2 citations · 3 across the 5 of their papers we have counts for
7 papers
Experimental investigations on Lehmer's conjecture for elliptic curves
Sven Cats, John Michael Clark, Charlotte Dombrowsky +3
In this short note, we give a method for computing a non-torsion point of smallest canonical height on a given elliptic curve over all number fields of a fixed degre…
Cycles and Cuts in Supersingular L-Isogeny Graphs
Sarah Arpin, Ross Bowden, James Clements +3
Supersingular elliptic curve isogeny graphs underlie isogeny-based cryptography. For isogenies of a single prime degree , their structure has been investigated graph-theoreti…
Machine learning for modular multiplication
Kristin Lauter, Cathy Yuanchen Li, Krystal Maughan +2
Motivated by cryptographic applications, we investigate two machine learning approaches to modular multiplication: namely circular regression and a sequence-to-sequence transformer…
Improving Utility for Privacy-Preserving Analysis of Correlated Columns using Pufferfish Privacy
Krystal Maughan, Joseph P. Near
Surveys are an important tool for many areas of social science research, but privacy concerns can complicate the collection and analysis of survey data. Differentially private anal…
Prediction Sensitivity: Continual Audit of Counterfactual Fairness in Deployed Classifiers
Krystal Maughan, Ivoline C. Ngong, Joseph P. Near
As AI-based systems increasingly impact many areas of our lives, auditing these systems for fairness is an increasingly high-stakes problem. Traditional group fairness metrics can…
Towards Auditability for Fairness in Deep Learning
Ivoline C. Ngong, Krystal Maughan, Joseph P. Near
Group fairness metrics can detect when a deep learning model behaves differently for advantaged and disadvantaged groups, but even models that score well on these metrics can make…
Towards a Measure of Individual Fairness for Deep Learning
Krystal Maughan, Joseph P. Near
Deep learning has produced big advances in artificial intelligence, but trained neural networks often reflect and amplify bias in their training data, and thus produce unfair predi…