4 citations · 9 across the 6 of their papers we have counts for
6 papers
Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability
Zhuoyan Xu, Zhenmei Shi, Yingyu Liang
Large language models (LLMs) have emerged as powerful tools for many AI problems and exhibit remarkable in-context learning (ICL) capabilities. Compositional ability, solving unsee…
Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning
Zhuoyan Xu, Zhenmei Shi, Junyi Wei +3
Foundation models have emerged as a powerful tool for many AI problems. Despite the tremendous success of foundation models, effective adaptation to new tasks, particularly those w…
A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
Yiyou Sun, Zhenmei Shi, Yixuan Li
Open-world semi-supervised learning aims at inferring both known and novel classes in unlabeled data, by harnessing prior knowledge from a labeled set with known classes. Despite i…
Provable Guarantees for Neural Networks via Gradient Feature Learning
Zhenmei Shi, Junyi Wei, Yingyu Liang
Neural networks have achieved remarkable empirical performance, while the current theoretical analysis is not adequate for understanding their success, e.g., the Neural Tangent Ker…
When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis
Yiyou Sun, Zhenmei Shi, Yingyu Liang +1
Novel Class Discovery (NCD) aims at inferring novel classes in an unlabeled set by leveraging prior knowledge from a labeled set with known classes. Despite its importance, there i…
The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning
Zhenmei Shi, Jiefeng Chen, Kunyang Li +4
Pre-training representations (a.k.a. foundation models) has recently become a prevalent learning paradigm, where one first pre-trains a representation using large-scale unlabeled d…