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Found 4 papers, 1 papers with code
Privacy at a Price: Exploring its Dual Impact on AI Fairness
The worldwide adoption of machine learning (ML) and deep learning models, particularly in critical sectors, such as healthcare and finance, presents substantial challenges in maintaining individual privacy and fairness.
Learn to Unlearn: A Survey on Machine Unlearning
This inspired recent research on removing the influence of specific data samples from a trained ML model.
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels
The early stopping strategy averts updating CNNs during the early training phase and is widely employed in the presence of noisy labels.
Realistic Differentially-Private Transmission Power Flow Data Release
For the modeling, design and planning of future energy transmission networks, it is vital for stakeholders to access faithful and useful power flow data, while provably maintaining the privacy of business confidentiality of service providers.
