Cynthia Rudin

Learning Certifiably Optimal Rule Lists

2017

We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm provides the optimal solution, with a certificate of optimality. By leveraging algorithmic …

Extreme Dimension Reduction for Handling Covariate Shift

2017 · arXiv (Cornell University)

In the covariate shift learning scenario, the training and test covariate distributions differ, so that a predictor's average loss over the training and test distributions also differ. In this work, we explore the potential of …

Playing Codenames with Language Graphs and Word Embeddings

2021 · Journal of Artificial Intelligence Research

Although board games and video games have been studied for decades in artificial intelligence research, challenging word games remain relatively unexplored. Word games are not as constrained as games like chess or poker. Instead, word …

How Smart Guessing Strategies Can Yield Massive Scalability Improvements for Sparse Decision Tree Optimization

2021 · arXiv (Cornell University)

Sparse decision tree optimization has been one of the most fundamental problems in AI since its inception and is a challenge at the core of interpretable machine learning. Sparse decision tree optimization is computationally hard, …

Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?

2023 · arXiv (Cornell University)

Missing values are a fundamental problem in data science. Many datasets have missing values that must be properly handled because the way missing values are treated can have large impact on the resulting machine learning …

The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance

2023 · arXiv (Cornell University)

Quantifying variable importance is essential for answering high-stakes questions in fields like genetics, public policy, and medicine. Current methods generally calculate variable importance for a given model trained on a given dataset. However, for a …

Sparse Density Trees and Lists: An Interpretable Alternative to High-Dimensional Histograms

2015 · arXiv (Cornell University)

We present sparse tree-based and list-based density estimation methods for binary/categorical data. Our density estimation models are higher dimensional analogies to variable bin width histograms. In each leaf of the tree (or list), the density …

Fast Rashomon Sets of Sparse Rule Sets

2026 · Machine Learning

Abstract A sparse rule set (SRS) is a small predictive model that is a disjunctive normal form – an “OR of ANDs”. SRS models are understandable to human experts and robust to outliers. Constructing an …