Semi-Automated Software Composition Through Generated Components. - Felix Mohr; Hans Kleine Büning (iiWAS, 2013)

Reducing Nondeterminism in Automated Service Composition. - Felix Mohr; Theodor Lettmann; Hans Kleine Büning (SOCA, 2013)

Estimating Functional Reusability of Services. - Felix Mohr (ICSOC, 2014)

Issues of automated software composition in AI planning. - Felix Mohr (ASE, 2014)

Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services. - Alexander Jungmann; Felix Mohr; Bernd Kleinjohann (SERVICES, 2014)

Applying Reinforcement Learning for Resolving Ambiguity in Service Composition. - Alexander Jungmann; Felix Mohr; Bernd Kleinjohann (SOCA, 2014)

Automated Online Service Composition. - Felix Mohr; Alexander Jungmann; Hans Kleine Büning (SCC, 2015)

A Metric for Functional Reusability of Services. - Felix Mohr (ICSR, 2015)

Template-Based Generation of Semantic Services. - Felix Mohr; Sven Walther (ICSR, 2015)

Market-Specific Service Compositions: Specification and Matching. - Svetlana Arifulina; Felix Mohr; Gregor Engels; Marie Christin Platenius; Wilhelm Schäfer (SERVICES, 2015)

An approach towards adaptive service composition in markets of composed services. - Alexander Jungmann; Felix Mohr (J. Internet Serv. Appl., 2015)

Automated Software and Service Composition - A Survey and Evaluating Review - Felix Mohr (Springer, 2016)

Planning with Independent Task Networks. - Felix Mohr; Theo Lettmann; Eyke Hüllermeier (KI, 2017)

Towards automated service composition under quality constraints - Felix Mohr (2017)

On-the-Fly Service Construction with Prototypes. - Felix Mohr; Marcel Wever; Eyke Hüllermeier (SCC, 2018)

(WIP) Towards the Automated Composition of Machine Learning Services. - Felix Mohr; Marcel Wever; Eyke Hüllermeier; Amin Faez (SCC, 2018)

Ensembles of evolved nested dichotomies for classification. - Marcel Wever; Felix Mohr; Eyke Hüllermeier (GECCO, 2018)

Reduction Stumps for Multi-class Classification. - Felix Mohr; Marcel Wever; Eyke Hüllermeier (IDA, 2018)

Automated Machine Learning Service Composition. - Felix Mohr; Marcel Wever; Eyke Hüllermeier (CoRR, 2018)

Automated Multi-Label Classification based on ML-Plan. - Marcel Wever; Felix Mohr; Eyke Hüllermeier (CoRR, 2018)

ML-Plan: Automated machine learning via hierarchical planning. - Felix Mohr; Marcel Wever; Eyke Hüllermeier (Mach. Learn., 2018)

From Automated to On-The-Fly Machine Learning. - Felix Mohr; Marcel Wever; Alexander Tornede; Eyke Hüllermeier (GI-Jahrestagung, 2019)

Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. - Alexander Tornede; Marcel Wever; Stefan Werner; Felix Mohr; Eyke Hüllermeier (ACML, 2020)

LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification. - Marcel Wever; Alexander Tornede; Felix Mohr; Eyke Hüllermeier (IDA, 2020)

AutoML for Predictive Maintenance: One Tool to RUL Them All. - Tanja Tornede; Alexander Tornede; Marcel Wever; Felix Mohr; Eyke Hüllermeier (IoT Streams/ITEM@PKDD/ECML, 2020)

Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model. - Felix Mohr; Viktor Bengs; Eyke Hüllermeier (AAAI, 2021)

Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification. - Adrian El Baz; Ihsan Ullah; Edesio Alcobaça; André C. P. L. F. de Carvalho; Hong Chen 011; Fabio Ferreira; Henry Gouk; Chaoyu Guan; Isabelle Guyon; Timothy M. Hospedales; Shell Hu; Mike Huisman; et al. (NeurIPS (Competition and Demos), 2021)

NeurIPS’22 Cross-Domain MetaDL Challenge: Results and lessons learned. - Dustin Carrión-Ojeda; Mahbubul Alam; Sergio Escalera; Ahmed K. Farahat; Dipanjan Ghosh; Maria Teresa Gonzalez Diaz; Chetan Gupta 001; Isabelle Guyon; Joël Roman Ky; Xian Yeow Lee; Xin Liu; Felix Mohr; et al. (NeurIPS (Competition and Demos), 2021)

Single and Parallel Machine Scheduling with Variable Release Dates. - Felix Mohr; Gonzalo Mejía; Francisco Yuraszeck (CoRR, 2021)

Naive Automated Machine Learning - A Late Baseline for AutoML. - Felix Mohr; Marcel Wever (CoRR, 2021)

Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. - Eyke Hüllermeier; Felix Mohr; Alexander Tornede; Marcel Wever (CoRR, 2021)

Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. - Felix Mohr; Marcel Wever; Alexander Tornede; Eyke Hüllermeier (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

AutoML for Multi-Label Classification: Overview and Empirical Evaluation. - Marcel Wever; Alexander Tornede; Felix Mohr; Eyke Hüllermeier (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Advances in Metalearning: ECML/PKDD Workshop on Meta-Knowledge Transfer. - Pavel Brazdil; Jan N. van Rijn; Henry Gouk; Felix Mohr (Meta-Knowledge Transfer @ ECML/PKDD, 2022)

Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification. - Ihsan Ullah; Dustin Carrión-Ojeda; Sergio Escalera; Isabelle Guyon; Mike Huisman; Felix Mohr; Jan N. van Rijn; Haozhe Sun; Joaquin Vanschoren; Phan Anh Vu (NeurIPS, 2022)

LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks. - Felix Mohr; Tom J. Viering; Marco Loog; Jan N. van Rijn (ECML/PKDD (5), 2022)

Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification. - Adrian El Baz; André C. P. L. F. de Carvalho; Hong Chen 011; Fabio Ferreira; Henry Gouk; Shell Hu; Frank Hutter; Zhengying Liu; Felix Mohr; Jan N. van Rijn; Xin Wang 019; Isabelle Guyon (CoRR, 2022)

RRR-Net: Reusing, Reducing, and Recycling a Deep Backbone Network. - Haozhe Sun; Isabelle Guyon; Felix Mohr; Hedi Tabia (IJCNN, 2023)

Towards Green Automated Machine Learning: Status Quo and Future Directions. - Tanja Tornede; Alexander Tornede; Jonas Hanselle; Felix Mohr; Marcel Wever; Eyke Hüllermeier (J. Artif. Intell. Res., 2023)

Naive automated machine learning. - Felix Mohr; Marcel Wever (Mach. Learn., 2023)

PyExperimenter: Easily distribute experiments and track results. - Tanja Tornede; Alexander Tornede; Lukas Fehring; Lukas Gehring; Helena Graf; Jonas Hanselle; Felix Mohr; Marcel Wever (J. Open Source Softw., 2023)

Fast and Informative Model Selection Using Learning Curve Cross-Validation. - Felix Mohr; Jan N. van Rijn (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Learning Curve Extrapolation Methods Across Extrapolation Settings. - Lionel Kielhöfer; Felix Mohr; Jan N. van Rijn (IDA (2), 2024)

Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals. - Till Aust; Eduard Buss; Felix Mohr; Heiko Hamann (CoRR, 2024)

The unreasonable effectiveness of early discarding after one epoch in neural network hyperparameter optimization. - Romain Egele; Felix Mohr; Tom J. Viering; Prasanna Balaprakash (Neurocomputing, 2024)

DARIO: Differentiable Vision Transformer Pruning With Low-Cost Proxies. - Haozhe Sun; Alexandre Heuillet; Felix Mohr; Hedi Tabia (IEEE J. Sel. Top. Signal Process., 2024)

Learning curves for decision making in supervised machine learning: a survey. - Felix Mohr; Jan N. van Rijn (Mach. Learn., 2024)

LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously Thought. - Cheng Yan; Felix Mohr; Tom J. Viering (CoRR, 2025)

Credal Prediction based on Relative Likelihood. - Timo Löhr; Paul Hofman; Felix Mohr; Eyke Hüllermeier (CoRR, 2025)

HKT: A Biologically Inspired Framework for Modular Hereditary Knowledge Transfer in Neural Networks. - Yanick Christian Tchenko; Felix Mohr; Hicham Hadj-Abdelkader; Hedi Tabia (CoRR, 2025)