Fetching the paper…
Reading the bibliography…
Advances in deep generative modelling have not translated well to tabular data.
Random decision forests
Tin Kam Ho · 1995
Earlier work this paper cites.
Diffusion models for missing value imputation in tabular data
Shuhan Zheng and Nontawat Charoenphakdee · 2000
Earlier work this paper cites.
SMOTE: Synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
Earlier work this paper cites.
Learning generative models via discriminative approaches
Zhuowen Tu · 2007
Earlier work this paper cites.
Bayesian learning via stochastic gradient Langevin dynamics
Max Welling and Yee Whye Teh · 2011
Earlier work this paper cites.
XGBoost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Earlier work this paper cites.
Survey of resampling techniques for improving classification performance in unbalanced datasets
Ajinkya More · 2016
Earlier work this paper cites.
A theory of generative ConvNet
Jianwen Xie, Yang Lu, Song-Chun Zhu, and Yingnian Wu · 2016
Earlier work this paper cites.
Learning energy-based models as generative ConvNets via multi-grid modeling and sampling
Ruiqi Gao, Yang Lu, Junpei Zhou, Song-Chun Zhu, and Ying Nian Wu · 2017
Earlier work this paper cites.
Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
Earlier work this paper cites.
PATE-GAN: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela Van Der Schaar · 2018
Earlier work this paper cites.
In Santurkar, Shibani and Ilyas, Andrew and Tsipras, Dimitris and Engstrom, Logan and Tran, Brandon and Madry, Aleksander , 2019
Image synthesis with a single (robust) classifier · 2019
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Earlier work this paper cites.
Implicit generation and modeling with energy based models
Yilun Du and Igor Mordatch · 2019
Earlier work this paper cites.
Neural spline flows
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
Cited alongside, same era.
FlowSeq: Non-autoregressive conditional sequence generation with generative flow
Xuezhe Ma, Chunting Zhou, Xian Li, Graham Neubig, and Eduard Hovy · 2019
Cited alongside, same era.
Modeling tabular data using conditional GAN
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni · 2019
Cited alongside, same era.
Google dataset search by the numbers
Omar Benjelloun, Shiyu Chen, and Natasha Noy · 2020
Cited alongside, same era.
Oversampling tabular data with deep generative models: Is it worth the effort?
Ramiro D. Camino, Radu State, and Christian A. Hammerschmidt · 2020
Cited alongside, same era.
Sequential deep learning for credit risk monitoring with tabular financial data
See through gradients: Image batch recovery via GradInversion
Hongxu Yin, Arun Mallya, Arash Vahdat, Jose M Alvarez, Jan Kautz, and Pavlo Molchanov · 2021
Later among the works it cites.
A robust variational autoencoder using beta divergence
Haleh Akrami, Anand A. Joshi, Jian Li, Sergül Aydöre, and Richard M. Leahy · 2022
Later among the works it cites.
Language models are realistic tabular data generators
Vadim Borisov, Kathrin Sessler, Tobias Leemann, Martin Pawelczyk, and Gjergji Kasneci · 2022
Later among the works it cites.
Implicit behavioral cloning
Pete Florence, Corey Lynch, Andy Zeng, Oscar A Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, and Jonathan Tompson · 2022
Later among the works it cites.
Haoyang Li · 2022
Later among the works it cites.
Transformers can do Bayesian inference
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jillian M Clements, Di Xu, Nooshin Yousefi, and Dmitry Efimov · 2020
Cited alongside, same era.
Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2020
Cited alongside, same era.
Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li · 2020
Cited alongside, same era.
A customer churn prediction model based on XGBoost and MLP
Qi Tang, Guoen Xia, Xianquan Zhang, and Feng Long · 2020
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
Cited alongside, same era.
NVAE: A deep hierarchical variational autoencoder
Arash Vahdat and Jan Kautz · 2020
Cited alongside, same era.
OpenML benchmarking suites
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan van Rijn, and Joaquin Vanschoren · 2021
Cited alongside, same era.
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter · 2022
Later among the works it cites.
Generative trees: Adversarial and copycat
Richard Nock and Mathieu Guillame-Bert · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
Tabular data: Deep learning is not all you need
Ravid Shwartz-Ziv and Amitai Armon · 2022
Later among the works it cites.
TabPFN: A transformer that solves small tabular classification problems in a second
Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter · 2023
Later among the works it cites.
TabDDPM: Modelling tabular data with diffusion models
Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, and Artem Babenko · 2023
Later among the works it cites.
On the usefulness of synthetic tabular data generation
Dionysis Manousakas and Sergül Aydöre · 2023
Later among the works it cites.
Synthcity: Facilitating innovative use cases of synthetic data in different data modalities
Zhaozhi Qian, Bogdan-Constantin Cebere, and Mihaela van der Schaar · 2023
Later among the works it cites.
REaLTabFormer: Generating realistic relational and tabular data using transformers
Aivin V. Solatorio and Olivier Dupriez · 2023
Later among the works it cites.