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https://github.com/wwweiwei/awesome-self-supervised-learning-for-tabular-data

A collection of research materials on SSL for non-sequential tabular data (SSL4NSTD)
https://github.com/wwweiwei/awesome-self-supervised-learning-for-tabular-data

List: awesome-self-supervised-learning-for-tabular-data

artificial-intelligence deep-learning machine-learning self-supervised-learning tabular-data

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A collection of research materials on SSL for non-sequential tabular data (SSL4NSTD)

Host: GitHub
URL: https://github.com/wwweiwei/awesome-self-supervised-learning-for-tabular-data
Owner: wwweiwei
Created: 2022-09-04T05:09:07.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2024-02-16T11:48:14.000Z (10 months ago)
Last Synced: 2024-05-23T02:05:16.998Z (7 months ago)
Topics: artificial-intelligence, deep-learning, machine-learning, self-supervised-learning, tabular-data
Homepage: http://arxiv.org/abs/2402.01204
Size: 32.2 KB
Stars: 118
Watchers: 6
Forks: 7
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

ultimate-awesome - awesome-self-supervised-learning-for-tabular-data - A collection of research materials on SSL for non-sequential tabular data (SSL4NSTD). (Other Lists / Monkey C Lists)

README

        # Awesome Self-Supervised Learning for Non-Sequential Tabular Data (SSL4NSTD)

![Version](https://img.shields.io/badge/Version-1.0-lightgrey.svg) 

![LastUpdated](https://img.shields.io/badge/LastUpdated-2024.10-lightblue.svg)

![Topic](https://img.shields.io/badge/Topic-SSL4NSTD-pink?logo=github)

This repository contains the frontier research on **self-supervised learning** for tabular data which has been a popular topic recently.


This list is maintained by [Wei-Wei Du](https://wwweiwei.github.io/) and [Wei-Yao Wang](https://github.com/wywyWang). (Actively keep updating)


If you have come across relevant resources or found some errors in this repository, feel free to open an issue or submit a PR.

## Survey Paper

[A Survey on Self-Supervised Learning for Non-Sequential Tabular Data (ACML-24 Journal Track)](https://arxiv.org/abs/2402.01204)

### Citation

```bibtex

@article{DBLP:journals/corr/abs-2402-01204,

  author       = {Wei{-}Yao Wang and

                  Wei{-}Wei Du and

                  Derek Xu and

                  Wei Wang and

                  Wen{-}Chih Peng},

  title        = {A Survey on Self-Supervised Learning for Non-Sequential Tabular Data},

  journal      = {CoRR},

  volume       = {abs/2402.01204},

  year         = {2024}

}

```

## Papers

### Predictive Learning

* VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain (NeurIPS'20) [[Paper]](https://proceedings.neurips.cc/paper/2020/file/7d97667a3e056acab9aaf653807b4a03-Paper.pdf) [[Supplementary]](https://proceedings.neurips.cc/paper/2020/file/7d97667a3e056acab9aaf653807b4a03-Supplemental.pdf) [[Code]](https://github.com/jsyoon0823/VIME)

* TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data (ACL'20) [[Paper]](https://arxiv.org/abs/2005.08314)

* TABBIE: Pretrained Representations of Tabular Data (NAACL'21) [[Paper]](https://arxiv.org/abs/2105.02584)) [[Code]](https://github.com/SFIG611/tabbie)

* CORE: Self- and Semi-supervised Tabular Learning with COnditional REgularizations (NeurIPS'21) [[Paper]](https://sslneurips21.github.io/files/CameraReady/CORE_workshop.pdf)

* TabTransformer: Tabular Data Modeling Using Contextual Embeddings [[Paper]](https://arxiv.org/abs/2012.06678)

* TabNet: Attentive Interpretable Tabular Learning (AAAI'21) [[Paper]](https://arxiv.org/abs/1908.07442) [Code](https://github.com/dreamquark-ai/tabnet)

* Self-Supervision Enhanced Feature Selection with Correlated Gates (ICLR'22) [[Paper]](https://openreview.net/pdf?id=oDFvtxzPOx) [[Code]](https://github.com/chl8856/SEFS)

* TransTab: Learning Transferable Tabular Transformers Across Tables (NeurIPS'22) [[Paper]](https://arxiv.org/abs/2205.09328) [[Code]](https://github.com/RyanWangZf/transtab) [[Blog]](https://realsunlab.medium.com/transtab-learning-transferable-tabular-transformers-across-tables-1e34eec161b8)

* LIFT: Language-Interfaced Fine-Tuning for Non-language Machine Learning Tasks (NeurIPS'22) [[Paper]](https://arxiv.org/pdf/2206.06565.pdf) [[Code]](https://github.com/UW-Madison-Lee-Lab/LanguageInterfacedFineTuning)

* Self Supervised Pre-training for Large Scale Tabular Data (NeurIPS'22 TRL Workshop) [[Paper]](https://table-representation-learning.github.io/assets/papers/self_supervised_pre_training_f.pdf) [[Blog]](https://www.amazon.science/publications/self-supervised-pre-training-for-large-scale-tabular-data)

* Local Contrastive Feature Learning for Tabular Data (CIKM'22) [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3511808.3557630)

* Revisiting Self-Training with Regularized Pseudo-Labeling for Tabular Data (preprint'23) [[Paper]](https://arxiv.org/abs/2302.14013)

* Generative Table Pre-training Empowers Models for Tabular Prediction (EMNLP'23) [[Paper]](https://arxiv.org/pdf/2305.09696.pdf) [[Code]](https://github.com/ZhangTP1996/TapTap)

* TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second (ICLR'23) [[Paper]](https://arxiv.org/pdf/2207.01848.pdf) [[Code]](https://github.com/automl/TabPFN)

* STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables (ICLR'23) [[Paper]](https://arxiv.org/pdf/2303.00918.pdf) [[Code]](https://github.com/jaehyun513/STUNT)

* Language Models are Realistic Tabular Data Generators (ICLR'23) [[Paper]](https://arxiv.org/pdf/2210.06280.pdf) [[Code]](https://github.com/kathrinse/be_great)

* Self-supervised Representation Learning from Random Data Projectors (NeurIPS'23 TRL Workshop) [[Paper]](https://arxiv.org/pdf/2310.07756.pdf) [[Code]](https://github.com/layer6ai-labs/lfr)

* SwitchTab: Switched Autoencoders Are Effective Tabular Learners (AAAI'24) [[Paper]](https://arxiv.org/pdf/2401.02013.pdf)

* Making Pre-trained Language Models Great on Tabular Prediction (ICLR'24) [[Paper]](https://openreview.net/pdf?id=anzIzGZuLi)

* Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains (ICML'24) [[Paper]](https://arxiv.org/abs/2405.07414) [[Code]](https://github.com/kyungeun-lee/tabularbinning)

* Large Scale Transfer Learning for Tabular Data via Language Modeling (NeurIPS'24) [[Paper]](https://arxiv.org/abs/2406.12031) [[Code]](https://github.com/mlfoundations/rtfm)

### Contrastive Learning

* SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption (ICLR'22) [[Paper]](https://arxiv.org/pdf/2106.15147.pdf) [[Code]](https://github.com/clabrugere/pytorch-scarf)

* STab: Self-supervised Learning for Tabular Data (NeurIPS'22 Workshop on TRL) [[Paper]](https://openreview.net/pdf?id=EfR55bFcrcI)

* TransTab: Learning Transferable Tabular Transformers Across Tables (NeurIPS'22) [[Paper]](https://arxiv.org/pdf/2205.09328.pdf)

* PTaRL: Prototype-based Tabular Representation Learning via Space Calibration (ICLR'24) [[Paper]](https://openreview.net/pdf?id=G32oY4Vnm8)

### Hybrid Learning

* SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning (NeurIPS'21) [[Paper]](https://arxiv.org/pdf/2110.04361.pdf) [[Supplementary]](https://openreview.net/attachment?id=vrhNQ7aYSdr&name=supplementary_material) [[Code]](https://github.com/AstraZeneca/SubTab)

* SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training (NurIPS‘22 Workshop on TRL) [[Paper]](https://arxiv.org/pdf/2106.01342.pdf) [[Code]](https://github.com/somepago/saint)

* Transfer Learning with Deep Tabular Models (ICLR'23) [[Paper]](https://arxiv.org/pdf/2206.15306.pdf) [[Code]](https://github.com/LevinRoman/tabular-transfer-learning)

* DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal (CIKM'23) [[Paper]](https://arxiv.org/abs/2309.00855) [[Code]](https://github.com/wwweiwei/DoRA)

* ReConTab: Regularized Contrastive Representation Learning for Tabular Data (NeurIPS'23 Workshop on TRL) [[Paper]](https://arxiv.org/pdf/2310.18541.pdf)

* XTab: Cross-table Pretraining for Tabular Transformers (ICML'23) [[Paper]](https://arxiv.org/abs/2305.06090)

* UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science (ICLR'24) [[Paper]](https://arxiv.org/pdf/2307.09249.pdf)

## Benchmarks

| Benchmark    | Task                                  | #Datasets | Paper |

|--------------|---------------------------------------|-----------|-------|

| MLPCBench    | Classification                        | 40        | [Kadra et al., 2021](https://arxiv.org/abs/2106.11189)  |

| DLBench      | Classification, Regression            | 11        | [Shwartz-Ziv and Armon, 2022](https://arxiv.org/abs/2106.03253)  |

| TabularBench | Classification, Regression            | 45        | [Grinsztajn et al., 2022](https://arxiv.org/abs/2207.08815)  |

| TabZilla     | Classification                        | 36        | [McElfresh et al., 2023](https://arxiv.org/abs/2305.02997)  |

| TabPretNet   | Unlabeled, Classification, Regression | 2000      | [Ye et al., 2023](https://arxiv.org/abs/2307.04308)  |

| The Tremendous TabLib Trawl (T4) | Unlabeled | 3.1M | [Gardner et al., 2024](https://arxiv.org/abs/2406.12031v1) |

## Tutorials

* Self-Supervised Learning: Self-Prediction and Contrastive Learning (NeurIPS'21) [[Website]](https://neurips.cc/virtual/2021/tutorial/21895)

## Workshops

* Table Representation Learning (NeurIPS) [[Website]](https://table-representation-learning.github.io/)

## Related Survey

* Deep Neural Networks and Tabular Data: A Survey [[Paper]](https://arxiv.org/abs/2110.01889)

* Self-Supervised Learning for Recommender Systems: A Survey (TKDE) [[Paper]](https://arxiv.org/pdf/2203.15876.pdf)

* Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data [[Paper]](https://arxiv.org/abs/2206.02353)

* Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects [[Paper]](https://arxiv.org/abs/2306.10125)

* On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence [[Paper]](https://arxiv.org/abs/2304.06798)

* A Survey on Time-Series Pre-Trained Models [[Paper]](https://arxiv.org/abs/2305.10716)

## Tools & Libraries

* Pytorch Frame: A modular deep learning framework for building neural network models on heterogeneous tabular data [[Link]](https://github.com/pyg-team/pytorch-frame#implemented-deep-tabular-models)

* PyTorch Tabular: A Framework for Deep Learning with Tabular Data [[Link]](https://github.com/manujosephv/pytorch_tabular)