173 citations · 256 across the 17 of their papers we have counts for
6 papers · 1 filter
Model-specific Data Subsampling with Influence Functions
Anant Raj, Cameron Musco, Lester Mackey +1
Model selection requires repeatedly evaluating models on a given dataset and measuring their relative performances. In modern applications of machine learning, the models being con…
Cross-validation Confidence Intervals for Test Error
Pierre Bayle, Alexandre Bayle, Lucas Janson +1
This work develops central limit theorems for cross-validation and consistent estimators of its asymptotic variance under weak stability conditions on the learning algorithm. Toget…
Stochastic Stein Discrepancies
Jackson Gorham, Anant Raj, Lester Mackey
Stein discrepancies (SDs) monitor convergence and non-convergence in approximate inference when exact integration and sampling are intractable. However, the computation of a Stein…
Minimax Estimation of Conditional Moment Models
Nishanth Dikkala, Greg Lewis, Lester Mackey +1
We develop an approach for estimating models described via conditional moment restrictions, with a prototypical application being non-parametric instrumental variable regression. W…
Weighted Meta-Learning
Diana Cai, Rishit Sheth, Lester Mackey +1
Meta-learning leverages related source tasks to learn an initialization that can be quickly fine-tuned to a target task with limited labeled examples. However, many popular meta-le…
Approximate Cross-validation: Guarantees for Model Assessment and Selection
Ashia Wilson, Maximilian Kasy, Lester Mackey
Cross-validation (CV) is a popular approach for assessing and selecting predictive models. However, when the number of folds is large, CV suffers from a need to repeatedly refit a…