Articles with public access mandates - Zhenmei ShiLearn more
Available somewhere: 8
Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability
Z Xu*, Z Shi*, Y Liang
COLM 2024: Conference on Language Modeling, 2024
Mandates: US National Science Foundation, US Department of Defense
The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning
Z Shi*, J Chen*, K Li, J Raghuram, X Wu, Y Liang, S Jha
ICLR 2023 (Spotlight): International Conference on Learning Representations, 2023
Mandates: US National Science Foundation, US Department of Defense
A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
Y Sun, Z Shi, Y Li
NeurIPS 2023 (Spotlight): Neural Information Processing Systems, 2023
Mandates: US National Science Foundation, US Department of Defense
Domain generalization via nuclear norm regularization
Z Shi, Y Ming, Y Fan, F Sala, Y Liang
CPAL 2024: Conference on Parsimony and Learning, 179-201, 2024
Mandates: US National Science Foundation, US Department of Defense
Attentive walk-aggregating graph neural networks
MF Demirel, S Liu, S Garg, Z Shi, Y Liang
Transactions on Machine Learning Research, 2022
Mandates: US National Science Foundation, US Department of Defense
Provable Guarantees for Neural Networks via Gradient Feature Learning
Z Shi*, J Wei*, Y Liang
NeurIPS 2023: Neural Information Processing Systems, 2023
Mandates: US National Science Foundation, US Department of Defense
Improving foundation models for few-shot learning via multitask finetuning
Z Xu, Z Shi, J Wei, Y Li, Y Liang
ME-FoMo Workshop @ ICLR 2023, 2023
Mandates: US National Science Foundation, US Department of Defense
Domain generalization with nuclear norm regularization
Z Shi, Y Ming, Y Fan, F Sala, Y Liang
NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and …, 2022
Mandates: US National Science Foundation, US Department of Defense
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