4 papers
Exploring the Rashomon Set for Concept-Based Models
Shihan Feng, Cheng Zhang, Michael Xi +3
In many machine learning problems, there may exist multiple models that achieve nearly identical predictive performance while relying on fundamentally different internal logic. How…
Where You Inject Diversity Matters: A Unified Framework for Diverse Generation
Cheng Zhang, Rui Xin, Chudi Zhong
Open-ended generation tasks often require a set of meaningfully different outputs, yet large language models often produce similar generations. Existing test-time diversity methods…
The Double-Edged Nature of the Rashomon Set for Trustworthy Machine Learning
Ethan Hsu, Harry Chen, Chudi Zhong +1
Real-world machine learning (ML) pipelines rarely produce a single model; instead, they produce a Rashomon set of many near-optimal ones. We show that this multiplicity reshapes ke…
Models That Are Interpretable But Not Transparent
Chudi Zhong, Panyu Chen, Cynthia Rudin
Faithful explanations are essential for machine learning models in high-stakes applications. Inherently interpretable models are well-suited for these applications because they nat…