1 citations · 1 across the 4 of their papers we have counts for
5 papers
Geometric Kernel Diversity: An Axiomatic Diversity Measure in Euclidean Space
Ditbul Ban, Younghun Roh, Julian Shun +1
Quantifying the diversity of a set of objects is a fundamental problem in machine learning and statistics. Mironov and Prokhorenkova (2025) recently showed that three axioms-monoto…
Breaking the Curse of Dimensionality: On the Stability of Modern Vector Retrieval
Vihan Lakshman, Blaise Munyampirwa, Julian Shun +1
Modern vector databases enable efficient retrieval over high-dimensional neural embeddings, powering applications from web search to retrieval-augmented generation. However, classi…
Practical and Accurate Local Edge Differentially Private Graph Algorithms
Pranay Mundra, Charalampos Papamanthou, Julian Shun +1
The rise of massive networks across diverse domains necessitates sophisticated graph analytics, often involving sensitive data and raising privacy concerns. This paper addresses th…
Faster Parallel Triangular Maximally Filtered Graphs and Hierarchical Clustering
Steven Raphael, Julian Shun
Filtered graphs provide a powerful tool for data clustering. The triangular maximally filtered graph (TMFG) method, when combined with the directed bubble hierarchy tree (DBHT) met…
Kairos: Efficient Temporal Graph Analytics on a Single Machine
Joana M. F. da Trindade, Julian Shun, Samuel Madden +1
Many important societal problems are naturally modeled as algorithms over temporal graphs. To date, however, most graph processing systems remain inefficient as they rely on distri…