32 citations · 52 across the 20 of their papers we have counts for
3 papers · 1 filter
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes
Liam Hodgkinson, Chris van der Heide, Fred Roosta +1
Despite their importance for assessing reliability of predictions, uncertainty quantification (UQ) measures for machine learning models have only recently begun to be rigorously ch…
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows
Feynman Liang, Liam Hodgkinson, Michael W. Mahoney
While fat-tailed densities commonly arise as posterior and marginal distributions in robust models and scale mixtures, they present challenges when Gaussian-based variational infer…
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data
Yaoqing Yang, Ryan Theisen, Liam Hodgkinson +4
Selecting suitable architecture parameters and training hyperparameters is essential for enhancing machine learning (ML) model performance. Several recent empirical studies conduct…