Search Results for author: Hang Wu

Found 6 papers, 2 papers with code

Balancing Enhancement, Harmlessness, and General Capabilities: Enhancing Conversational LLMs with Direct RLHF

no code implementations4 Mar 2024 Chen Zheng, Ke Sun, Hang Wu, Chenguang Xi, Xun Zhou

This process often leads to issues such as forgetting or a decrease in the base model's abilities.

EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records

1 code implementation13 Jan 2024 Wenqi Shi, ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, May D. Wang

Large language models (LLMs) have demonstrated exceptional capabilities in planning and tool utilization as autonomous agents, but few have been developed for medical problem-solving.

Code Generation Few-Shot Learning +1

Public Health Informatics: Proposing Causal Sequence of Death Using Neural Machine Translation

no code implementations22 Sep 2020 Yuanda Zhu, Ying Sha, Hang Wu, Mai Li, Ryan A. Hoffman, May D. Wang

The sequence of clinical codes on the death report is named as causal chain of death, coded in the tenth revision of International Statistical Classification of Diseases (ICD-10); in line with the ICD-9-CM Official Guidelines for Coding and Reporting, the priority-ordered clinical conditions on the discharge record are coded in ICD-9.

Machine Translation Translation

Improve Model Generalization and Robustness to Dataset Bias with Bias-regularized Learning and Domain-guided Augmentation

no code implementations12 Oct 2019 Yundong Zhang, Hang Wu, Huiye Liu, Li Tong, May D. Wang

In this study, we investigated model robustness to dataset bias using three large-scale Chest X-ray datasets: first, we assessed the dataset bias using vanilla training baseline; second, we proposed a novel multi-source domain generalization model by (a) designing a new bias-regularized loss function; and (b) synthesizing new data for domain augmentation.

Domain Generalization

Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization

1 code implementation ICLR 2018 Hang Wu

Off-policy learning, the task of evaluating and improving policies using historic data collected from a logging policy, is important because on-policy evaluation is usually expensive and has adverse impacts.

counterfactual

Large-Scale Multi-Label Learning with Incomplete Label Assignments

no code implementations6 Jul 2014 Xiangnan Kong, Zhaoming Wu, Li-Jia Li, Ruofei Zhang, Philip S. Yu, Hang Wu, Wei Fan

Unlike prior works, our method can effectively and efficiently consider missing labels and label correlations simultaneously, and is very scalable, that has linear time complexities over the size of the data.

Missing Labels

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