University of British Columbia

SILO: Decision-Aware Models for Adaptive Experimentation and Bayesian Optimization

Abstract: Machine learning and AI models hold great promise for accelerating scientific discovery by intelligently planning experiments and learning from their results. While existing models can be applied in this setting, I will argue in this talk for decision-aware models designed explicitly for adaptive experimentation tasks rather than traditional predictive …

SILO: Automating Statistical Inference for Modern Probabilistic Models

Abstract: How do you reconstruct an image of a black hole using only noisy telescope measurements in the Fourier domain? How do you predict the cure state of a carbon fibre aircraft wing as it cures in an autoclave using a few faulty thermocouples? In theory, Bayesian probabilistic models are …