134 citations · 302 across the 27 of their papers we have counts for
20 papers · 1 filter
Solving Few-Shot Multiobjective Multitask Optimization via Iterative Sequential Transfer
Tingyang Wei, Haofeng Wu, Ananda Phan Iman +3
Applying knowledge transfer across multiple optimization tasks, multitask optimization (MTO) emerges as a promising approach to solving synergistic optimization tasks simultaneousl…
Interpretable and Fair Generalized Additive Neural Networks via Multi-objective Learning
Ziming Wang, Changwu Huang, Ke Tang +2
Interpretability and fairness are two of the most emphasized dimensions in trustworthy artificial intelligence (AI). Various explainable AI methods have been introduced to improve…
PETA:Parameter-Efficient Test-Time Adaptation for Virtual Screening
Jia-Qi Lin, Yinghua Yao, Chang-Dong Wang +2
Accurately ranking active ligands for a target protein pocket from massive chemical libraries remains a central challenge in virtual screening. DrugCLIP and its recent extensions s…
Co-Learning Bayesian Optimization
Zhendong Guo, Yew-Soon Ong, Tiantian He +1
Bayesian optimization (BO) is well known to be sample-efficient for solving black-box problems. However, the BO algorithms can sometimes get stuck in suboptimal solutions even with…
Prompt Evolution for Generative AI: A Classifier-Guided Approach
Melvin Wong, Yew-Soon Ong, Abhishek Gupta +2
Synthesis of digital artifacts conditioned on user prompts has become an important paradigm facilitating an explosion of use cases with generative AI. However, such models often fa…
Bayesian Federated Learning: A Survey
Longbing Cao, Hui Chen, Xuhui Fan +3
Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. However, the ro…