23 citations · 32 across the 12 of their papers we have counts for
14 papers
Selective Inference for Clustering Under Unknown Covariance
Yiqun T. Chen, Lucy L. Gao
Unsupervised clustering is widely used to discover underlying group structure, after which researchers often seek to validate these groups via post-hoc hypothesis testing on the po…
InferenceEvolve: Towards Automated Causal Effect Estimators through Self-Evolving AI
Can Wang, Hongyu Zhao, Yiqun Chen
Causal inference is central to scientific discovery, yet choosing appropriate methods remains challenging because of the complexity of both statistical methodology and real-world d…
Power Analysis for Prediction-Powered Inference
Yiqun T. Chen, Moran Guo, Shengy Li
Modern studies increasingly leverage outcomes predicted by machine learning and artificial intelligence (AI/ML) models, and recent work, such as prediction-powered inference (PPI),…
Personalized Prediction of Perceived Message Effectiveness Using Large Language Model Based Digital Twins
Jasmin Han, Janardan Devkota, Joseph Waring +8
Perceived message effectiveness (PME) by potential intervention end-users is important for selecting and optimizing personalized smoking cessation intervention messages for mobile…
Efficient Inference for Noisy LLM-as-a-Judge Evaluation
Yiqun T Chen, Sizhu Lu, Sijia Li +2
Large language models (LLMs) are increasingly used as automatic evaluators of generative AI outputs, a paradigm often referred to as "LLM-as-a-judge." In practice, LLM judges are i…
Large Language Models for Full-Text Methods Assessment: A Case Study on Mediation Analysis
Wenqing Zhang, Trang Nguyen, Elizabeth A. Stuart +1
Systematic reviews are crucial for synthesizing scientific evidence but remain labor-intensive, especially when extracting detailed methodological information. Large language model…
Evaluating Large Language Models for Evidence-Based Clinical Question Answering
Can Wang, Yiqun Chen
Large Language Models (LLMs) have demonstrated substantial progress in biomedical and clinical applications, motivating rigorous evaluation of their ability to answer nuanced, evid…
LAVA: Language Model Assisted Verbal Autopsy for Cause-of-Death Determination
Yiqun T. Chen, Tyler H. McCormick, Li Liu +1
Verbal autopsy (VA) is a critical tool for estimating causes of death in resource-limited settings where medical certification is unavailable. This study presents LA-VA, a proof-of…
What's documented in AI? Systematic Analysis of 32K AI Model Cards
Weixin Liang, Nazneen Rajani, Xinyu Yang +5
The rapid proliferation of AI models has underscored the importance of thorough documentation, as it enables users to understand, trust, and effectively utilize these models in var…
Testing for a difference in means of a single feature after clustering
Yiqun T. Chen, Lucy L. Gao
For many applications, it is critical to interpret and validate groups of observations obtained via clustering. A common validation approach involves testing differences in feature…
TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter
Yiqun Chen, James Zou
Recent progress in generative artificial intelligence (gen-AI) has enabled the generation of photo-realistic and artistically-inspiring photos at a single click, catering to millio…
Selective inference for k-means clustering
Yiqun T. Chen, Daniela M. Witten
We consider the problem of testing for a difference in means between clusters of observations identified via k-means clustering. In this setting, classical hypothesis tests lead to…
More powerful selective inference for the graph fused lasso
Yiqun T. Chen, Sean W. Jewell, Daniela M. Witten
The graph fused lasso -- which includes as a special case the one-dimensional fused lasso -- is widely used to reconstruct signals that are piecewise constant on a graph, meaning t…
Quantifying uncertainty in spikes estimated from calcium imaging data
Yiqun T. Chen, Sean W. Jewell, Daniela M. Witten
In recent years, a number of methods have been proposed to estimate the times at which a neuron spikes on the basis of calcium imaging data. However, quantifying the uncertainty as…