6 citations · 10 across the 5 of their papers we have counts for
5 papers
Recursive Function Definitions in Static Dataflow Graphs and their Implementation in TensorFlow
Kelly Kostopoulou, Angelos Charalambidis, Panos Rondogiannis
Modern machine learning systems represent their computations as dataflow graphs. The increasingly complex neural network architectures crave for more powerful yet efficient program…
Cookie Monster: Efficient On-device Budgeting for Differentially-Private Ad-Measurement Systems
Pierre Tholoniat, Kelly Kostopoulou, Peter McNeely +6
With the impending removal of third-party cookies from major browsers and the introduction of new privacy-preserving advertising APIs, the research community has a timely opportuni…
Turbo: Effective Caching in Differentially-Private Databases
Kelly Kostopoulou, Pierre Tholoniat, Asaf Cidon +2
Differentially-private (DP) databases allow for privacy-preserving analytics over sensitive datasets or data streams. In these systems, user privacy is a limited resource that must…
DPack: Efficiency-Oriented Privacy Budget Scheduling
Pierre Tholoniat, Kelly Kostopoulou, Mosharaf Chowdhury +4
Machine learning (ML) models can leak information about users, and differential privacy (DP) provides a rigorous way to bound that leakage under a given budget. This DP budget can…
DeepReduce: A Sparse-tensor Communication Framework for Distributed Deep Learning
Kelly Kostopoulou, Hang Xu, Aritra Dutta +3
Sparse tensors appear frequently in distributed deep learning, either as a direct artifact of the deep neural network's gradients, or as a result of an explicit sparsification proc…