8 citations · 20 across the 16 of their papers we have counts for
4 papers · 1 filter
Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification
Kamil Adamczewski, Yingchen He, Mijung Park
Scalability is a significant challenge when it comes to applying differential privacy to training deep neural networks. The commonly used DP-SGD algorithm struggles to maintain a h…
Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion
Kamil Adamczewski, Christos Sakaridis, Vaishakh Patil +1
Lidar is a vital sensor for estimating the depth of a scene. Typical spinning lidars emit pulses arranged in several horizontal lines and the monetary cost of the sensor increases…
Differential Privacy Meets Neural Network Pruning
Kamil Adamczewski, Mijung Park
A major challenge in applying differential privacy to training deep neural network models is scalability.The widely-used training algorithm, differentially private stochastic gradi…
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation
Yilin Yang, Kamil Adamczewski, Danica J. Sutherland +2
Maximum mean discrepancy (MMD) is a particularly useful distance metric for differentially private data generation: when used with finite-dimensional features it allows us to summa…