7 citations · 21 across the 12 of their papers we have counts for
8 papers · 1 filter
The Matching Number Characterizes the Query Complexity of Edge Counting and Degree Moment Estimation
Chenglin Fan, Wonseok Shin
Consider an unknown graph with vertex degrees , accessed only by sampling uniformly random edges and querying the degrees of their endpoints. We study the query co…
A -Approximation for Weighted Ultrametric Embedding with Outliers
Chenglin Fan
Weighted ultrametric embedding with outliers asks for a minimum weight set of points whose deletion makes the remaining metric an ultrametric, equivalently, leaves no triple with a…
Fitting Metrics and Ultrametrics with Minimum Disagreements
Vincent Cohen-Addad, Chenglin Fan, Euiwoong Lee +1
Given recording pairwise distances, the METRIC VIOLATION DISTANCE (MVD) problem asks to compute the distance between and…
-Median Clustering via Metric Embedding: Towards Better Initialization with Differential Privacy
Chenglin Fan, Ping Li, Xiaoyun Li
When designing clustering algorithms, the choice of initial centers is crucial for the quality of the learned clusters. In this paper, we develop a new initialization scheme, calle…
Breaking the Linear Error Barrier in Differentially Private Graph Distance Release
Chenglin Fan, Ping Li, Xiaoyun Li
Releasing all pairwise shortest path (APSP) distances between vertices on general graphs under weight Differential Privacy (DP) is known as a challenging task. In the previous atte…
Distances Release with Differential Privacy in Tree and Grid Graph
Chenglin Fan, Ping Li
Data about individuals may contain private and sensitive information. The differential privacy (DP) was proposed to address the problem of protecting the privacy of each individual…
Generalized Metric Repair on Graphs
Chenglin Fan, Anna C. Gilbert, Benjamin Raichel +2
Many modern data analysis algorithms either assume or are considerably more efficient if the distances between the data points satisfy a metric. These algorithms include metric lea…
Generalized Metric Repair on Graphs
Anna C. Gilbert, Rishi Sonthalia
Many modern data analysis algorithms either assume that or are considerably more efficient if the distances between the data points satisfy a metric. These algorithms include metri…