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
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Updated
Sep 25, 2024 - C++
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
Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
Real time eye tracking for embedded and mobile devices.
Secure collaborative training and inference for XGBoost.
[ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples
[NeurIPS 2019] H. Chen*, H. Zhang*, S. Si, Y. Li, D. Boning and C.-J. Hsieh, Robustness Verification of Tree-based Models (*equal contribution)
Machine Learning Models Deployment using C++ Code Generation
counterfactual explanations for XGBoost and tree ensemble models - counterfactual reasoning - model interpretability
This project implements a common rest server which can serve tensorflow-serving & xgboost models.
Easy xgboost installation for R users (no recursive)
S-BDT: Distributed Differentially Private Boosted Decision Trees
Simple C++ interface for XGBoost(binary classification)
Auto-ML based on a coevolutionary model.
A simple stand-alone version of XGBoost named EasyXGB.
Electronic Parts Classification Top Coder Data Science Marathon Match
Predict the cost of a CMAPD assignment with XGBoost regression
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