Tabular Machine Learning

• GitHub - autogluon/tabrepo

Trees

• GitHub - catboost/catboost: A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

• GitHub - microsoft/LightGBM: A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

• GitHub - dmlc/xgboost: Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

• GitHub - sb-ai-lab/Py-Boost: Python based GBDT implementation on GPU. Efficient multioutput (multiclass/multilabel/multitask) training

  • GitHub - sb-ai-lab/sketchboost-paper: Repository to reproduce SketchBoost paper experiments

• GitHub - pyg-team/pytorch-frame: Tabular Deep Learning Library for PyTorch

• [2410.08511] Distributionally robust self-supervised learning for tabular data

LLMs

• [2410.04739] TableRAG: Million-Token Table Understanding with Language Models

OCR

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• GitHub - VikParuchuri/tabled: Detect and extract tables to markdown and csv