Inference via Machine Learning

Oregon Convention Center 

From proteomics to remote sensing, machine learning predictions are beginning to substitute for real data when collection of the latter is difficult, slow or costly. In this talk I will present recent and ongoing work that permits the use of predictions for the purpose of valid statistical inference. I will discuss the use of machine learning predictions as substitutes for high-quality data on one hand, and as a tool for guiding real data collection on the other. In both cases, machine learning allows for a significant boost in statistical power compared to "classical" baselines for inference that do not leverage prediction. Based on joint works with Anastasios Angelopoulos, Stephen Bates, Emmanuel Candes, John Duchi, Clara Fannjiang, and Michael Jordan.

machine learning

prediction-powered inference

active inference 

IMS