Closing the Loop: Machine Learning for Optimization and Discovery

Andreas Krause

International Conference on Machine Learning 2025 · Invited Talk

In this insightful talk at ICML 2025, Andreas Krause, a leading researcher in machine learning, presented a compelling vision for "Closing the Loop: Machine Learning for Optimization and Discovery." The presentation transcends the traditional linear machine learning pipeline—collecting vast datasets, fitting models, and generalizing—to advocate for a dynamic, iterative approach where ML actively drives scientific experimentation and discovery. Krause argues that while current ML advancements, from image classification to large generative models, are profound, their full potential in science remains untapped due to the inherent challenges of data scarcity and the high cost of real-world experiments.

AI review

Andreas Krause delivers a well-organized survey talk at ICML 2025 that synthesizes roughly fifteen years of work on closed-loop machine learning—Bayesian optimization, Bayesian model-based RL, physics-aware priors, and active learning for LLMs—under a unifying 'optimization and discovery' banner. The work represented across these vignettes is genuinely solid; Krause's group has produced real contributions to each subarea. But the talk itself, as reported here, is primarily a retrospective integration of prior results rather than the announcement of a new theorem, a new framework, or a new…