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Found 6 papers, 2 papers with code
Harvesting Ambient RF for Presence Detection Through Deep Learning
With presence detection, how to collect training data with human presence can have a significant impact on the performance.
FOCUS: Dealing with Label Quality Disparity in Federated Learning
It maintains a small set of benchmark samples on the FL server and quantifies the credibility of the client local data without directly observing them by computing the mutual cross-entropy between performance of the FL model on the local datasets and that of the client local FL model on the benchmark dataset.
Asymptotically Optimal One- and Two-Sample Testing with Kernels
With Sanov's theorem, we derive a sufficient condition for one-sample tests to achieve the optimal error exponent in the universal setting, i. e., for any distribution defining the alternative hypothesis.
K-medoids Clustering of Data Sequences with Composite Distributions
In particular, the error exponent is characterized when either the Kolmogrov-Smirnov distance or the maximum mean discrepancy are used as the distance metric.
Exponentially Consistent Kernel Two-Sample Tests
Given two sets of independent samples from unknown distributions $P$ and $Q$, a two-sample test decides whether to reject the null hypothesis that $P=Q$.
Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit
We show that two classes of Maximum Mean Discrepancy (MMD) based tests attain this optimality on $\mathbb R^d$, while the quadratic-time Kernel Stein Discrepancy (KSD) based tests achieve the maximum exponential decay rate under a relaxed level constraint.
