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06/28/2023
Boost: Effective Caching in Differentially-Private Databases
Differentially private (DP) databases can enable privacy-preserving anal...
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04/21/2023
DP-Adam: Correcting DP Bias in Adam's Second Moment Estimation
We observe that the traditional use of DP with the Adam optimizer introd...
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12/26/2022
Packing Privacy Budget Efficiently
Machine learning (ML) models can leak information about users, and diffe...
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12/03/2022
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning
Federated learning (FL) is an effective technique to directly involve ed...
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10/13/2022
Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design
Sparse decision trees are one of the most common forms of interpretable ...
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06/20/2022
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
We develop a new, principled algorithm for estimating the contribution o...
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10/28/2021
Sayer: Using Implicit Feedback to Optimize System Policies
We observe that many system policies that make threshold decisions invol...
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06/29/2021
Privacy Budget Scheduling
Machine learning (ML) models trained on personal data have been shown to...
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03/02/2021
Practical Privacy Filters and Odometers with Rényi Differential Privacy and Applications to Differentially Private Deep Learning
Differential Privacy (DP) is the leading approach to privacy preserving ...
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09/04/2019
Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform
Companies increasingly expose machine learning (ML) models trained over ...
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02/09/2018
