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Found 6 papers, 4 papers with code
The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels
Kernel techniques are among the most influential approaches in data science and statistics.
Uncertainty-Aware Partial-Label Learning
In real-world applications, one often encounters ambiguously labeled data, where different annotators assign conflicting class labels.
Adaptive Bernstein Change Detector for High-Dimensional Data Streams
In high-dimensional data, change detectors should not only be able to identify when changes happen, but also in which subspace they occur.
Nyström $M$-Hilbert-Schmidt Independence Criterion
In order to alleviate the quadratic computational bottleneck in large-scale applications, multiple HSIC approximations have been proposed, however these estimators are restricted to $M=2$ random variables, do not extend naturally to the $M\ge 2$ case, and lack theoretical guarantees.
Maximum Mean Discrepancy on Exponential Windows for Online Change Detection
Detecting changes is of fundamental importance when analyzing data streams and has many applications, e. g., predictive maintenance, fraud detection, or medicine.
Efficient Subspace Search in Data Streams
In the real world, data streams are ubiquitous -- think of network traffic or sensor data.
