4 papers
ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution
Liu Yang, Zeyu Nie, Andrew Liu +4
The transition from sequential to parallel computing is essential for modern high-performance applications but is hindered by the steep learning curve of concurrent programming. Th…
LAPRAS : Learning-Augmented PRivate Answering for linear query Streams
Pranay Mundra, Adam Sealfon, Ziteng Sun +1
Modern database workloads are highly predictable: query streams are dominated by recurring jobs and templates, even when their arrival order is not known in advance. This motivates…
Practical and Accurate Local Edge Differentially Private Graph Algorithms
Pranay Mundra, Charalampos Papamanthou, Julian Shun +1
The rise of massive networks across diverse domains necessitates sophisticated graph analytics, often involving sensitive data and raising privacy concerns. This paper addresses th…
Near-Optimal Differentially Private Graph Algorithms via the Multidimensional AboveThreshold Mechanism
Laxman Dhulipala, Monika Henzinger, George Z. Li +3
Many differentially private and classical non-private graph algorithms rely crucially on determining whether some property of each vertex meets a threshold. For example, for the $k…