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

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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CRAFT: ClusteR-specific Assorted Feature selecTion

We present a framework for clustering with cluster-specific feature sele...
06/25/2015 ∙ by Vikas K. Garg, et al. ∙ 0 ∙ share

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Regulating Greed Over Time

In retail, there are predictable yet dramatic time-dependent patterns in...
05/21/2015 ∙ by Stefano Tracà, et al. ∙ 0 ∙ share

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Modeling Recovery Curves With Application to Prostatectomy

We propose a Bayesian model that predicts recovery curves based on infor...
04/27/2015 ∙ by Fulton Wang, et al. ∙ 0 ∙ share

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Interpretable Classification Models for Recidivism Prediction

We investigate a long-debated question, which is how to create predictiv...
03/26/2015 ∙ by Jiaming Zeng, et al. ∙ 0 ∙ share

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Cynthia Rudin

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