7 citations · 22 across the 7 of their papers we have counts for
7 papers · 1 filter
Continual and Sliding Window Release for Private Empirical Risk Minimization
Lauren Watson, Abhirup Ghosh, Benedek Rozemberczki +1
It is difficult to continually update private machine learning models with new data while maintaining privacy. Data incur increasing privacy loss -- as measured by differential pri…
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
Benedek Rozemberczki, Paul Scherer, Yixuan He +8
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of…
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
Benedek Rozemberczki, Paul Scherer, Oliver Kiss +2
Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing. Newly proposed graph neural network architectur…
The Shapley Value of Classifiers in Ensemble Games
Benedek Rozemberczki, Rik Sarkar
What is the value of an individual model in an ensemble of binary classifiers? We answer this question by introducing a class of transferable utility cooperative games called \text…
Stability Enhanced Privacy and Applications in Private Stochastic Gradient Descent
Lauren Watson, Benedek Rozemberczki, Rik Sarkar
Private machine learning involves addition of noise while training, resulting in lower accuracy. Intuitively, greater stability can imply greater privacy and improve this privacy-u…
Fast Sequence-Based Embedding with Diffusion Graphs
Benedek Rozemberczki, Rik Sarkar
A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes. Vertex sequence-based…