Global Capacity Measures for Deep ReLU Networks via Path Sampling. - Ryan Theisen; Jason M. Klusowski; Huan Wang 014; Nitish Shirish Keskar; Caiming Xiong; Richard Socher (CoRR, 2019)

Good linear classifiers are abundant in the interpolating regime. - Ryan Theisen; Jason M. Klusowski; Michael W. Mahoney (CoRR, 2020)

Good Classifiers are Abundant in the Interpolating Regime. - Ryan Theisen; Jason M. Klusowski; Michael W. Mahoney (AISTATS, 2021)

Evaluating State-of-the-Art Classification Models Against Bayes Optimality. - Ryan Theisen; Huan Wang 014; Lav R. Varshney; Caiming Xiong; Richard Socher (NeurIPS, 2021)

Taxonomizing local versus global structure in neural network loss landscapes. - Yaoqing Yang; Liam Hodgkinson; Ryan Theisen; Joe Zou; Joseph E. Gonzalez; Kannan Ramchandran; Michael W. Mahoney (NeurIPS, 2021)

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; Joseph E. Gonzalez; Kannan Ramchandran; Charles H. Martin; Michael W. Mahoney (CoRR, 2022)

Test Accuracy vs. Generalization Gap: Model Selection in NLP without Accessing Training or Testing Data. - Yaoqing Yang; Ryan Theisen; Liam Hodgkinson; Joseph E. Gonzalez; Kannan Ramchandran; Charles H. Martin; Michael W. Mahoney (KDD, 2023)

When are ensembles really effective? - Ryan Theisen; Hyunsuk Kim; Yaoqing Yang; Liam Hodgkinson; Michael W. Mahoney (NeurIPS, 2023)

Preference Optimization for Molecular Language Models. - Ryan Park; Ryan Theisen; Navriti Sahni; Marcel Patek; Anna Cichonska; Rayees Rahman (CoRR, 2023)

How many classifiers do we need? - Hyunsuk Kim; Liam Hodgkinson; Ryan Theisen; Michael W. Mahoney (NeurIPS, 2024)