48 citations · 79 across the 22 of their papers we have counts for
10 papers · 1 filter
Towards Human Centered AutoML
Florian Pfisterer, Janek Thomas, Bernd Bischl
Building models from data is an integral part of the majority of data science workflows. While data scientists are often forced to spend the majority of the time available for a gi…
Benchmarking time series classification -- Functional data vs machine learning approaches
Florian Pfisterer, Laura Beggel, Xudong Sun +2
Time series classification problems have drawn increasing attention in the machine learning and statistical community. Closely related is the field of functional data analysis (FDA…
Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning
Xudong Sun, Bernd Bischl
Aiming at a comprehensive and concise tutorial survey, recap of variational inference and reinforcement learning with Probabilistic Graphical Models are given with detailed derivat…
An Open Source AutoML Benchmark
Pieter Gijsbers, Erin LeDell, Janek Thomas +3
In recent years, an active field of research has developed around automated machine learning (AutoML). Unfortunately, comparing different AutoML systems is hard and often done inco…
Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift
Xudong Sun, Alexej Gossmann, Yu Wang +1
A novel variational inference based resampling framework is proposed to evaluate the robustness and generalization capability of deep learning models with respect to distribution s…
Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning
Jann Goschenhofer, Franz MJ Pfister, Kamer Ali Yuksel +3
One major challenge in the medication of Parkinson's disease is that the severity of the disease, reflected in the patients' motor state, cannot be measured using accessible biomar…