Cynthia Rudin

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Duke University ∙ MIT ∙ Columbia University

Associate Professor of Computer Science and Electrical and Computer Engineering PI, Prediction Analysis Lab at Duke University

Playing Codenames with Language Graphs and Word Embeddings

Although board games and video games have been studied for decades in ar...
05/12/2021 ∙ by Divya Koyyalagunta, et al. ∙ 7 ∙ share

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Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization

AI has the potential to revolutionize many areas of healthcare. Radiolog...
04/30/2021 ∙ by Michael Anis Mihdi Afnan, et al. ∙ 37 ∙ share

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IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography

Interpretability in machine learning models is important in high-stakes ...
03/23/2021 ∙ by Alina Jade Barnett, et al. ∙ 30 ∙ share

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Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Interpretability in machine learning (ML) is crucial for high stakes dec...
03/20/2021 ∙ by Cynthia Rudin, et al. ∙ 80 ∙ share

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dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference

dame-flame is a Python package for performing matching for observational...
01/06/2021 ∙ by Neha R. Gupta, et al. ∙ 21 ∙ share

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Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization

Dimension reduction (DR) techniques such as t-SNE, UMAP, and TriMAP have...
12/08/2020 ∙ by Yingfan Wang, et al. ∙ 11 ∙ share

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Cryo-ZSSR: multiple-image super-resolution based on deep internal learning

Single-particle cryo-electron microscopy (cryo-EM) is an emerging imagin...
11/22/2020 ∙ by Qinwen Huang, et al. ∙ 3 ∙ share

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Matching Bounds: How Choice of Matching Algorithm Impacts Treatment Effects Estimates and What to Do about It

Different matches on the same data may produce different treatment effec...
09/06/2020 ∙ by Marco Morucci, et al. ∙ 0 ∙ share

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Bandits for BMO Functions

We study the bandit problem where the underlying expected reward is a Bo...
07/17/2020 ∙ by Tianyu Wang, et al. ∙ 0 ∙ share

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Generalized Optimal Sparse Decision Trees

Decision tree optimization is notoriously difficult from a computational...
06/15/2020 ∙ by Jimmy Lin, et al. ∙ 9 ∙ share

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In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction

In recent years, academics and investigative journalists have criticized...
05/08/2020 ∙ by Caroline Wang, et al. ∙ 16 ∙ share

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PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

The primary aim of single-image super-resolution is to construct a high-...
03/08/2020 ∙ by Sachit Menon, et al. ∙ 0 ∙ share

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Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation

We propose a matching method for observational data that matches units w...
03/03/2020 ∙ by Marco Morucci, et al. ∙ 0 ∙ share

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Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference

We propose a matching method that recovers direct treatment effects from...
03/02/2020 ∙ by M. Usaid Awan, et al. ∙ 0 ∙ share

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Concept Whitening for Interpretable Image Recognition

What does a neural network encode about a concept as we traverse through...
02/05/2020 ∙ by Zhi Chen, et al. ∙ 5 ∙ share

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A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning

The Rashomon effect occurs when many different explanations exist for th...
08/05/2019 ∙ by Lesia Semenova, et al. ∙ 0 ∙ share

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Reducing Exploration of Dying Arms in Mortal Bandits

Mortal bandits have proven to be extremely useful for providing news art...
07/04/2019 ∙ by Stefano Tracà, et al. ∙ 0 ∙ share

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Interpretable Almost-Matching-Exactly With Instrumental Variables

Uncertainty in the estimation of the causal effect in observational stud...
06/27/2019 ∙ by M. Usaid Awan, et al. ∙ 0 ∙ share

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Interpretable Image Recognition with Hierarchical Prototypes

Vision models are interpretable when they classify objects on the basis ...
06/25/2019 ∙ by Peter Hase, et al. ∙ 2 ∙ share

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The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis

Despite the widespread usage of machine learning throughout organization...
06/04/2019 ∙ by Cynthia Rudin, et al. ∙ 0 ∙ share

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Optimal Sparse Decision Trees

Decision tree algorithms have been among the most popular algorithms for...
04/29/2019 ∙ by Xiyang Hu, et al. ∙ 6 ∙ share

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A Practical Bandit Method with Advantages in Neural Network Tuning

Stochastic bandit algorithms can be used for challenging non-convex opti...
01/26/2019 ∙ by Tianyu Wang, et al. ∙ 0 ∙ share

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Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models

Variable importance is central to scientific studies, including the soci...
01/10/2019 ∙ by Jiayun Dong, et al. ∙ 0 ∙ share

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Hypothesis Tests That Are Robust to Choice of Matching Method

A vast number of causal inference studies test hypotheses on treatment e...
12/05/2018 ∙ by Marco Morucci, et al. ∙ 0 ∙ share

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An Interpretable Model with Globally Consistent Explanations for Credit Risk

We propose a possible solution to a public challenge posed by the Fair I...
11/30/2018 ∙ by Chaofan Chen, et al. ∙ 0 ∙ share

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Please Stop Explaining Black Box Models for High Stakes Decisions

There are black box models now being used for high stakes decision-makin...
11/26/2018 ∙ by Cynthia Rudin, et al. ∙ 0 ∙ share

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MALTS: Matching After Learning to Stretch

We introduce a flexible framework for matching in causal inference that ...
11/18/2018 ∙ by Harsh Parikh, et al. ∙ 0 ∙ share

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Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation

We provide code that produces beautiful poetry. Our sonnet-generation al...
11/13/2018 ∙ by John Benhart, et al. ∙ 0 ∙ share

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The age of secrecy and unfairness in recidivism prediction

In our current society, secret algorithms make important decisions about...
11/02/2018 ∙ by Cynthia Rudin, et al. ∙ 0 ∙ share

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Bayesian Patchworks: An Approach to Case-Based Reasoning

Doctors often rely on their past experience in order to diagnose patient...
09/10/2018 ∙ by Ramin Moghaddass, et al. ∙ 0 ∙ share

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This looks like that: deep learning for interpretable image recognition

When we are faced with challenging image classification tasks, we often ...
06/27/2018 ∙ by Chaofan Chen, et al. ∙ 6 ∙ share

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Collapsing-Fast-Large-Almost-Matching-Exactly: A Matching Method for Causal Inference

We aim to create the highest possible quality of treatment-control match...
06/18/2018 ∙ by Awa Dieng, et al. ∙ 0 ∙ share

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New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution

This work identifies and addresses two important technical challenges in...
05/09/2018 ∙ by Yijie Bei, et al. ∙ 0 ∙ share

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A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results

Inference is the process of using facts we know to learn about facts we ...
04/23/2018 ∙ by Beau Coker, et al. ∙ 0 ∙ share

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A Minimax Surrogate Loss Approach to Conditional Difference Estimation

We present a new machine learning approach to estimate personalized trea...
03/10/2018 ∙ by Siong Thye Goh, et al. ∙ 0 ∙ share

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Direct Learning to Rank and Rerank

Learning-to-rank techniques have proven to be extremely useful for prior...
02/21/2018 ∙ by Cynthia Rudin, et al. ∙ 0 ∙ share

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Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the "Rashomon" Perspective

There are serious drawbacks to many current variable importance (VI) met...
01/04/2018 ∙ by Aaron Fisher, et al. ∙ 0 ∙ share

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Dimension Reduction for Robust Covariate Shift Correction

In the covariate shift learning scenario, the training and test covariat...
11/29/2017 ∙ by Fulton Wang, et al. ∙ 0 ∙ share

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Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect

We introduce a novel generative model for interpretable subgroup analysi...
10/16/2017 ∙ by Tong Wang, et al. ∙ 0 ∙ share

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Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions

Deep neural networks are widely used for classification. These deep mode...
10/13/2017 ∙ by Oscar Li, et al. ∙ 0 ∙ share

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FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference

A classical problem in causal inference is that of matching, where treat...
07/19/2017 ∙ by Sudeepa Roy, et al. ∙ 0 ∙ share

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Learning Certifiably Optimal Rule Lists for Categorical Data

We present the design and implementation of a custom discrete optimizati...
04/06/2017 ∙ by Elaine Angelino, et al. ∙ 0 ∙ share

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Learning Cost-Effective Treatment Regimes using Markov Decision Processes

Decision makers, such as doctors and judges, make crucial decisions such...
10/21/2016 ∙ by Himabindu Lakkaraju, et al. ∙ 0 ∙ share

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Learning Optimized Risk Scores on Large-Scale Datasets

Risk scores are simple classification models that let users quickly asse...
10/01/2016 ∙ by Berk Ustun, et al. ∙ 0 ∙ share

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Interpretable Machine Learning Models for the Digital Clock Drawing Test

The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular neurop...
06/23/2016 ∙ by William Souillard-Mandar, et al. ∙ 0 ∙ share

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Scalable Bayesian Rule Lists

We present an algorithm for building probabilistic rule lists that is tw...
02/27/2016 ∙ by Hongyu Yang, et al. ∙ 0 ∙ share

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Learning Optimized Or's of And's

Or's of And's (OA) models are comprised of a small number of disjunction...
11/06/2015 ∙ by Tong Wang, et al. ∙ 0 ∙ share

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Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

We aim to produce predictive models that are not only accurate, but are ...
11/05/2015 ∙ by Benjamin Letham, et al. ∙ 0 ∙ share

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Cascaded High Dimensional Histograms: A Generative Approach to Density Estimation

We present tree- and list- structured density estimation methods for hig...
10/22/2015 ∙ by Siong Thye Goh, et al. ∙ 0 ∙ share

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Causal Falling Rule Lists

A causal falling rule list (CFRL) is a sequence of if-then rules that sp...
10/18/2015 ∙ by Fulton Wang, et al. ∙ 0 ∙ share

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Cynthia Rudin

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