| Near optimal behavior via approximate state abstraction D Abel, D Hershkowitz, M Littman International Conference on Machine Learning, 2915-2923, 2016 | 166 | 2016 |
| Goal-based action priors D Abel, D Hershkowitz, G Barth-Maron, S Brawner, K O'Farrell, ... Proceedings of the International Conference on Automated Planning and …, 2015 | 56 | 2015 |
| Round-and message-optimal distributed graph algorithms B Haeupler, DE Hershkowitz, D Wajc Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing …, 2018 | 44 | 2018 |
| Finding options that minimize planning time Y Jinnai, D Abel, D Hershkowitz, M Littman, G Konidaris International Conference on Machine Learning, 3120-3129, 2019 | 32 | 2019 |
| District-fair participatory budgeting DE Hershkowitz, A Kahng, D Peters, AD Procaccia Proceedings of the AAAI Conference on Artificial Intelligence 35 (6), 5464-5471, 2021 | 23 | 2021 |
| Broadcasting in Noisy Radio Networks K Censor-Hillel, B Haeupler, DE Hershkowitz, G Zuzic arXiv preprint arXiv:1705.07369, 2017 | 15 | 2017 |
| Tree embeddings for hop-constrained network design B Haeupler, DE Hershkowitz, G Zuzic Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing …, 2021 | 14 | 2021 |
| Erasure correction for noisy radio networks K Censor-Hillel, B Haeupler, DE Hershkowitz, G Zuzic arXiv preprint arXiv:1805.04165, 2018 | 12 | 2018 |
| Learning propositional functions for planning and reinforcement learning DE Hershkowitz, J MacGlashan, S Tellex 2015 AAAI Fall Symposium Series, 2015 | 6 | 2015 |
| Maximum Length-Constrained Flows and Disjoint Paths: Distributed, Deterministic, and Fast B Haeupler, DE Hershkowitz, T Saranurak Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 1371-1383, 2023 | 3* | 2023 |
| Adaptive-Adversary-Robust Algorithms via Small Copy Tree Embeddings B Haepler, DE Hershkowitz, G Zuzic Leibniz International Proceedings in Informatics, LIPIcs 244, 2022 | 3* | 2022 |
| Steiner Point Removal in Series-Parallel Graphs DE Hershkowitz, J Li arXiv preprint arXiv:2104.00750, 2021 | 3 | 2021 |
| Near-optimal schedules for simultaneous multicasts B Haeupler, DE Hershkowitz, D Wajc arXiv preprint arXiv:2001.00072, 2019 | 3 | 2019 |
| Bad-policy density: A measure of reinforcement learning hardness D Abel, C Allen, D Arumugam, DE Hershkowitz, ML Littman, LLS Wong arXiv preprint arXiv:2110.03424, 2021 | 2 | 2021 |
| Reverse greedy is bad for k-center DE Hershkowitz, G Kehne Information Processing Letters 158, 105941, 2020 | 2 | 2020 |
| Prepare for the expected worst: Algorithms for reconfigurable resources under uncertainty DE Hershkowitz, R Ravi, S Singla arXiv preprint arXiv:1811.11635, 2018 | 2 | 2018 |
| Parallel Greedy Spanners B Haeupler, DE Hershkowitz, Z Tan arXiv preprint arXiv:2304.08892, 2023 | 1 | 2023 |
| Skill Discovery with Well-Defined Objectives Y Jinnai, D Abel, JW Park, DE Hershkowitz, ML Littman, G Konidaris Proceedings of the 7th Int. Conf. on Learning Representations Workshop on …, 2019 | 1 | 2019 |
| Ghost Value Augmentation for -ECSS and -ECSM DE Hershkowitz, N Klein, R Zenklusen arXiv preprint arXiv:2311.09941, 2023 | | 2023 |
| One Tree to Rule Them All: Poly-Logarithmic Universal Steiner Tree C Busch, DQ Chen, A Filtser, D Hathcock, DE Hershkowitz, R Rajaraman arXiv preprint arXiv:2308.01199, 2023 | | 2023 |
Bernhard HaeuplerETH Zurich & Carnegie Mellon UniversityVerified email at inf.ethz.ch
