10 citations · 15 across the 17 of their papers we have counts for
5 papers · 1 filter
Deep Distance Sensitivity Oracles
Davin Jeong, Allison Gunby-Mann, Sarel Cohen +5
One of the most fundamental graph problems is finding a shortest path from a source to a target node. While in its basic forms the problem has been studied extensively and efficien…
Deterministic Sensitivity Oracles for Diameter, Eccentricities and All Pairs Distances
Davide Bilò, Keerti Choudhary, Sarel Cohen +2
We construct data structures for extremal and pairwise distances in directed graphs in the presence of transient edge failures. Henzinger et al. [ITCS 2017] initiated the study of…
Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysis
Andrew Wood, Moshik Hershcovitch, Daniel Waddington +5
Dimensionality reduction algorithms are standard tools in a researcher's toolbox. Dimensionality reduction algorithms are frequently used to augment downstream tasks such as machin…
Fast Feature Selection with Fairness Constraints
Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch +4
We study the fundamental problem of selecting optimal features for model construction. This problem is computationally challenging on large datasets, even with the use of greedy al…
What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization
Maximilian Böther, Otto Kißig, Martin Taraz +3
Combinatorial optimization lies at the core of many real-world problems. Especially since the rise of graph neural networks (GNNs), the deep learning community has been developing…