3 papers
stat.ML2026
Measuring consistency via ensemble margin and local prediction variability: Auditing decision systems in the presence of predictive multiplicity
Sinjini Banerjee, Tim Marrinan, Anand D. Sarwate
The Rashomon effect is a machine learning phenomenon where equally accurate models produce different predictions for the same inputs (predictive multiplicity). Existing work primar…
cs.LG2024
Understanding Generative AI Content with Embedding Models
Max Vargas, Reilly Cannon, Andrew Engel +2
Constructing high-quality features is critical to any quantitative data analysis. While feature engineering was historically addressed by carefully hand-crafting data representatio…
stat.ML2024
Measuring training variability from stochastic optimization using robust nonparametric testing
Sinjini Banerjee, Tim Marrinan, Reilly Cannon +2
Deep neural network training often involves stochastic optimization, meaning each run will produce a different model. This implies that hyperparameters of the training process, suc…