4 papers
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…
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…
Robust Nonparametric Hypothesis Testing to Understand Variability in Training Neural Networks
Sinjini Banerjee, Reilly Cannon, Tim Marrinan +2
Training a deep neural network (DNN) often involves stochastic optimization, which means each run will produce a different model. Several works suggest this variability is negligib…
Robust Hierarchical-Optimization RLS Against Sparse Outliers
Konstantinos Slavakis, Sinjini Banerjee
This paper fortifies the recently introduced hierarchical-optimization recursive least squares (HO-RLS) against outliers which contaminate infrequently linear-regression models. Ou…