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Found 12 papers, 3 papers with code
SEALion: a Framework for Neural Network Inference on Encrypted Data
We present SEALion: an extensible framework for privacy-preserving machine learning with homomorphic encryption.
Three Tools for Practical Differential Privacy
Differentially private learning on real-world data poses challenges for standard machine learning practice: privacy guarantees are difficult to interpret, hyperparameter tuning on private data reduces the privacy budget, and ad-hoc privacy attacks are often required to test model privacy.
Sinkhorn AutoEncoders
We show that minimizing the p-Wasserstein distance between the generator and the true data distribution is equivalent to the unconstrained min-min optimization of the p-Wasserstein distance between the encoder aggregated posterior and the prior in latent space, plus a reconstruction error.
Entity Resolution and Federated Learning get a Federated Resolution
In our experiments, we modify a simple token-based entity resolution algorithm so that it indeed aims at avoiding matching rows belonging to different classes, and perform experiments in the setting where entity resolution relies on noisy data, which is very relevant to real world domains.
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
Our results bring a clear and strong support for federated learning: under reasonable assumptions on the number and magnitude of entity resolution's mistakes, it can be extremely beneficial to carry out federated learning in the setting where each peer's data provides a significant uplift to the other.
Tsallis Regularized Optimal Transport and Ecological Inference
We also present the first application of optimal transport to the problem of ecological inference, that is, the reconstruction of joint distributions from their marginals, a problem of large interest in the social sciences.
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise.
Ranked #2 on
Image Classification
on Clothing1M (using clean data)
(using extra training data)
The Crossover Process: Learnability and Data Protection from Inference Attacks
It is usual to consider data protection and learnability as conflicting objectives.
Fast Learning from Distributed Datasets without Entity Matching
Our goal is to learn a classifier in the cross product space of the two domains, in the hard case in which no shared ID is available -- e. g. due to anonymization.
Loss factorization, weakly supervised learning and label noise robustness
We prove that the empirical risk of most well-known loss functions factors into a linear term aggregating all labels with a term that is label free, and can further be expressed by sums of the loss.
Rademacher Observations, Private Data, and Boosting
We show that rados comply with various privacy requirements that make them good candidates for machine learning in a privacy framework.
(Almost) No Label No Cry
In Learning with Label Proportions (LLP), the objective is to learn a supervised classifier when, instead of labels, only label proportions for bags of observations are known.
