7 citations · 9 across the 9 of their papers we have counts for
10 papers
Unbounded Characteristic and Universal Kernels
Jose Cribeiro-Ramallo, Florian Kalinke, Zoltán Szabó
Kernel methods are among the most powerful tools in machine learning and statistics, with a large number of successful applications. Their immense success stems from the flexible f…
Minimax Lower Bounds of Kernel Discrepancy Estimation: MMD, HSIC, KSD
Jose Cribeiro-Ramallo, Florian Kalinke, Zoltán Szabó
Over the past 20 years, kernel discrepancies have been leveraged as a highly powerful tool for quantifying the disagreement of distributions, with numerous successful applications…
Nyström Kernel Stein Discrepancy Tests
Florian Kalinke, Zoltán Szabó, Bharath K. Sriperumbudur
Kernel Stein discrepancy (KSD) is among the most popular goodness-of-fit (GoF) measures on general domains with a large number of successful deployments. One of the main applicatio…
Kernel Integrated : A Measure of Dependence
Pouya Roudaki, Shakeel Gavioli-Akilagun, Florian Kalinke +2
We introduce kernel integrated , a new measure of statistical dependence that combines the local normalization principle of the recently introduced integrated with the f…
The Minimax Lower Bound of Kernel Stein Discrepancy Estimation
Jose Cribeiro-Ramallo, Agnideep Aich, Florian Kalinke +2
Kernel Stein discrepancies (KSDs) have emerged as a powerful tool for quantifying goodness-of-fit over the last decade, featuring numerous successful applications. To the best of o…
Nyström Kernel Stein Discrepancy
Florian Kalinke, Zoltan Szabo, Bharath K. Sriperumbudur
Kernel methods underpin many of the most successful approaches in data science and statistics, and they allow representing probability measures as elements of a reproducing kernel…
The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels
Florian Kalinke, Zoltan Szabo
Kernel techniques are among the most influential approaches in data science and statistics. Under mild conditions, the reproducing kernel Hilbert space associated to a kernel is ca…
Nyström -Hilbert-Schmidt Independence Criterion
Florian Kalinke, Zoltán Szabó
Kernel techniques are among the most popular and powerful approaches of data science. Among the key features that make kernels ubiquitous are (i) the number of domains they have be…
Maximum Mean Discrepancy on Exponential Windows for Online Change Detection
Florian Kalinke, Marco Heyden, Georg Gntuni +2
Detecting changes is of fundamental importance when analyzing data streams and has many applications, e.g., in predictive maintenance, fraud detection, or medicine. A principled ap…
Efficient Subspace Search in Data Streams
Edouard Fouché, Florian Kalinke, Klemens Böhm
In the real world, data streams are ubiquitous -- think of network traffic or sensor data. Mining patterns, e.g., outliers or clusters, from such data must take place in real time.…