Search Results for author:
Found 6 papers, 2 papers with code
ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges
In high-stakes medical applications, it is critical to have interpretable models so that experts can validate the reasoning of the model before making important diagnoses.
Fast and Interpretable Mortality Risk Scores for Critical Care Patients
Both of these have disadvantages: black box models are unacceptable for use in hospitals, whereas manual creation of models (including hand-tuning of logistic regression parameters) relies on humans to perform high-dimensional constrained optimization, which leads to a loss in performance.
Multitask Learning for Citation Purpose Classification
The goal of the competition is to classify a citation in a scientific article based on its purpose.
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting.
On the Existence of Simpler Machine Learning Models
We hypothesize that there is an important reason that simple-yet-accurate models often do exist.
Sparse Density Trees and Lists: An Interpretable Alternative to High-Dimensional Histograms
We present sparse tree-based and list-based density estimation methods for binary/categorical data.
