431 citations · 727 across the 10 of their papers we have counts for
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stat.ML2018★ 22 cited
Three Tools for Practical Differential Privacy
Koen Lennart van der Veen, Ruben Seggers, Peter Bloem +1
Differentially private learning on real-world data poses challenges for standard machine learning practice: privacy guarantees are difficult to interpret, hyperparameter tuning on…
stat.ML2016★ 114 cited
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
Giorgio Patrini, Alessandro Rozza, Aditya Menon +2
We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss…