4 citations · 7 across the 4 of their papers we have counts for
6 papers
Faster Fundamental Graph Algorithms via Learned Predictions
Justin Y. Chen, Sandeep Silwal, Ali Vakilian +1
We consider the question of speeding up classic graph algorithms with machine-learned predictions. In this model, algorithms are furnished with extra advice learned from past or si…
All-Pairs Shortest Path Distances with Differential Privacy: Improved Algorithms for Bounded and Unbounded Weights
Justin Y. Chen, Shyam Narayanan, Yinzhan Xu
We revisit the problem of privately releasing the all-pairs shortest path distances of a weighted undirected graph up to low additive error, which was first studied by Sealfon [Sea…
Triangle and Four Cycle Counting with Predictions in Graph Streams
Justin Y. Chen, Talya Eden, Piotr Indyk +7
We propose data-driven one-pass streaming algorithms for estimating the number of triangles and four cycles, two fundamental problems in graph analytics that are widely studied in…
Towards Automatic Grading of D3.js Visualizations
Matthew Hull, Connor Guerin, Justin Chen +2
Manually grading D3 data visualizations is a challenging endeavor, and is especially difficult for large classes with hundreds of students. Grading an interactive visualization req…
Worst-Case Analysis for Randomly Collected Data
Justin Y. Chen, Gregory Valiant, Paul Valiant
We introduce a framework for statistical estimation that leverages knowledge of how samples are collected but makes no distributional assumptions on the data values. Specifically,…
Combinatorially Generated Piecewise Activation Functions
Justin Chen
In the neuroevolution literature, research has primarily focused on evolving the number of nodes, connections, and weights in artificial neural networks. Few attempts have been mad…