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cs.LG2024
The Lie Derivative for Measuring Learned Equivariance
Nate Gruver, Marc Finzi, Micah Goldblum +1
Equivariance guarantees that a model's predictions capture key symmetries in data. When an image is translated or rotated, an equivariant model's representation of that image will…
cs.LG2024
The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning
Micah Goldblum, Marc Finzi, Keefer Rowan +1
No free lunch theorems for supervised learning state that no learner can solve all problems or that all learners achieve exactly the same accuracy on average over a uniform distrib…
cs.LG2024
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson
Bayesian optimization is a highly efficient approach to optimizing objective functions which are expensive to query. These objectives are typically represented by Gaussian process…