3 citations · 6 across the 13 of their papers we have counts for
4 papers · 1 filter
Differentially Private Learning Needs Better Model Initialization and Self-Distillation
Ivoline C. Ngong, Joseph P. Near, Niloofar Mireshghallah
Differentially private SGD (DPSGD) enables privacy-preserving training of language models, but often reduces utility, diversity, and linguistic quality. We introduce DPRefine, a th…
Backpropagation Clipping for Deep Learning with Differential Privacy
Timothy Stevens, Ivoline C. Ngong, David Darais +3
We present backpropagation clipping, a novel variant of differentially private stochastic gradient descent (DP-SGD) for privacy-preserving deep learning. Our approach clips each tr…
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…