Learning to Learn Functions
Michael Y. Li,Frederick Callaway,Ryan P. Adams,Thomas L. Griffiths +3 more
TL;DR: In this article , the process of learning to learn functions is modeled as a form of hierarchical Bayesian inference about the Gaussian process hyperparameters, and it is shown that people learn to adjust these expectations through experience, learning about the likely forms of the functions they will encounter.
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About: This article is published in Cognitive Science. The article was published on 01 Apr 2023. and is currently open access. The article focuses on the topics: Medicine & Computer science.
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Citations
Challenges of meta-learning and rational analysis in large worlds
Margherita Calderan,Antonino Visalli +1 more
TL;DR: This study challenges Binz et al.'s claim that meta-learned models outperform Bayesian inference for large world problems, questioning meta-learning's feature exclusivity and advocating for diverse theoretical frameworks beyond rational analysis for research advancement.
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Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek,Hugo Larochelle,Ryan P. Adams +2 more
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TL;DR: This work describes new algorithms that take into account the variable cost of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation and shows that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms.
Taking the Human Out of the Loop: A Review of Bayesian Optimization
Bobak Shahriari,Kevin Swersky,Ziyu Wang,Ryan P. Adams,Nando de Freitas +4 more
- 01 Jan 2016
TL;DR: This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.