| Quantum Annealing for Industry Applications: Introduction and Review S Yarkoni, E Raponi, T Bäck, S Schmitt Reports on Progress in Physics 85 (10), 104001, 2022 | 314 | 2022 |
| Recent Advances in Bayesian Optimization X Wang, Y Jin, S Schmitt, M Olhofer ACM Computing Surveys, 2023 | 290 | 2023 |
| Real-World Anomaly Detection by Using Digital Twin Systems and Weakly Supervised Learning A Castellani, S Schmitt, S Squartini IEEE Transactions on Industrial Informatics 17 (7), 4733-4742, 2020 | 154 | 2020 |
| Real photon scattering up to 10 MeV: the improved facility at the Darmstadt electron accelerator S-DALINAC P Mohr, J Enders, T Hartmann, H Kaiser, D Schiesser, S Schmitt, S Volz, ... Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 1999 | 152 | 1999 |
| An adaptive Bayesian approach to surrogate-assisted evolutionary multi-objective optimization X Wang, Y Jin, S Schmitt, M Olhofer Information Sciences 519, 317-331, 2020 | 133 | 2020 |
| Comparison between scattering-states numerical renormalization group and the Kadanoff-Baym-Keldysh approach to quantum transport: Crossover from weak to strong correlations S Schmitt, FB Anders Physical Review B—Condensed Matter and Materials Physics 81 (16), 165106, 2010 | 42 | 2010 |
| A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition P Wollstadt, S Schmitt, M Wibral Journal of Machine Learning Research 24 (131), 1--44, 2023 | 41 | 2023 |
| study of Ru-based perovskite and E Jakobi, S Kanungo, S Sarkar, S Schmitt, T Saha-Dasgupta Physical Review B—Condensed Matter and Materials Physics 83 (4), 041103, 2011 | 40 | 2011 |
| Transfer learning based surrogate assisted evolutionary bi-objective optimization for objectives with different evaluation times X Wang, Y Jin, S Schmitt, M Olhofer, R Allmendinger Knowledge-Based Systems 227, 107190, 2021 | 35 | 2021 |
| Non-Fermi-liquid signatures in the Hubbard model due to van Hove singularities S Schmitt Physical Review B—Condensed Matter and Materials Physics 82 (15), 155126, 2010 | 35 | 2010 |
| Conserving approximations in direct perturbation theory: new semianalytical impurity solvers and their application to general lattice problems N Grewe, S Schmitt, T Jabben, FB Anders Journal of Physics: Condensed Matter 20 (36), 365217, 2008 | 35 | 2008 |
| Nonequilibrium Zeeman splitting in quantum transport through nanoscale junctions S Schmitt, FB Anders Physical review letters 107 (5), 056801, 2011 | 31 | 2011 |
| Quantum approximate optimization algorithm for qudit systems Y Deller, S Schmitt, M Lewenstein, S Lenk, F Federer , M, Jendrzejewski, ... Phys. Rev. A 107, 062410, 2023 | 29 | 2023 |
| Transfer learning for gaussian process assisted evolutionary bi-objective optimization for objectives with different evaluation times X Wang, Y Jin, S Schmitt, M Olhofer Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 587-594, 2020 | 29 | 2020 |
| Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise A Castellani, S Schmitt, B Hammer Machine Learning and Knowledge Discovery in Databases. Research Track. (ECML …, 2021 | 26 | 2021 |
| Spectral properties of the two-impurity Anderson model with varying distance and various interactions T Jabben, N Grewe, S Schmitt Physical Review B—Condensed Matter and Materials Physics 85 (4), 045133, 2012 | 26 | 2012 |
| Task-Sensitive Concept Drift Detector with Constraint Embedding A Castellani, S Schmitt, B Hammer 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01-08, 2021 | 23 | 2021 |
| Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times X Wang, Y Jin, S Schmitt, M Olhofer Evolutionary computation 30 (2), 221-251, 2022 | 22 | 2022 |
| Kinks in the electronic dispersion of the Hubbard model away from half filling P Grete, S Schmitt, C Raas, FB Anders, GS Uhrig Physical Review B—Condensed Matter and Materials Physics 84 (20), 205104, 2011 | 21 | 2011 |
| Anomaly Detection in Univariate Time Series: An Empirical Comparison of Machine Learning Algorithms. S Däubener, S Schmitt, H Wang, P Krause, T Bäck ICDM, 161-175, 2019 | 19 | 2019 |
OlhoferHonda Research Institute EuropeVerified email at honda-ri.de
