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Learning to Learn Functions
Michael Y Li1, Fred Callaway2, William D Thompson3
1Department of Computer Science, Stanford University.
Cognitive Science
|April 13, 2023
Summary
Humans learn complex functions by adjusting expectations based on experience. This study models learning-to-learn functions using hierarchical Bayesian inference on Gaussian process hyperparameters.
Area of Science:
- Cognitive Science
- Machine Learning
- Statistics
Background:
- Humans excel at learning complex functional relationships from limited data.
- Prior expectations about function forms guide human learning.
- Gaussian processes are a statistical framework for modeling human function learning.
Purpose of the Study:
- To investigate how humans adjust their expectations about function forms through experience.
- To model the process of learning to learn functions.
- To apply hierarchical Bayesian inference to Gaussian process hyperparameters.
Main Methods:
- Three experiments were conducted to observe human function learning.
- Human function learning was modeled using Gaussian processes.
- Hierarchical Bayesian inference was used to model learning-to-learn functions.
Main Results:
- People adjust their expectations about function forms based on encountered data.
- Experience leads to learning about the likely forms of functions.
- The study provides a computational model for learning-to-learn functions.
Conclusions:
- Human function learning involves adapting prior expectations through experience.
- Hierarchical Bayesian inference offers a framework for understanding learning-to-learn processes.
- This research bridges cognitive science and machine learning through advanced statistical modeling.
Keywords:
Bayesian nonparametricsFunction learningGaussian processHierarchical Bayesian modelsLearning-to-learnMore Related Videos
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