8 citations · 13 across the 6 of their papers we have counts for
6 papers
Large Language Model Enhanced Machine Learning Estimators for Classification
Yuhang Wu, Yingfei Wang, Chu Wang +1
Pre-trained large language models (LLM) have emerged as a powerful tool for simulating various scenarios and generating output given specific instructions and multimodal input. In…
Collaborative Intelligence in Sequential Experiments: A Human-in-the-Loop Framework for Drug Discovery
Jinghai He, Cheng Hua, Yingfei Wang +1
Drug discovery is a complex process that involves sequentially screening and examining a vast array of molecules to identify those with the target properties. This process, also re…
No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution
Mengxiao Zhang, Shi Chen, Haipeng Luo +1
Supply chain management (SCM) has been recognized as an important discipline with applications to many industries, where the two-echelon stochastic inventory model, involving one d…
Joint Optimization and Statistical Inference for Zero-th Order Simulation Optimization
Yuhang Wu, Zeyu Zheng, Yingfei Wang +3
We consider stochastic optimization problems with the dual tasks of (i) effectively finding the optimizer and (ii) reliably conducting statistical inference for the optimal objecti…
Functional Frank-Wolfe Boosting for General Loss Functions
Chu Wang, Yingfei Wang, Weinan E +1
Boosting is a generic learning method for classification and regression. Yet, as the number of base hypotheses becomes larger, boosting can lead to a deterioration of test performa…
The Knowledge Gradient with Logistic Belief Models for Binary Classification
Yingfei Wang, Chu Wang, Warren Powell
We consider sequential decision making problems for binary classification scenario in which the learner takes an active role in repeatedly selecting samples from the action pool an…