125 citations · 311 across the 20 of their papers we have counts for
7 papers · 1 filter
Joint Coreset Construction and Quantization for Distributed Machine Learning
Hanlin Lu, Changchang Liu, Shiqiang Wang +4
Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication…
Communication-efficient k-Means for Edge-based Machine Learning
Hanlin Lu, Ting He, Shiqiang Wang +5
We consider the problem of computing the k-means centers for a large high-dimensional dataset in the context of edge-based machine learning, where data sources offload machine lear…
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
Todd Huster, Jeremy E. J. Cohen, Zinan Lin +5
Generative adversarial networks (GANs) are often billed as "universal distribution learners", but precisely what distributions they can represent and learn is still an open questio…
Sharing Models or Coresets: A Study based on Membership Inference Attack
Hanlin Lu, Changchang Liu, Ting He +2
Distributed machine learning generally aims at training a global model based on distributed data without collecting all the data to a centralized location, where two different appr…
Topological Effects on Attacks Against Vertex Classification
Benjamin A. Miller, Mustafa Çamurcu, Alexander J. Gomez +2
Vertex classification is vulnerable to perturbations of both graph topology and vertex attributes, as shown in recent research. As in other machine learning domains, concerns about…
GLEE: Geometric Laplacian Eigenmap Embedding
Leo Torres, Kevin S Chan, Tina Eliassi-Rad
Graph embedding seeks to build a low-dimensional representation of a graph G. This low-dimensional representation is then used for various downstream tasks. One popular approach is…
Robust Coreset Construction for Distributed Machine Learning
Hanlin Lu, Ming-Ju Li, Ting He +3
Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over dist…