An adaptive machine learning methodology to determine manufacturing process parameters for each part. - David Muhr; Shailesh Tripathi; Herbert Jodlbauer (ISM, 2020)

Large scale predictability analysis of process variables from injection molding machines. - Shailesh Tripathi; Christian Mittermayr; David Muhr; Herbert Jodlbauer (ISM, 2020)

Little data is often enough for distance-based outlier detection. - David Muhr; Michael Affenzeller (ISM, 2021)

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing. - Shailesh Tripathi; David Muhr; Manuel Brunner; Herbert Jodlbauer; Matthias Dehmer; Frank Emmert-Streib (Frontiers Artif. Intell., 2021)

Outlier/Anomaly Detection of Univariate Time Series: A Dataset Collection and Benchmark. - David Muhr; Michael Affenzeller (DaWaK, 2022)

Hybrid (CPU/GPU) Exact Nearest Neighbors Search in High-Dimensional Spaces. - David Muhr; Michael Affenzeller (AIAI (2), 2022)

OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming language. - David Muhr; Michael Affenzeller; Anthony D. Blaom (CoRR, 2022)

Implicit Directed Acyclic Graphs (DAGs) for Parallel Outlier/Anomaly Detection Ensembles. - David Muhr; Michael Affenzeller; Josef Küng (AIAI (2), 2023)

Probabilistic Distance-Based Outlier Detection. - David Muhr; Michael Affenzeller; Josef Küng (CoRR, 2023)

A Probabilistic Transformation of Distance-Based Outliers. - David Muhr; Michael Affenzeller; Josef Küng (Mach. Learn. Knowl. Extr., 2023)