Zobrazeno 1 - 10
of 232
pro vyhledávání: '"Gleich, David F."'
Autor:
Huang, Yufan, Gleich, David F.
Semidefinite programs (SDPs) and their solvers are powerful tools with many applications in machine learning and data science. Designing scalable SDP solvers is challenging because by standard the positive semidefinite decision variable is an $n \tim
Externí odkaz:
http://arxiv.org/abs/2406.10407
Autor:
Gleich, David F.
The Eckhart-Young theorem states that the best low-rank approximation of a matrix can be constructed from the leading singular values and vectors of the matrix. Here, we illustrate that the practical implications of this result crucially depend on th
Externí odkaz:
http://arxiv.org/abs/2402.18427
Network epidemic simulation holds the promise of enabling fine-grained understanding of epidemic behavior, beyond that which is possible with coarse-grained compartmental models. Key inputs to these epidemic simulations are the networks themselves. H
Externí odkaz:
http://arxiv.org/abs/2312.17351
Dense subgraph discovery is a fundamental primitive in graph and hypergraph analysis which among other applications has been used for real-time story detection on social media and improving access to data stores of social networking systems. We prese
Externí odkaz:
http://arxiv.org/abs/2310.13792
We study a new connection between a technical measure called $\mu$-conductance that arises in the study of Markov chains for sampling convex bodies and the network community profile that characterizes size-resolved properties of clusters and communit
Externí odkaz:
http://arxiv.org/abs/2303.14550
Autor:
Huang, Yufan, Gleich, David F.
The $\mu$-conductance measure proposed by Lovasz and Simonovits is a size-specific conductance score that identifies the set with smallest conductance while disregarding those sets with volume smaller than a $\mu$ fraction of the whole graph. Using $
Externí odkaz:
http://arxiv.org/abs/2303.11452
Complex prediction models such as deep learning are the output from fitting machine learning, neural networks, or AI models to a set of training data. These are now standard tools in science. A key challenge with the current generation of models is t
Externí odkaz:
http://arxiv.org/abs/2207.14358
We study a simple embedding technique based on a matrix of personalized PageRank vectors seeded on a random set of nodes. We show that the embedding produced by the element-wise logarithm of this matrix (1) are related to the spectral embedding for a
Externí odkaz:
http://arxiv.org/abs/2207.11321
A collection of over 3000 pages of emails sent by Anthony Fauci and his staff were released in an effort to understand the United States government response to the COVID-19 pandemic. We describe how this email data was translated into a resource cons
Externí odkaz:
http://arxiv.org/abs/2108.01239