible difference in measurement accuracy, rounding errors, and even the frequency of their appearance. Multiple approaches have been proposed in the literature to tackle the problem of semantic labeling of numeric values in existing tabular datasets. However, they also suffer from several shortcomings: closely coupled with entity-linking, re

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Minami, Kazuhiro, and Yutaka Abe. "Algorithmic Matching Attacks on Optimally Suppressed Tabular Data." Algorithms 12, no. 8 (2019): 165. http://dx.doi.org/10.3390/a12080165.

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The objective of the cell suppression problem (CSP) is to protect sensitive cell values in tabular data under the presence of linear relations concerning marginal sums. Previous algorithms for solving CSPs ensure that every sensitive cell has enough uncertainty on its values based on the interval width of all possible values. However, we find that every deterministic CSP algorithm is vulnerable to an adversary who possesses the knowledge of that algorithm. We devise a matching attack scheme that narrows down the ranges of sensitive cell values by matching the suppression pattern of an original

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Hertz, Marla I., and Ashley S. McNeill. "Eleven quick tips for properly handling tabular data." PLOS Computational Biology 20, no. 11 (2024): e1012604. http://dx.doi.org/10.1371/journal.pcbi.1012604.

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Mauer, Patryk, and Szczepan Paszkiel. "Tabular Data Models for Predicting Art Auction Results." Applied Sciences 14, no. 23 (2024): 11006. http://dx.doi.org/10.3390/app142311006.

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Predicting art auction results presents a unique challenge due to the complexity and variability of factors influencing artwork prices. This study explores a range of machine learning architectures designed to forecast auction outcomes using tabular data, including historical auction records, artwork characteristics, artist profiles, and market indicators. We evaluate traditional models such as LinearModel, K-Nearest Neighbors, DecisionTree, RandomForest, XGBoost, CatBoost, LightGBM, MLP, VIME, ModelTree, DeepGBM, DeepFM, and SAINT. By comparing the performance of these models on a dataset com

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Adelfio, Marco D., and Hanan Samet. "Schema extraction for tabular data on the web." Proceedings of the VLDB Endowment 6, no. 6 (2013): 421–32. http://dx.doi.org/10.14778/2536336.2536343.

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Karr, Alan F., Adrian Dobra, and Ashish P. Sanil. "Table servers protect confidentiality in tabular data releases." Communications of the ACM 46, no. 1 (2003): 57–58. http://dx.doi.org/10.1145/602421.602451.

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