Fuzzy-Prototyp-Klassifikatoren und deren Anwendung zur automatischen Merkmalsselektion. - Clemens Otte (1998)

Interpretable ensembles of local models for safety-related applications. - Sebastian Nusser; Clemens Otte; Werner Hauptmann (ESANN, 2008)

Multi-Class Extension of Verifiable Ensemble Models for Safety-Related Applications. - Sebastian Nusser; Clemens Otte; Werner Hauptmann (GfKl, 2008)

An EM-Based Piecewise Linear Regression Algorithm. - Sebastian Nusser; Clemens Otte; Werner Hauptmann (HAIS, 2008)

Tackling Multiple-Instance Problems in Safety-Related Domains by Quasilinear SVM. - Christian Moewes; Clemens Otte; Rudolf Kruse (SMPS, 2008)

Ensemble Learning for Multi-source Information Fusion. - Jörg Beyer; Kai Heesche; Werner Hauptmann; Clemens Otte; Rudolf Kruse (ECSQARU, 2009)

Heterogeneous mixture-of-experts for fusion of locally valid knowledge-based submodels. - Jörg Beyer; Kai Heesche; Werner Hauptmann; Clemens Otte (ESANN, 2009)

Maschinelles Lernen von validierbaren Klassifikatoren zur autonomen Steuerung sicherheitsrelevanter Systeme (Machine Learning of Verifiable Classifiers for Autonomous Control of Safety-related Systems). - Sebastian Nusser; Clemens Otte; Werner Hauptmann; Oskar Leirich; Manfred Krätschmer; Rudolf Kruse (Autom., 2009)

Verifiable Ensembles of Low-Dimensional Submodels for Multi-class Problems with Imbalanced Misclassification Costs. - Sebastian Nusser; Clemens Otte; Werner Hauptmann (Applications of Supervised and Unsupervised Ensemble Methods, 2009)

Ensemble Learning for Multi-source Information Fusion. - Jörg Beyer; Kai Heesche; Werner Hauptmann; Clemens Otte; Rudolf Kruse (Intelligent Autonomous Systems, 2010)

Improving the accuracy of network intrusion detectors by input-dependent stacking. - Clemens Otte; Christof Störmann (Integr. Comput. Aided Eng., 2011)

Learning regression models with guaranteed error bounds. - Clemens Otte (ESANN, 2013)

Interpretable semi-parametric regression models with defined error bounds. - Clemens Otte (Neurocomputing, 2014)

Bayesian Alignments of Warped Multi-Output Gaussian Processes. - Markus Kaiser 001; Clemens Otte; Thomas A. Runkler; Carl Henrik Ek (NeurIPS, 2018)

Multimodal Deep Gaussian Processes. - Markus Kaiser 001; Clemens Otte; Thomas A. Runkler; Carl Henrik Ek (CoRR, 2018)

interpretable dynamics models for data-efficient reinforcement learning. - Markus Kaiser 001; Clemens Otte; Thomas A. Runkler; Carl Henrik Ek (ESANN, 2019)

Data Association with Gaussian Processes. - Markus Kaiser 001; Clemens Otte; Thomas A. Runkler; Carl Henrik Ek (ECML/PKDD (2), 2019)

Bayesian decomposition of multi-modal dynamical systems for reinforcement learning. - Markus Kaiser 001; Clemens Otte; Thomas A. Runkler; Carl Henrik Ek (Neurocomputing, 2020)

Safe Policy Improvement Approaches on Discrete Markov Decision Processes. - Philipp Scholl 003; Felix Dietrich; Clemens Otte; Steffen Udluft (ICAART (2), 2022)

Safe Policy Improvement Approaches and Their Limitations. - Philipp Scholl 003; Felix Dietrich; Clemens Otte; Steffen Udluft (ICAART (Revised Selected Paper, 2022)

Data-driven Verification of DNNs for Object Recognition. - Clemens Otte; Yinchong Yang; Danny Benlin Oswan (CoRR, 2024)

Neural-ANOVA: Model Decomposition for Interpretable Machine Learning. - Steffen Limmer; Steffen Udluft; Clemens Otte (CoRR, 2024)