27 citations · 64 across the 23 of their papers we have counts for
5 papers · 1 filter
LabBook: Harnessing Experimental History for Efficient LLM-Driven Discovery
Bo Yuan, Wenqian Ye, Zelin Zhao +4
Evolutionary approaches to LLM-driven discovery often generate new programs from a small set of selected ancestors. This keeps contexts manageable but can omit useful evidence from…
Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium
Amin Adibi, Xu Cao, Zongliang Ji +39
The fourth Machine Learning for Health (ML4H) symposium was held in person on December 15th and 16th, 2024, in the traditional, ancestral, and unceded territories of the Musqueam,…
Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation
Guangtao Zheng, Wenqian Ye, Aidong Zhang
Deep neural classifiers tend to rely on spurious correlations between spurious attributes of inputs and targets to make predictions, which could jeopardize their generalization cap…
The Clever Hans Mirage: A Comprehensive Survey on Spurious Correlations in Machine Learning
Wenqian Ye, Luyang Jiang, Eric Xie +16
Back in the early 20th century, a horse named Hans appeared to perform arithmetic and other intellectual tasks during exhibitions in Germany, while it actually relied solely on inv…
Mitigating Transformer Overconfidence via Lipschitz Regularization
Wenqian Ye, Yunsheng Ma, Xu Cao +1
Though Transformers have achieved promising results in many computer vision tasks, they tend to be over-confident in predictions, as the standard Dot Product Self-Attention (DPSA)…