4 papers
Global Optimization on Graph-Structured Data via Gaussian Processes with Spectral Representations
Shu Hong, Yongsheng Mei, Mahdi Imani +1
Bayesian optimization (BO) is a powerful framework for optimizing expensive black-box objectives, yet extending it to graph-structured domains remains challenging due to the discre…
Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?
Yongsheng Mei, Liangqi Yuan, Dong-Jun Han +3
Federated learning (FL) has become a cornerstone in decentralized learning, where, in many scenarios, the incoming data distribution will change dynamically over time, introducing…
RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space
Jingdi Chen, Hanhan Zhou, Yongsheng Mei +4
Deep Reinforcement Learning (DRL) algorithms have achieved great success in solving many challenging tasks while their black-box nature hinders interpretability and real-world appl…
Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions
Jiayu Chen, Bhargav Ganguly, Yang Xu +3
Deep generative models (DGMs) have demonstrated great success across various domains, particularly in generating texts, images, and videos using models trained from offline data. S…