Daniel Schneegaß

Kernel Rewards Regression: An Information Efficient Batch Policy Iteration Approach

Thomas Martinetz, Steffen Udluft, Daniel Schneegaß, 2006, Artificial Intelligence and Applications.

Uncertainty in Reinforcement Learning - Awareness, Quantisation, and Control

Steffen Udluft, Alexander Hans, Daniel Schneegass, 2010 .

Safe exploration for reinforcement learning

Steffen Udluft, Anton Maximilian Schäfer, Daniel Schneegaß, 2008, ESANN.

Uncertainty propagation for quality assurance in Reinforcement Learning

Thomas Martinetz, Steffen Udluft, Daniel Schneegaß, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

RelSen: An Optimization-based Framework for Simultaneous Sensor Reliability Monitoring and Process State Estimation pdf

Xiao Liang, Daniel Schneegass, Cheng Feng, 2020, ArXiv.

RelSen: An Optimization-based Framework for Simultaneously Sensor Reliability Monitoring and Data Cleaning

X. Liang, Cheng Feng, Daniel Schneegaß, 2020, CIKM.

Improving Optimality of Neural Rewards Regression for Data-Efficient Batch Near-Optimal Policy Identification

Thomas Martinetz, Steffen Udluft, Daniel Schneegaß, 2007, ICANN.

SoftDoubleMaxMinOver: Perceptron-Like Training of Support Vector Machines

Thomas Martinetz, Kai Labusch, Daniel Schneegaß, 2009, IEEE Transactions on Neural Networks.

A Search for the Underlying Equation Governing Similar Systems pdf

Pengwei Tian, Daniel Schneegass, Changwei Loh, 2019, ArXiv.

Neural Rewards Regression for near-optimal policy identification in Markovian and partial observable environments

Thomas Martinetz, Steffen Udluft, Daniel Schneegaß, 2007, ESANN.

MaxMinOver Regression: A Simple Incremental Approach for Support Vector Function Approximation

Thomas Martinetz, Kai Labusch, Daniel Schneegaß, 2006, ICANN.

Explicit Kernel Rewards Regression for data-efficient near-optimal policy identification

Thomas Martinetz, Steffen Udluft, Daniel Schneegaß, 2007, ESANN.