D Ellis Hershkowitz
D Ellis Hershkowitz
Verified email at cs.cmu.edu - Homepage
Title
Cited by
Cited by
Year
Near optimal behavior via approximate state abstraction
D Abel, D Hershkowitz, M Littman
International Conference on Machine Learning, 2915-2923, 2016
532016
Goal-based action priors
D Abel, DE Hershkowitz, G Barth-Maron, S Brawner, K O'Farrell, ...
Twenty-Fifth International Conference on Automated Planning and Scheduling, 2015
322015
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
19*2018
Broadcasting in Noisy Radio Networks
K Censor-Hillel, B Haeupler, DE Hershkowitz, G Zuzic
arXiv preprint arXiv:1705.07369, 2017
82017
Finding options that minimize planning time
Y Jinnai, D Abel, D Hershkowitz, M Littman, G Konidaris
International Conference on Machine Learning, 3120-3129, 2019
52019
Erasure correction for noisy radio networks
K Censor-Hillel, B Haeupler, DE Hershkowitz, G Zuzic
arXiv preprint arXiv:1805.04165, 2018
52018
Learning propositional functions for planning and reinforcement learning
DE Hershkowitz, J MacGlashan, S Tellex
2015 AAAI Fall Symposium Series, 2015
22015
Reverse greedy is bad for k-center
DE Hershkowitz, G Kehne
Information Processing Letters, 105941, 2020
2020
Near-Optimal Schedules for Simultaneous Multicasts
B Haeupler, DE Hershkowitz, D Wajc
arXiv preprint arXiv:2001.00072, 2019
2019
Prepare for the Expected Worst: Algorithms for Reconfigurable Resources Under Uncertainty
DE Hershkowitz, R Ravi, S Singla
arXiv preprint arXiv:1811.11635, 2018
2018
Computation-Aware Data Aggregation
B Haeupler, DE Hershkowitz, A Kahng, AD Procaccia
arXiv preprint arXiv:1806.05701, 2018
2018
Leveraging and Learning Propositional Functions for Large State Spaces in Planning and Reinforcement Learning
DE Hershkowitz
2015
SKILL DISCOVERY WITH WELL-DEFINED OBJECTIVES
Y Jinnai, D Abel, JW Park, DE Hershkowitz, ML Littman, G Konidaris
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Articles 1–13