30 citations · 48 across the 13 of their papers we have counts for
4 papers · 1 filter
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…
Kernelized Cumulants: Beyond Kernel Mean Embeddings
Patric Bonnier, Harald Oberhauser, Zoltán Szabó
In , it is well-known that cumulants provide an alternative to moments that can achieve the same goals with numerous benefits such as lower variance estimators. In thi…
Keep it Tighter -- A Story on Analytical Mean Embeddings
Linda Chamakh, Zoltan Szabo
Kernel techniques are among the most popular and flexible approaches in data science allowing to represent probability measures without loss of information under mild conditions. T…
Emotion Transfer Using Vector-Valued Infinite Task Learning
Alex Lambert, Sanjeel Parekh, Zoltán Szabó +1
Style transfer is a significant problem of machine learning with numerous successful applications. In this work, we present a novel style transfer framework building upon infinite…