D Ellis Hershkowitz
D Ellis Hershkowitz
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Cited by
Cited by
Near optimal behavior via approximate state abstraction
D Abel, D Hershkowitz, M Littman
International Conference on Machine Learning, 2915-2923, 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
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
Finding options that minimize planning time
Y Jinnai, D Abel, D Hershkowitz, M Littman, G Konidaris
International Conference on Machine Learning, 3120-3129, 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
Broadcasting in Noisy Radio Networks
K Censor-Hillel, B Haeupler, DE Hershkowitz, G Zuzic
arXiv preprint arXiv:1705.07369, 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
Erasure correction for noisy radio networks
K Censor-Hillel, B Haeupler, DE Hershkowitz, G Zuzic
arXiv preprint arXiv:1805.04165, 2018
Learning propositional functions for planning and reinforcement learning
DE Hershkowitz, J MacGlashan, S Tellex
2015 AAAI Fall Symposium Series, 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
Adaptive-Adversary-Robust Algorithms via Small Copy Tree Embeddings
B Haepler, DE Hershkowitz, G Zuzic
Leibniz International Proceedings in Informatics, LIPIcs 244, 2022
Steiner Point Removal in Series-Parallel Graphs
DE Hershkowitz, J Li
arXiv preprint arXiv:2104.00750, 2021
Near-optimal schedules for simultaneous multicasts
B Haeupler, DE Hershkowitz, D Wajc
arXiv preprint arXiv:2001.00072, 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
Reverse greedy is bad for k-center
DE Hershkowitz, G Kehne
Information Processing Letters 158, 105941, 2020
Prepare for the expected worst: Algorithms for reconfigurable resources under uncertainty
DE Hershkowitz, R Ravi, S Singla
arXiv preprint arXiv:1811.11635, 2018
Parallel Greedy Spanners
B Haeupler, DE Hershkowitz, Z Tan
arXiv preprint arXiv:2304.08892, 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
Ghost Value Augmentation for -ECSS and -ECSM
DE Hershkowitz, N Klein, R Zenklusen
arXiv preprint arXiv:2311.09941, 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
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