28 citations · 88 across the 15 of their papers we have counts for
18 papers
Unbounded Differentially Private Quantile and Maximum Estimation
David Durfee
In this work we consider the problem of differentially private computation of quantiles for the data, especially the highest quantiles such as maximum, but with an unbounded range…
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
Kayhan Behdin, Qingquan Song, Aman Gupta +7
Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance. The Sharpness-Aware Minimization (SAM) technique…
Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization
Kayhan Behdin, Qingquan Song, Aman Gupta +4
Modern deep learning models are over-parameterized, where the optimization setup strongly affects the generalization performance. A key element of reliable optimization for these s…
Heterogeneous Calibration: A post-hoc model-agnostic framework for improved generalization
David Durfee, Aman Gupta, Kinjal Basu
We introduce the notion of heterogeneous calibration that applies a post-hoc model-agnostic transformation to model outputs for improving AUC performance on binary classification t…
LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale
Ryan Rogers, Subbu Subramaniam, Sean Peng +5
We present a privacy system that leverages differential privacy to protect LinkedIn members' data while also providing audience engagement insights to enable marketing analytics re…
Optimal Differential Privacy Composition for Exponential Mechanisms and the Cost of Adaptivity
Jinshuo Dong, David Durfee, Ryan Rogers
Composition is one of the most important properties of differential privacy (DP), as it allows algorithm designers to build complex private algorithms from DP primitives. We consid…