6 citations · 16 across the 15 of their papers we have counts for
16 papers
SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing
Shuyu Chen, Chen Zhu, Ye Zhang +3
Identifying therapeutic target genes from single-cell RNA sequencing (scRNA-seq) data remains a fundamental challenge in translational biology. Unlike bulk assays, scRNA-seq captur…
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…
MemeCLIP: Leveraging CLIP Representations for Multimodal Meme Classification
Siddhant Bikram Shah, Shuvam Shiwakoti, Maheep Chaudhary +1
The complexity of text-embedded images presents a formidable challenge in machine learning given the need for multimodal understanding of multiple aspects of expression conveyed by…
Simple Unsupervised Knowledge Distillation With Space Similarity
Aditya Singh, Haohan Wang
As per recent studies, Self-supervised learning (SSL) does not readily extend to smaller architectures. One direction to mitigate this shortcoming while simultaneously training a s…
GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data Analysis
Haoyang Liu, Shuyu Chen, Ye Zhang +1
Recent advancements in machine learning have significantly improved the identification of disease-associated genes from gene expression datasets. However, these processes often req…
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…
Beyond Pixels: Exploring Human-Readable SVG Generation for Simple Images with Vision Language Models
Tong Zhang, Haoyang Liu, Peiyan Zhang +2
In the field of computer graphics, the use of vector graphics, particularly Scalable Vector Graphics (SVG), represents a notable development from traditional pixel-based imagery. S…
Dataset Distillation via the Wasserstein Metric
Haoyang Liu, Yijiang Li, Tiancheng Xing +5
Dataset Distillation (DD) aims to generate a compact synthetic dataset that enables models to achieve performance comparable to training on the full large dataset, significantly re…
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…
Towards Understanding Adversarial Transferability in Federated Learning
Yijiang Li, Ying Gao, Haohan Wang
We investigate a specific security risk in FL: a group of malicious clients has impacted the model during training by disguising their identities and acting as benign clients but l…
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models
Peiyan Zhang, Haoyang Liu, Chaozhuo Li +3
Machine learning has demonstrated remarkable performance over finite datasets, yet whether the scores over the fixed benchmarks can sufficiently indicate the model's performance in…
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives
Haoyang Liu, Maheep Chaudhary, Haohan Wang
The trustworthiness of machine learning has emerged as a critical topic in the field, encompassing various applications and research areas such as robustness, security, interpretab…