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Found 4 papers, 2 papers with code
Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark
Unsupervised Domain Adaptation (UDA) aims to harness labeled source data to train models for unlabeled target data.
Leveraging Adversarial Examples to Quantify Membership Information Leakage
The use of personal data for training machine learning systems comes with a privacy threat and measuring the level of privacy of a model is one of the major challenges in machine learning today.
Bounding Information Leakage in Machine Learning
We present a novel formalism, generalizing membership and attribute inference attack setups previously studied in the literature and connecting them to memorization and generalization.
Privacy-Preserving Synthetic Smart Meters Data
However, the use of power consumption data raises significant privacy concerns, as this data usually belongs to clients of a power company.
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