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📰 Excited to share our new work on risk control in prediction! Multiple testing leads to practical calibration algorithms with PAC guarantees for any statistical error rate. Works with any model + data distribution!
arxiv.org/abs/2110.01052
#Statistics #MachineLearning
arxiv.org/abs/2110.01052
#Statistics #MachineLearning
arxiv.org Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees. Our calibration algorithms work with any... Thrilled to share Learn then Test, a tool to calibrate any model to control risk (eg. IOU, recall in object detection). No assns on model/data.
See arXiv arxiv.org/abs/2110.01052
+ Colab colab.research.google.com/github/aangelo…
✍️w/@stats_stephen, E.J. Candes, M.I. Jordan, @lihua_lei_stat! 🧵1/nView quoted post on X
