1 citations · 2 across the 6 of their papers we have counts for
6 papers
QDSB: Quantized Diffusion Schrödinger Bridges
Tobias Fuchs, Florian Kalinke, Nadja Klein
Learning generative models in settings where the source and target distributions are only specified through unpaired samples is gaining in importance. Here, one frequently-used mod…
Optimal Online Change Detection via Random Fourier Features
Florian Kalinke, Shakeel Gavioli-Akilagun
This article studies the problem of online non-parametric change point detection in multivariate data streams. We approach the problem through the lens of kernel-based two-sample t…
Robust Partial-Label Learning by Leveraging Class Activation Values
Tobias Fuchs, Florian Kalinke
Real-world training data is often noisy; for example, human annotators assign conflicting class labels to the same instances. Partial-label learning (PLL) is a weakly supervised le…
Partial-Label Learning with Conformal Candidate Cleaning
Tobias Fuchs, Florian Kalinke
Real-world data is often ambiguous; for example, human annotation produces instances with multiple conflicting class labels. Partial-label learning (PLL) aims at training a classif…
Partial-Label Learning with a Reject Option
Tobias Fuchs, Florian Kalinke, Klemens Böhm
In real-world applications, one often encounters ambiguously labeled data, where different annotators assign conflicting class labels. Partial-label learning allows training classi…
Adaptive Bernstein Change Detector for High-Dimensional Data Streams
Marco Heyden, Edouard Fouché, Vadim Arzamasov +3
Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., b…