48 citations · 74 across the 13 of their papers we have counts for
10 papers · 1 filter
Mutation is all you need
Lennart Schneider, Florian Pfisterer, Martin Binder +1
Neural architecture search (NAS) promises to make deep learning accessible to non-experts by automating architecture engineering of deep neural networks. BANANAS is one state-of-th…
Semi-Structured Deep Piecewise Exponential Models
Philipp Kopper, Sebastian Pölsterl, Christian Wachinger +3
We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning. The presented framework is based on piecewise exponential…
Debiasing classifiers: is reality at variance with expectation?
Ashrya Agrawal, Florian Pfisterer, Bernd Bischl +5
We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample, and can in fact make fairness worse r…
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…