1 citations · 2 across the 9 of their papers we have counts for
9 papers
DeconDTN-Toolkit: A Library for Evaluation and Enhancement of Robustness to Provenance Shift
Yongsen Tan, Zhecheng Sheng, Xiruo Ding +2
Despite the burgeoning body of work on distribution shifts, provenance shift-where the relationship between data source and label changes at deployment-remains poorly understood an…
When Prompts Interact: Assessing Prompt Arithmetic for Deconfounding under Distribution Shift
Zhecheng Sheng, Yongsen Tan, Xiruo Ding +2
In classification tasks, models may rely on confounding variables to achieve strong in-distribution performance, capturing spurious features that fail under distribution shift. Thi…
Toward Unifying Group Fairness Evaluation from a Sparsity Perspective
Zhecheng Sheng, Jiawei Zhang, Enmao Diao
Ensuring algorithmic fairness remains a significant challenge in machine learning, particularly as models are increasingly applied across diverse domains. While numerous fairness c…
Mitigating Confounding in Speech-Based Dementia Detection through Weight Masking
Zhecheng Sheng, Xiruo Ding, Brian Hur +3
Deep transformer models have been used to detect linguistic anomalies in patient transcripts for early Alzheimer's disease (AD) screening. While pre-trained neural language models…
"Is There Anything Else?'': Examining Administrator Influence on Linguistic Features from the Cookie Theft Picture Description Cognitive Test
Changye Li, Zhecheng Sheng, Trevor Cohen +1
Alzheimer's Disease (AD) dementia is a progressive neurodegenerative disease that negatively impacts patients' cognitive ability. Previous studies have demonstrated that changes in…
Too Big to Fail: Larger Language Models are Disproportionately Resilient to Induction of Dementia-Related Linguistic Anomalies
Changye Li, Zhecheng Sheng, Trevor Cohen +1
As artificial neural networks grow in complexity, understanding their inner workings becomes increasingly challenging, which is particularly important in healthcare applications. T…
Enhancing Robustness of Foundation Model Representations under Provenance-related Distribution Shifts
Xiruo Ding, Zhecheng Sheng, Brian Hur +3
Foundation models are a current focus of attention in both industry and academia. While they have shown their capabilities in a variety of tasks, in-depth research is required to d…
Backdoor Adjustment of Confounding by Provenance for Robust Text Classification of Multi-institutional Clinical Notes
Xiruo Ding, Zhecheng Sheng, Meliha Yetişgen +2
Natural Language Processing (NLP) methods have been broadly applied to clinical tasks. Machine learning and deep learning approaches have been used to improve the performance of cl…
A Dialogue System for Assessing Activities of Daily Living: Improving Consistency with Grounded Knowledge
Zhecheng Sheng, Raymond Finzel, Michael Lucke +3
In healthcare, the ability to care for oneself is reflected in the "Activities of Daily Living (ADL)," which serve as a measure of functional ability (functioning). A lack of funct…