3 papers
cs.LG2025
Learning Hyperparameters via a Data-Emphasized Variational Objective
Ethan Harvey, Mikhail Petrov, Michael C. Hughes
When training large models on limited data, avoiding overfitting is paramount. Common grid search or smarter search methods rely on expensive separate runs for each candidate hyper…
cs.LG2024
Learning the Regularization Strength for Deep Fine-Tuning via a Data-Emphasized Variational Objective
Ethan Harvey, Mikhail Petrov, Michael C. Hughes
A number of popular transfer learning methods rely on grid search to select regularization hyperparameters that control over-fitting. This grid search requirement has several key d…
cs.LG2024
Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported
Ethan Harvey, Mikhail Petrov, Michael C. Hughes
We pursue transfer learning to improve classifier accuracy on a target task with few labeled examples available for training. Recent work suggests that using a source task to learn…