| Robustification of online graph exploration methods F Eberle, A Lindermayr, N Megow, L Nölke, J Schlöter Proceedings of the AAAI Conference on Artificial Intelligence 36 (9), 9732-9740, 2022 | 21 | 2022 |
| On the complexity of conditional DAG scheduling in multiprocessor systems A Marchetti-Spaccamela, N Megow, J Schlöter, M Skutella, L Stougie 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2020 | 18 | 2020 |
| Learning-augmented query policies for minimum spanning tree with uncertainty T Erlebach, MS de Lima, N Megow, J Schlöter arXiv preprint arXiv:2206.15201, 2022 | 11 | 2022 |
| Orienting (hyper) graphs under explorable stochastic uncertainty E Bampis, C Dürr, T Erlebach, MS de Lima, N Megow, J Schlöter arXiv preprint arXiv:2107.00572, 2021 | 8 | 2021 |
| Fully dynamic algorithms for knapsack problems with polylogarithmic update time F Eberle, N Megow, L Nölke, B Simon, A Wiese arXiv preprint arXiv:2007.08415, 2020 | 6 | 2020 |
| Improving sat solving using monte Carlo tree search-based clause learning O Keszocze, K Schmitz, J Schloeter, R Drechsler Advanced Boolean Techniques: Selected Papers from the 13th International …, 2020 | 6 | 2020 |
| A Monte Carlo tree search based conflict-driven clause learning SAT solver J Schloeter Gesellschaft für Informatik, Bonn, 2017 | 6 | 2017 |
| Throughput scheduling with equal additive laxity M Böhm, N Megow, J Schlöter Algorithms and Complexity: 12th International Conference, CIAC 2021, Virtual …, 2021 | 5 | 2021 |
| Untrusted predictions improve trustable query policies T Erlebach, M Hoffmann, MS de Lima, N Megow, J Schlöter CoRR, abs/2011.07385, 2020 | 4 | 2020 |
| Sorting and hypergraph orientation under uncertainty with predictions T Erlebach, MS de Lima, N Megow, J Schlöter arXiv preprint arXiv:2305.09245, 2023 | 3 | 2023 |
| Minimalistic predictions to schedule jobs with online precedence constraints AA Lassota, A Lindermayr, N Megow, J Schlöter International Conference on Machine Learning, 18563-18583, 2023 | 1 | 2023 |
| Set Selection Under Explorable Stochastic Uncertainty via Covering Techniques N Megow, J Schlöter International Conference on Integer Programming and Combinatorial …, 2023 | 1 | 2023 |
| Explorable Uncertainty Meets Decision-Making in Logistics N Megow, J Schlöter Dynamics in Logistics: Twenty-Five Years of Interdisciplinary Logistics …, 2021 | 1 | 2021 |
| Accelerating Matroid Optimization through Fast Imprecise Oracles F Eberle, F Hommelsheim, A Lindermayr, Z Liu, N Megow, J Schlöter arXiv preprint arXiv:2402.02774, 2024 | | 2024 |
| Santa Claus meets Makespan and Matroids: Algorithms and Reductions É Bamas, A Lindermayr, N Megow, L Rohwedder, J Schlöter Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2024 | | 2024 |
| Optimization under explorable uncertainty: beyond the worst-case J Schlöter Universität Bremen, 2023 | | 2023 |
| Dagstuhl Reports, Vol. 13, Issue 2 ISSN 2192-5283 N Megow, BJ Moseley, D Shmoys, O Svensson, S Vassilvitskii, J Schlöter, ... | | 2023 |
| Scheduling (Dagstuhl Seminar 23061) N Megow, BJ Moseley, D Shmoys, O Svensson, S Vassilvitskii, J Schlöter Dagstuhl Reports 13 (2), 2023 | | 2023 |
| Learning-Augmented Query Policies T Erlebach, MS de Lima, N Megow, J Schlöter arXiv preprint arXiv:2011.07385, 2020 | | 2020 |
| Conditional Directed Acyclic Graphs: On the Complexity of Computing the Worst-Case Execution Time J Schlöter University of Bremen, 2019 | | 2019 |
