research
∙
08/21/2023
Unlocking Accuracy and Fairness in Differentially Private Image Classification
Privacy-preserving machine learning aims to train models on private data...
share
research
∙
02/27/2023
Differentially Private Diffusion Models Generate Useful Synthetic Images
The ability to generate privacy-preserving synthetic versions of sensiti...
research
∙
04/28/2022
Unlocking High-Accuracy Differentially Private Image Classification through Scale
Differential Privacy (DP) provides a formal privacy guarantee preventing...
research
∙
01/29/2022
A Stochastic Bundle Method for Interpolating Networks
We propose a novel method for training deep neural networks that are cap...
research
∙
05/20/2021
Comment on Stochastic Polyak Step-Size: Performance of ALI-G
This is a short note on the performance of the ALI-G algorithm (Berrada ...
research
∙
02/18/2021
Verifying Probabilistic Specifications with Functional Lagrangians
We propose a general framework for verifying input-output specifications...
research
∙
06/13/2019
Training Neural Networks for and by Interpolation
The majority of modern deep learning models are able to interpolate the ...
research
∙
11/19/2018
Deep Frank-Wolfe For Neural Network Optimization
Learning a deep neural network requires solving a challenging optimizati...
research
∙
02/21/2018
Smooth Loss Functions for Deep Top-k Classification
The top-k error is a common measure of performance in machine learning a...
research
∙
11/07/2016