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pro vyhledávání: '"Ding, Hu"'
The fairness of clustering algorithms has gained widespread attention across various areas, including machine learning, In this paper, we study fair $k$-means clustering in Euclidean space. Given a dataset comprising several groups, the fairness cons
Externí odkaz:
http://arxiv.org/abs/2411.01115
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 store all the historical data, and therefore confront the ``catastrop
Externí odkaz:
http://arxiv.org/abs/2407.20956
DBSCAN is a popular density-based clustering algorithm that has many different applications in practice. However, the running time of DBSCAN in high-dimensional space or general metric space ({\em e.g.,} clustering a set of texts by using edit distan
Externí odkaz:
http://arxiv.org/abs/2405.06899
Autor:
Yang, Qingyuan, Ding, Hu
Wasserstein Barycenter (WB) is one of the most fundamental optimization problems in optimal transportation. Given a set of distributions, the goal of WB is to find a new distribution that minimizes the average Wasserstein distance to them. The proble
Externí odkaz:
http://arxiv.org/abs/2404.13401
Autor:
Xu, Xiaoyang, Ding, Hu
Optimal transport is a fundamental topic that has attracted a great amount of attention from the optimization community in the past decades. In this paper, we consider an interesting discrete dynamic optimal transport problem: can we efficiently upda
Externí odkaz:
http://arxiv.org/abs/2310.18446
Autor:
LI Jie, DING Hu
Publikováno v:
Zhongguo quanke yixue, Vol 27, Iss 36, Pp 4505-4514 (2024)
Lipoprotein (a) [Lp (a) ] is significantly related to atherosclerotic cardiovascular disease (ASCVD), but it is unclear whether clinical agents that lower Lp (a) can reduce the risk of ASCVD. Here, we systematically reviewed the structure, function,
Externí odkaz:
https://doaj.org/article/7723f39f63964f549f3f2d2c7414ae9b
Autor:
Ding, Hu
In this paper, we study several important geometric optimization problems arising in machine learning. First, we revisit the Minimum Enclosing Ball (MEB) problem in Euclidean space $\mathbb{R}^d$. The problem has been extensively studied before, but
Externí odkaz:
http://arxiv.org/abs/2301.02870
In this paper, we study the problem of {\em $k$-center clustering with outliers}. The problem has many important applications in real world, but the presence of outliers can significantly increase the computational complexity. Though a number of meth
Externí odkaz:
http://arxiv.org/abs/2301.02814
Wasserstein distributionally robust optimization (\textsf{WDRO}) is a popular model to enhance the robustness of machine learning with ambiguous data. However, the complexity of \textsf{WDRO} can be prohibitive in practice since solving its ``minimax
Externí odkaz:
http://arxiv.org/abs/2210.04260
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 computational complexity. Conventional coreset techniques assume that the
Externí odkaz:
http://arxiv.org/abs/2210.04249