1 citations · 1 across the 14 of their papers we have counts for
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On Simplifying Large-Scale Spatial Vectors: Fast, Memory-Efficient, and Cost-Predictable k-means
Yushuai Ji, Zepeng Liu, Sheng Wang +2
The k-means algorithm can simplify large-scale spatial vectors, such as 2D geo-locations and 3D point clouds, to support fast analytics and learning. However, when processing large…
Budgeted Spatial Data Acquisition: When Coverage and Connectivity Matter
Wenzhe Yang, Shixun Huang, Sheng Wang +1
Data is undoubtedly becoming a commodity like oil, land, and labor in the 21st century. Although there have been many successful marketplaces for data trading, the existing data ma…
A Unified Approach for Multi-Granularity Search over Spatial Datasets
Wenzhe Yang, Sheng Wang, Shixun Huang +4
There has been increased interest in data search as a means to find relevant datasets or data points in data lakes and repositories. Although approaches have been proposed to suppo…
Approximate Vector Set Search Inspired by Fly Olfactory Neural System
Yiqi Li, Sheng Wang, Zhiyu Chen +2
Vector set search, an underexplored similarity search paradigm, aims to find vector sets similar to a query set. This search paradigm leverages the inherent structural alignment be…
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness
Shengkun Zhu, Jinshan Zeng, Sheng Wang +4
Statistical heterogeneity is a root cause of tension among accuracy, fairness, and robustness of federated learning (FL), and is key in paving a path forward. Personalized FL (PFL)…
Efficient k-means with Individual Fairness via Exponential Tilting
Shengkun Zhu, Jinshan Zeng, Yuan Sun +3
In location-based resource allocation scenarios, the distances between each individual and the facility are desired to be approximately equal, thereby ensuring fairness. Individual…