4 citations · 5 across the 7 of their papers we have counts for
7 papers
GenoMAS: Guided Multi-Agent Programming for Scientific Discovery from Genomic Data
Haoyang Liu, Yijiang Li, Haohan Wang
Gene expression analysis holds the key to many biomedical discoveries, yet extracting insights from raw transcriptomic data remains challenging due to the complexity of multiple la…
Achilles Heel of Distributed Multi-Agent Systems
Yiting Zhang, Yijiang Li, Tianwei Zhao +3
Multi-agent system (MAS) has demonstrated exceptional capabilities in addressing complex challenges, largely due to the integration of multiple large language models (LLMs). Howeve…
Beyond Finite Data: Towards Data-free Out-of-distribution Generalization via Extrapolation
Yijiang Li, Sucheng Ren, Weipeng Deng +4
Out-of-distribution (OOD) generalization is a favorable yet challenging property for deep neural networks. The core challenges lie in the limited availability of source domains tha…
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers
Haoyang Liu, Aditya Singh, Yijiang Li +1
Enhancing the robustness of deep learning models, particularly in the realm of vision transformers (ViTs), is crucial for their real-world deployment. In this work, we provide a fi…
Towards Adversarially Robust Dataset Distillation by Curvature Regularization
Eric Xue, Yijiang Li, Haoyang Liu +3
Dataset distillation (DD) allows datasets to be distilled to fractions of their original size while preserving the rich distributional information, so that models trained on the di…
Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data
Haoyang Liu, Yijiang Li, Jinglin Jian +7
Machine learning has emerged as a powerful tool for scientific discovery, enabling researchers to extract meaningful insights from complex datasets. For instance, it has facilitate…
Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalization
Jixuan Leng, Yijiang Li, Haohan Wang
Domain Generalization (DG), a crucial research area, seeks to train models across multiple domains and test them on unseen ones. In this paper, we introduce a novel approach, namel…