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cs.LG2025
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…
cs.LG2025
Parameter-Efficient Construction of the Rashomon Slice for Concept Bottleneck 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…
cs.LG2025
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…