Data-driven exploration and continuum modeling of dislocation networks M Sudmanns, J Bach, D Weygand, K Schulz Modelling and Simulation in Materials Science and Engineering 28 (6), 2020 | 15 | 2020 |
On the Tradeoff between Energy Data Aggregation and Clustering Quality H Trittenbach, J Bach, K Böhm e-Energy 2018, 399-401, 2018 | 5 | 2018 |
Analyzing and Predicting Verification of Data-Aware Process Models–A Case Study With Spectrum Auctions E Ordoni, J Bach, AK Fleck IEEE Access 10, 31699-31713, 2022 | 3 | 2022 |
Active Learning for SAT Solver Benchmarking T Fuchs, J Bach, M Iser TACAS 2023, 407-425, 2023 | 2 | 2023 |
An Empirical Evaluation of Constrained Feature Selection J Bach, K Zoller, H Trittenbach, K Schulz, K Böhm SN Computer Science 3 (6), 2022 | 2 | 2022 |
A Comprehensive Study of k-Portfolios of Recent SAT Solvers J Bach, M Iser, K Böhm SAT 2022, 2:1-2:18, 2022 | 2 | 2022 |
Understanding the effects of temporal energy-data aggregation on clustering quality H Trittenbach, J Bach, K Böhm it-Information Technology 61 (2-3), 111-123, 2019 | 2 | 2019 |
Alternative feature selection with user control J Bach, K Böhm International Journal of Data Science and Analytics, 2024 | 1 | 2024 |
Finding Optimal Diverse Feature Sets with Alternative Feature Selection J Bach arXiv:2307.11607 [cs.LG], 2023 | 1 | 2023 |
Using Constraints to Discover Sparse and Alternative Subgroup Descriptions J Bach arXiv:2406.01411 [cs.LG], 2024 | | 2024 |
Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines P Bielski, A Eismont, J Bach, F Leiser, D Kottonau, K Böhm e-Energy 2024, 279-290, 2024 | | 2024 |
Leveraging Constraints for User-Centric Selection of Predictive Features J Bach AI Hub @ Karlsruhe (2022), 2022 | | 2022 |