D. Ellis Hershkowitz

According to our database, D. Ellis Hershkowitz authored at least 26 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Ghost Value Augmentation for k-ECSS and k-ECSM.

D. Ellis Hershkowitz, Nathan Klein, Rico Zenklusen

CoRR, 2023

Parallel Greedy Spanners.

Bernhard Haeupler, D. Ellis Hershkowitz, Zihan Tan

CoRR, 2023

Maximum Length-Constrained Flows and Disjoint Paths: Distributed, Deterministic, and Fast.

Bernhard Haeupler, D. Ellis Hershkowitz, Thatchaphol Saranurak

Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

One Tree to Rule Them All: Poly-Logarithmic Universal Steiner Tree.

Costas Busch, Da Qi Chen, Arnold Filtser, Daniel Hathcock, D. Ellis Hershkowitz, Rajmohan Rajaraman

Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

2022

O(1) Steiner Point Removal in Series-Parallel Graphs.

D. Ellis Hershkowitz, Jason Li

Proceedings of the 30th Annual European Symposium on Algorithms, 2022

Adaptive-Adversary-Robust Algorithms via Small Copy Tree Embeddings.

Bernhard Haepler, D. Ellis Hershkowitz, Goran Zuzic

Proceedings of the 30th Annual European Symposium on Algorithms, 2022

2021

An optimal rounding for half-integral weighted minimum strongly connected spanning subgraph.

D. Ellis Hershkowitz, Gregory Kehne, R. Ravi

Inf. Process. Lett., 2021

Fast Algorithms for Hop-Constrained Flows and Moving Cuts.

Bernhard Haeupler, D. Ellis Hershkowitz, Thatchaphol Saranurak

CoRR, 2021

Bad-Policy Density: A Measure of Reinforcement Learning Hardness.

David Abel, Cameron Allen, Dilip Arumugam, D. Ellis Hershkowitz, Michael L. Littman, Lawson L. S. Wong

CoRR, 2021

Deterministic Tree Embeddings with Copies for Algorithms Against Adaptive Adversaries.

Bernhard Haeupler, D. Ellis Hershkowitz, Goran Zuzic

CoRR, 2021

Tree embeddings for hop-constrained network design.

Bernhard Haeupler, D. Ellis Hershkowitz, Goran Zuzic

Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

Near-Optimal Schedules for Simultaneous Multicasts.

Bernhard Haeupler, D. Ellis Hershkowitz, David Wajc

Proceedings of the 48th International Colloquium on Automata, Languages, and Programming, 2021

District-Fair Participatory Budgeting.

D. Ellis Hershkowitz, Anson Kahng, Dominik Peters, Ariel D. Procaccia

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Reverse greedy is bad for <i>k</i>-center.

D. Ellis Hershkowitz, Gregory Kehne

Inf. Process. Lett., 2020

Computation-Aware Data Aggregation.

Bernhard Haeupler, D. Ellis Hershkowitz, Anson Kahng, Ariel D. Procaccia

Proceedings of the 11th Innovations in Theoretical Computer Science Conference, 2020

2019

Erasure Correction for Noisy Radio Networks.

Keren Censor-Hillel, Bernhard Haeupler, D. Ellis Hershkowitz, Goran Zuzic

Proceedings of the 33rd International Symposium on Distributed Computing, 2019

Finding Options that Minimize Planning Time.

Yuu Jinnai, David Abel, David Ellis Hershkowitz, Michael L. Littman, George Dimitri Konidaris

Proceedings of the 36th International Conference on Machine Learning, 2019

Prepare for the Expected Worst: Algorithms for Reconfigurable Resources Under Uncertainty.

David Ellis Hershkowitz, R. Ravi, Sahil Singla

Proceedings of the Approximation, 2019

2018

Reverse Greedy is Bad for k-Center.

D. Ellis Hershkowitz, Gregory Kehne

CoRR, 2018

A Computational Approach to Organizational Structure.

Bernhard Haeupler, D. Ellis Hershkowitz, Anson Kahng, Ariel D. Procaccia

CoRR, 2018

Round- and Message-Optimal Distributed Part-Wise Aggregation.

Bernhard Haeupler, D. Ellis Hershkowitz, David Wajc

CoRR, 2018

Round- and Message-Optimal Distributed Graph Algorithms.

Bernhard Haeupler, D. Ellis Hershkowitz, David Wajc

Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing, 2018

2017

Broadcasting in Noisy Radio Networks.

Keren Censor-Hillel, Bernhard Haeupler, D. Ellis Hershkowitz, Goran Zuzic

Proceedings of the ACM Symposium on Principles of Distributed Computing, 2017

2016

Near Optimal Behavior via Approximate State Abstraction.

David Abel, D. Ellis Hershkowitz, Michael L. Littman

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Goal-Based Action Priors.

David Abel, D. Ellis Hershkowitz, Gabriel Barth-Maron, Stephen Brawner, Kevin O'Farrell, James MacGlashan, Stefanie Tellex

Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling, 2015

Learning Propositional Functions for Planning and Reinforcement Learning.

David Ellis Hershkowitz, James MacGlashan, Stefanie Tellex

Proceedings of the 2015 AAAI Fall Symposia, Arlington, Virginia, USA, November 12-14, 2015, 2015