4 citations · 12 across the 7 of their papers we have counts for
6 papers · 1 filter
An Effective Dynamic Gradient Calibration Method for Continual Learning
Weichen Lin, Jiaxiang Chen, Ruomin Huang +1
Continual learning (CL) is a fundamental topic in machine learning, where the goal is to train a model with continuously incoming data and tasks. Due to the memory limit, we cannot…
Randomized Greedy Algorithms and Composable Coreset for k-Center Clustering with Outliers
Hu Ding, Ruomin Huang, Kai Liu +2
In this paper, we study the problem of {\em -center clustering with outliers}. The problem has many important applications in real world, but the presence of outliers can signif…
Coresets for Relational Data and The Applications
Jiaxiang Chen, Qingyuan Yang, Ruomin Huang +1
A coreset is a small set that can approximately preserve the structure of the original input data set. Therefore we can run our algorithm on a coreset so as to reduce the total com…
Coresets for Wasserstein Distributionally Robust Optimization Problems
Ruomin Huang, Jiawei Huang, Wenjie Liu +1
Wasserstein distributionally robust optimization (\textsf{WDRO}) is a popular model to enhance the robustness of machine learning with ambiguous data. However, the complexity of \t…
A Novel Sequential Coreset Method for Gradient Descent Algorithms
Jiawei Huang, Ruomin Huang, Wenjie Liu +2
A wide range of optimization problems arising in machine learning can be solved by gradient descent algorithms, and a central question in this area is how to efficiently compress a…
Generating a Doppelganger Graph: Resembling but Distinct
Yuliang Ji, Ru Huang, Jie Chen +1
Deep generative models, since their inception, have become increasingly more capable of generating novel and perceptually realistic signals (e.g., images and sound waves). With the…