5 papers
It's LIT! Reliability-Optimized LLMs with Inspectable Tools
Ruixin Zhang, Jon Donnelly, Zhicheng Guo +4
Large language models (LLMs) have exhibited remarkable capabilities across various domains. The ability to call external tools further expands their capability to handle real-world…
This EEG Looks Like These EEGs: Interpretable Interictal Epileptiform Discharge Detection With ProtoEEG-kNN
Dennis Tang, Jon Donnelly, Alina Jade Barnett +8
The presence of interictal epileptiform discharges (IEDs) in electroencephalogram (EEG) recordings is a critical biomarker of epilepsy. Even trained neurologists find detecting IED…
Leveraging Predictive Equivalence in Decision Trees
Hayden McTavish, Zachery Boner, Jon Donnelly +2
Decision trees are widely used for interpretable machine learning due to their clearly structured reasoning process. However, this structure belies a challenge we refer to as predi…
Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time
Jon Donnelly, Zhicheng Guo, Alina Jade Barnett +3
Interpretability is critical for machine learning models in high-stakes settings because it allows users to verify the model's reasoning. In computer vision, prototypical part mode…
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data
Ishaan Maitra, Raymond Lin, Eric Chen +3
Health outcomes depend on complex environmental and sociodemographic factors whose effects change over location and time. Only recently has fine-grained spatial and temporal data b…