Fetching the paper…
Reading the bibliography…
Tabular data is critical across diverse domains, yet high-quality datasets remain scarce due to privacy concerns and the cost of collection.
The use of multiple measurements in taxonomic problems
Ronald A Fisher. 1936 · 1936
Earlier work this paper cites.
The Scope of Integer and Combinatorial Optimization , chapter I.1. John Wiley & Sons, Ltd
George Nemhauser and Laurence Wolsey. 1988 · 1988
Earlier work this paper cites.
Five balltree construction algorithms
Stepphen M. Omohundro. 1989 · 1989
Earlier work this paper cites.
Support-vector networks
Corinna Cortes and Vladimir Vapnik. 1995 · 1995
Earlier work this paper cites.
Glu variants improve transformer
Noam Shazeer. 2020 · 2002
Earlier work this paper cites.
Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop. 2006 · 2006
Earlier work this paper cites.
Pattern recognition and machine learning , volume 4
Christopher M Bishop and Nasser M Nasrabadi. 2006 · 2006
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008 · 2008
Earlier work this paper cites.
Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay. 2011 · 2011
Earlier work this paper cites.
Fast search in hamming space with multi-index hashing
Mohammad Norouzi, Ali Punjani, and David J Fleet. 2012 · 2012
Earlier work this paper cites.
Processing genome scale tabular data with wormtable
Jerome Kelleher, Rob W Ness, and Daniel L Halligan. 2013 · 2013
Earlier work this paper cites.
Incidence of end-stage renal disease attributed to diabetes among persons with diagnosed diabetes—united states and puerto rico, 2000–2014
Nilka Rios Burrows. 2017 · 2014
Earlier work this paper cites.
Multivariate density estimation: theory, practice, and visualization
David W Scott. 2015 · 2015
Earlier work this paper cites.
A survey of data mining and machine learning methods for cyber security intrusion detection
Anna L. Buczak and Erhan Guven. 2016 · 2016
Earlier work this paper cites.
The synthetic data vault
Neha Patki, Roy Wedge, and Kalyan Veeramachaneni. 2016 · 2016
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Banach wasserstein gan
Jonas Adler and Sebastian Lunz. 2018 · 2018
Earlier work this paper cites.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal. 2018 · 2018
Earlier work this paper cites.
California housing prices
Cameron Nugent. 2018 · 2018
Earlier work this paper cites.
Density estimation for statistics and data analysis
Bernard W Silverman. 2018 · 2018
Cited alongside, same era.
Dags with no tears: Continuous optimization for structure learning
Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing. 2018 · 2018
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Cited alongside, same era.
Modeling tabular data using conditional GAN . Curran Associates Inc., Red Hook, NY, USA
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. 2019 · 2019
Cited alongside, same era.
Trust issues: Uncertainty estimation does not enable reliable ood detection on medical tabular data
Dennis Ulmer, Lotta Meijerink, and Giovanni Cinà. 2020 · 2020
Cited alongside, same era.
Robust cognitive load detection from wrist-band sensors
GOGGLE: Generative modelling for tabular data by learning relational structure
Tennison Liu, Zhaozhi Qian, Jeroen Berrevoets, and Mihaela van der Schaar. 2023 · 2023
Later among the works it cites.
Findiff: Diffusion models for financial tabular data generation
Timur Sattarov, Marco Schreyer, and Damian Borth. 2023 · 2023
Later among the works it cites.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. 2023 · 2023
Later among the works it cites.
Prompt design and engineering: Introduction and advanced methods
Xavier Amatriain. 2024 · 2024
Later among the works it cites.
The claude 3 model family: Opus, sonnet, haiku
Anthropic. 2024 · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Vadim Borisov, Enkelejda Kasneci, and Gjergji Kasneci. 2021 · 2021
Cited alongside, same era.
Copula flows for synthetic data generation
Sanket Kamthe, Samuel A. Assefa, and Marc Peter Deisenroth. 2021 · 2021
Cited alongside, same era.
Fidelity and privacy of synthetic medical data
Ofer Mendelevitch and Michael D. Lesh. 2021 · 2021
Cited alongside, same era.
Mushroom data creation, curation, and simulation to support classification tasks
Dennis Wagner, Dominik Heider, and Georges Hattab. 2021 · 2021
Cited alongside, same era.
When are deep networks really better than decision forests at small sample sizes, and how?
Haoyin Xu, Kaleab A. Kinfu, Will LeVine, Sambit Panda, Jayanta Dey, Michael Ainsworth, Yu-Chung Peng, Madi Kusmanov, Florian Engert, Christopher M. White, Joshua T. Vogelstein, and Carey E. Priebe. 2021 · 2021
Cited alongside, same era.
Ganblr: a tabular data generation model
Yishuo Zhang, Nayyar A Zaidi, Jiahui Zhou, and Gang Li. 2021 · 2021
Cited alongside, same era.
Deep neural networks and tabular data: A survey
Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci. 2022 · 2022
Cited alongside, same era.
Generating realistic tabular data with large language models
Dang Nguyen, Sunil Gupta, Kien Do, Thin Nguyen, and Svetha Venkatesh. 2024 · 2024
Later among the works it cites.
An empirical study of utility and disclosure risk for tabular data synthesis models: In-depth analysis and interesting findings
Dae-Young Park and In-Young Ko. 2024 · 2024
Later among the works it cites.
Incorporating causality in energy consumption forecasting using deep neural networks
Kshitij Sharma, Yogesh K. Dwivedi, and Bhimaraya Metri. 2024 · 2024
Later among the works it cites.
Multiview deep anomaly detection: A systematic exploration
Siqi Wang, Jiyuan Liu, Guang Yu, Xinwang Liu, Sihang Zhou, En Zhu, Yuexiang Yang, Jianping Yin, and Wenjing Yang. 2024 · 2024
Later among the works it cites.
Understanding the performance and estimating the cost of llm fine-tuning
Yuchen Xia, Jiho Kim, Yuhan Chen, Haojie Ye, Souvik Kundu, Cong Callie Hao, and Nishil Talati. 2024 · 2024
Later among the works it cites.
P-TA: Using proximal policy optimization to enhance tabular data augmentation via large language models
Shuo Yang, Chenchen Yuan, Yao Rong, Felix Steinbauer, and Gjergji Kasneci. 2024 · 2024
Later among the works it cites.
Mixed-type tabular data synthesis with score-based diffusion in latent space
Hengrui Zhang, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Xiao Qin, Christos Faloutsos, Huzefa Rangwala, and George Karypis. 2024 · 2024
Later among the works it cites.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. 2025 · 2025
Closest in time.
Accurate predictions on small data with a tabular foundation model
Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, and Frank Hutter. 2025 · 2025
Closest in time.
Llm-tabflow: Synthetic tabular data generation with inter-column logical relationship preservation
Yunbo Long, Liming Xu, and Alexandra Brintrup. 2025 · 2025
Closest in time.
Assessing data augmentation-induced bias in training and testing of machine learning models
Riddhi More and Jeremy S Bradbury. 2025 · 2025
Closest in time.
Deep learning within tabular data: Foundations, challenges, advances and future directions
Weijieying Ren, Tianxiang Zhao, Yuqing Huang, and Vasant Honavar. 2025 · 2025
Closest in time.
Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints
Mihaela C. Stoian and Eleonora Giunchiglia. 2025 · 2025
Closest in time.
A survey on tabular data generation: Utility, alignment, fidelity, privacy, and beyond
Mihaela Cătălina Stoian, Eleonora Giunchiglia, and Thomas Lukasiewicz. 2025 · 2025
Closest in time.
Why llms are bad at synthetic table generation (and what to do about it)
Shengzhe Xu, Cho-Ting Lee, Mandar Sharma, Raquib Bin Yousuf, Nikhil Muralidhar, and Naren Ramakrishnan. 2025 · 2025
Closest in time.