DRO: A Python Library for Distributionally Robust Optimization in Machine Learning
We introduce dro, an open-source Python library for distributionally robust optimization (DRO) for regression and classification problems. The library implements 14 DRO formulations and 9 backbone models, enabling 79 distinct DRO methods. Furthermore, dro is compatible with both scikit-learn and PyTorch. Through vectorization and optimization approximation techniques, dro reduces runtime by 10x to over 1000x compared to baseline implementations on large-scale datasets. Comprehensive documentation is available at https://python-dro.org.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
$\texttt{skwdro}$: a library for Wasserstein distributionally robust machine learning
We present skwdro, a Python library for training robust machine learning models. The library is based on distributionally robust optimization using optimal transport distances. For ease of use, it features both scikit-le…
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms
Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the h…
Bayesian OptimizationBIG-bench Machine LearningHyperparameter OptimizationModel SelectionA Library for Representing Python Programs as Graphs for Machine Learning
Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static analysis to construct graph representat…
PyTorch Hyperparameter Tuning - A Tutorial for spotPython
The goal of hyperparameter tuning (or hyperparameter optimization) is to optimize the hyperparameters to improve the performance of the machine or deep learning model. spotPython (``Sequential Parameter Optimization Tool…
Hyperparameter Optimizationskfolio: Portfolio Optimization in Python
Portfolio optimization is a fundamental challenge in quantitative finance, requiring robust computational tools that integrate statistical rigor with practical implementation. We present skfolio, an open-source Python li…
ManagementPortfolio OptimizationTime Series