Search Results for author: Graham Cormode

Found 11 papers, 5 papers with code

Optimal Membership Inference Bounds for Adaptive Composition of Sampled Gaussian Mechanisms

no code implementations12 Apr 2022 Saeed Mahloujifar, Alexandre Sablayrolles, Graham Cormode, Somesh Jha

A common countermeasure against MI attacks is to utilize differential privacy (DP) during model training to mask the presence of individual examples.

On the Importance of Difficulty Calibration in Membership Inference Attacks

1 code implementation ICLR 2022 Lauren Watson, Chuan Guo, Graham Cormode, Alex Sablayrolles

The vulnerability of machine learning models to membership inference attacks has received much attention in recent years.

Pruning Compact ConvNets For Efficient Inference

no code implementations29 Sep 2021 Sayan Ghosh, Karthik Prasad, Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Graham Cormode, Peter Vajda

The resulting family of pruned models can consistently obtain better performance than existing FBNetV3 models at the same level of computation, and thus provide state-of-the-art results when trading off between computational complexity and generalization performance on the ImageNet benchmark.

Network Pruning Neural Architecture Search

Opacus: User-Friendly Differential Privacy Library in PyTorch

1 code implementation25 Sep 2021 Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, Graham Cormode, Ilya Mironov

We introduce Opacus, a free, open-source PyTorch library for training deep learning models with differential privacy (hosted at https://opacus. ai).

Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streaming

no code implementations5 Apr 2021 Ziyue Huang, Yuan Qiu, Ke Yi, Graham Cormode

We study the fundamental problem of frequency estimation under both privacy and communication constraints, where the data is distributed among $k$ parties.

Iterative Hessian Sketch in Input Sparsity Time

1 code implementation30 Oct 2019 Graham Cormode, Charlie Dickens

Scalable algorithms to solve optimization and regression tasks even approximately, are needed to work with large datasets.

Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms

no code implementations ICML 2018 Charlie Dickens, Graham Cormode, David Woodruff

Work on approximate linear algebra has led to efficient distributed and streaming algorithms for problems such as approximate matrix multiplication, low rank approximation, and regression, primarily for the Euclidean norm $\ell_2$.

Learning Graphical Models from a Distributed Stream

no code implementations5 Oct 2017 Yu Zhang, Srikanta Tirthapura, Graham Cormode

We study Bayesian networks, the workhorse of graphical models, and present a communication-efficient method for continuously learning and maintaining a Bayesian network model over data that is arriving as a distributed stream partitioned across multiple processors.

Node Classification in Social Networks

1 code implementation17 Jan 2011 Smriti Bhagat, Graham Cormode, S. Muthukrishnan

When dealing with large graphs, such as those that arise in the context of online social networks, a subset of nodes may be labeled.

Social and Information Networks Physics and Society

Cannot find the paper you are looking for? You can Submit a new open access paper.