4 citations · 16 across the 13 of their papers we have counts for
14 papers
Space-Optimal Profile Estimation in Data Streams with Applications to Symmetric Functions
Justin Y. Chen, Piotr Indyk, David P. Woodruff
We revisit the problem of estimating the profile (also known as the rarity) in the data stream model. Given a sequence of elements from a universe of size , its profile is a…
VisGrader: Automatic Grading of D3 Visualizations
Matthew Hull, Vivian Pednekar, Hannah Murray +9
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…
Data Structures for Density Estimation
Anders Aamand, Alexandr Andoni, Justin Y. Chen +3
We study statistical/computational tradeoffs for the following density estimation problem: given distributions over a discrete domain of size , and sampli…
Learned Interpolation for Better Streaming Quantile Approximation with Worst-Case Guarantees
Nicholas Schiefer, Justin Y. Chen, Piotr Indyk +3
An -approximate quantile sketch over a stream of inputs approximates the rank of any query point - that is, the number of input points less than - up to an…
Improved Space Bounds for Learning with Experts
Anders Aamand, Justin Y. Chen, Huy Lê Nguyen +1
We give improved tradeoffs between space and regret for the online learning with expert advice problem over days with experts. Given a space budget of for $δ\in (0,1)…
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
Anders Aamand, Justin Y. Chen, Piotr Indyk +5
Recent work shows that the expressive power of Graph Neural Networks (GNNs) in distinguishing non-isomorphic graphs is exactly the same as that of the Weisfeiler-Lehman (WL) graph…
Streaming Algorithms for Support-Aware Histograms
Justin Y. Chen, Piotr Indyk, Tal Wagner
Histograms, i.e., piece-wise constant approximations, are a popular tool used to represent data distributions. Traditionally, the difference between the histogram and the underlyin…
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…
(Optimal) Online Bipartite Matching with Degree Information
Anders Aamand, Justin Y. Chen, Piotr Indyk
We propose a model for online graph problems where algorithms are given access to an oracle that predicts (e.g., based on modeling assumptions or on past data) the degrees of nodes…
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…