23 citations · 74 across the 22 of their papers we have counts for
21 papers · 1 filter
Approximation Algorithms for Directed Weighted Spanners
Elena Grigorescu, Nithish Kumar, Young-San Lin
In the pairwise weighted spanner problem, the input consists of an -vertex-directed graph, where each edge is assigned a cost and a length. Given vertex pairs and a distance…
Learning-Augmented Algorithms for Online Linear and Semidefinite Programming
Elena Grigorescu, Young-San Lin, Sandeep Silwal +2
Semidefinite programming (SDP) is a unifying framework that generalizes both linear programming and quadratically-constrained quadratic programming, while also yielding efficient s…
How to Make Your Approximation Algorithm Private: A Black-Box Differentially-Private Transformation for Tunable Approximation Algorithms of Functions with Low Sensitivity
Jeremiah Blocki, Elena Grigorescu, Tamalika Mukherjee +1
We develop a framework for efficiently transforming certain approximation algorithms into differentially-private variants, in a black-box manner. Specifically, our results focus on…
On computing Discretized Ricci curvatures of graphs: local algorithms and (localized) fine-grained reductions
Bhaskar DasGupta, Elena Grigorescu, Tamalika Mukherjee
Characterizing shapes of high-dimensional objects via Ricci curvatures plays a critical role in many research areas in mathematics and physics. However, even though several discret…
Privately Estimating Graph Parameters in Sublinear time
Jeremiah Blocki, Elena Grigorescu, Tamalika Mukherjee
We initiate a systematic study of algorithms that are both differentially private and run in sublinear time for several problems in which the goal is to estimate natural graph para…
Fixed-Parameter Algorithms for Longest Heapable Subsequence and Maximum Binary Tree
Karthekeyan Chandrasekaran, Elena Grigorescu, Gabriel Istrate +3
A heapable sequence is a sequence of numbers that can be arranged in a "min-heap data structure". Finding a longest heapable subsequence of a given sequence was proposed by Byers,…