Towards a Measure of Individual Fairness for Deep Learning. - Krystal Maughan; Joseph P. Near (CoRR, 2020)

Towards Auditability for Fairness in Deep Learning. - Ivoline C. Ngong; Krystal Maughan; Joseph P. Near (CoRR, 2020)

Prediction Sensitivity: Continual Audit of Counterfactual Fairness in Deployed Classifiers. - Krystal Maughan; Ivoline C. Ngong; Joseph P. Near (CoRR, 2022)

Improving Utility for Privacy-Preserving Analysis of Correlated Columns using Pufferfish Privacy. - Krystal Maughan; Joseph P. Near (CoRR, 2022)

Machine learning for modular multiplication. - Kristin E. Lauter; Cathy Yuanchen Li; Krystal Maughan; Rachel Newton; Megha Srivastava (CoRR, 2024)

Foldable, Recursive Proofs of Isogeny Computation with Reduced Time Complexity. - Krystal Maughan; Joseph P. Near; Christelle Vincent (IACR Cryptol. ePrint Arch., 2024)

Cycles and Cuts in Supersingular L-Isogeny Graphs. - Sarah Arpin; Ross Bowden; James Clements; Wissam Ghantous; Jason T. LeGrow; Krystal Maughan (IACR Cryptol. ePrint Arch., 2025)