A Tunable Loss Function for Binary Classification. - Tyler Sypherd; Mario Díaz; Lalitha Sankar; Peter Kairouz (ISIT, 2019)

A Tunable Loss Function for Classification. - Tyler Sypherd; Mario Díaz; Harshit Laddha; Lalitha Sankar; Peter Kairouz; Gautam Dasarathy (CoRR, 2019)

On the α-loss Landscape in the Logistic Model. - Tyler Sypherd; Mario Díaz; Lalitha Sankar; Gautam Dasarathy (ISIT, 2020)

On the alpha-loss Landscape in the Logistic Model. - Tyler Sypherd; Mario Díaz; Lalitha Sankar; Gautam Dasarathy (CoRR, 2020)

Realizing GANs via a Tunable Loss Function. - Gowtham R. Kurri; Tyler Sypherd; Lalitha Sankar (ITW, 2021)

Being Properly Improper. - Tyler Sypherd; Richard Nock; Lalitha Sankar (ICML, 2022)

α-GAN: Convergence and Estimation Guarantees. - Gowtham R. Kurri; Monica Welfert; Tyler Sypherd; Lalitha Sankar (ISIT, 2022)

AugLoss: A Learning Methodology for Real-World Dataset Corruption. - Kyle Otstot; John Kevin Cava; Tyler Sypherd; Lalitha Sankar (CoRR, 2022)

A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization. - Tyler Sypherd; Mario Díaz; John Kevin Cava; Gautam Dasarathy; Peter Kairouz; Lalitha Sankar (IEEE Trans. Inf. Theory, 2022)

Smoothly Giving up: Robustness for Simple Models. - Tyler Sypherd; Nathaniel Stromberg 001; Richard Nock; Visar Berisha; Lalitha Sankar (AISTATS, 2023)