5 citations · 8 across the 6 of their papers we have counts for
6 papers
CKGAN: Training Generative Adversarial Networks Using Characteristic Kernel Integral Probability Metrics
Kuntian Zhang, Simin Yu, Yaoshu Wang +2
In this paper, we propose CKGAN, a novel generative adversarial network (GAN) variant based on an integral probability metrics framework with characteristic kernel (CKIPM). CKIPM,…
"Guinea Pig Trials" Utilizing GPT: A Novel Smart Agent-Based Modeling Approach for Studying Firm Competition and Collusion
Xu Han, Zengqing Wu, Chuan Xiao
Firm competition and collusion involve complex dynamics, particularly when considering communication among firms. Such issues can be modeled as problems of complex systems, traditi…
Scardina: Scalable Join Cardinality Estimation by Multiple Density Estimators
Ryuichi Ito, Yuya Sasaki, Chuan Xiao +1
In recent years, machine learning-based cardinality estimation methods are replacing traditional methods. This change is expected to contribute to one of the most important applica…
Ultraverse: A System-Centric Framework for Efficient What-If Analysis for Database-Intensive Web Applications
Ronny Ko, Chuan Xiao, Makoto Onizuka +2
Existing what-if analysis systems are predominantly tailored to operate on either only the application layer or only the database layer of software. This isolated approach limits t…
An Empirical Study of Personalized Federated Learning
Koji Matsuda, Yuya Sasaki, Chuan Xiao +1
Federated learning is a distributed machine learning approach in which a single server and multiple clients collaboratively build machine learning models without sharing datasets o…
FedMe: Federated Learning via Model Exchange
Koji Matsuda, Yuya Sasaki, Chuan Xiao +1
Federated learning is a distributed machine learning method in which a single server and multiple clients collaboratively build machine learning models without sharing datasets on…