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Found 3 papers, 1 papers with code
Bagged Regularized $k$-Distances for Anomaly Detection
We consider the paradigm of unsupervised anomaly detection, which involves the identification of anomalies within a dataset in the absence of labeled examples.
Optimal Locally Private Nonparametric Classification with Public Data
In this work, we investigate the problem of public data-assisted non-interactive LDP (Local Differential Privacy) learning with a focus on non-parametric classification.
Frequency Principle in Deep Learning Beyond Gradient-descent-based Training
Frequency perspective recently makes progress in understanding deep learning.
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