b'Cynthia Rudin'

research

∙ 07/07/2023

A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables

Smart watches and other wearable devices are equipped with photoplethysm...

0 pranay-jain, et al. ∙

research

∙ 07/06/2023

Learned Kernels for Interpretable and Efficient PPG Signal Quality Assessment and Artifact Segmentation

Photoplethysmography (PPG) provides a low-cost, non-invasive method to c...

0 Sully F. Chen, et al. ∙

research

∙ 07/04/2023

A Double Machine Learning Approach to Combining Experimental and Observational Data

Experimental and observational studies often lack validity due to untest...

0 Marco Morucci, et al. ∙

research

∙ 04/23/2023

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

Missing values are a fundamental problem in data science. Many datasets ...

0 Zhi Chen, et al. ∙

research

∙ 04/13/2023

OKRidge: Scalable Optimal k-Sparse Ridge Regression for Learning Dynamical Systems

We consider an important problem in scientific discovery, identifying sp...

0 Jiachang Liu, et al. ∙

research

∙ 04/03/2023

Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics

We introduce Matched Machine Learning, a framework that combines the fle...

0 Marco Morucci, et al. ∙

research

∙ 03/28/2023

Understanding and Exploring the Whole Set of Good Sparse Generalized Additive Models

In real applications, interaction between machine learning model and dom...

0 Zhi Chen, et al. ∙

research

∙ 02/23/2023

From Feature Importance to Distance Metric: An Almost Exact Matching Approach for Causal Inference

Our goal is to produce methods for observational causal inference that a...

0 Quinn Lanners, et al. ∙

research

∙ 11/09/2022

Mapping the Ictal-Interictal-Injury Continuum Using Interpretable Machine Learning

IMPORTANCE: An interpretable machine learning model can provide faithful...

0 Alina Jade Barnett, et al. ∙

research

∙ 10/13/2022

Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design

Sparse decision trees are one of the most common forms of interpretable ...

7 Ali Behrouz, et al. ∙

research

∙ 10/12/2022

FasterRisk: Fast and Accurate Interpretable Risk Scores

Over the last century, risk scores have been the most popular form of pr...

9 Jiachang Liu, et al. ∙

research

∙ 09/19/2022

TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization

Given thousands of equally accurate machine learning (ML) models, how ca...

6 Zijie J. Wang, et al. ∙

research

∙ 09/16/2022

Exploring the Whole Rashomon Set of Sparse Decision Trees

In any given machine learning problem, there may be many models that cou...

1 Rui Xin, et al. ∙

research

∙ 06/09/2022

There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes

Interpretability is an essential building block for trustworthiness in r...

16 Yishay Mansour, et al. ∙

research

∙ 04/22/2022

SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation

Visual concept discovery has long been deemed important to improve inter...

0 Haiyang Huang, et al. ∙

research

∙ 03/09/2022

Why Interpretable Causal Inference is Important for High-Stakes Decision Making for Critically Ill Patients and How To Do It

Many fundamental problems affecting the care of critically ill patients ...

15 Harsh Parikh, et al. ∙

research

∙ 02/23/2022

Fast Sparse Classification for Generalized Linear and Additive Models

We present fast classification techniques for sparse generalized linear ...

6 Jiachang Liu, et al. ∙

research

∙ 12/01/2021

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

Sparse decision tree optimization has been one of the most fundamental p...

3 Hayden McTavish, et al. ∙

research

∙ 11/10/2021

How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning

Metamaterials are composite materials with engineered geometrical micro-...

51 Zhi Chen, et al. ∙

research

∙ 09/15/2021

BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales

Algorithmic harmonization - the automated harmonization of a musical pie...

13 Yunyao Zhu, et al. ∙

research

∙ 07/12/2021

Interpretable Mammographic Image Classification using Cased-Based Reasoning and Deep Learning

When we deploy machine learning models in high-stakes medical settings, ...

22 Alina Jade Barnett, et al. ∙

research

∙ 06/24/2021

Multitask Learning for Citation Purpose Classification

We present our entry into the 2021 3C Shared Task Citation Context Class...

2 Alex Oesterling, et al. ∙

research

∙ 06/04/2021

A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations

Lending decisions are usually made with proprietary models that provide ...

13 Chaofan Chen, et al. ∙

research

∙ 05/12/2021

Playing Codenames with Language Graphs and Word Embeddings

Although board games and video games have been studied for decades in ar...

7 Divya Koyyalagunta, et al. ∙

research

∙ 04/30/2021

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

AI has the potential to revolutionize many areas of healthcare. Radiolog...

37 Michael Anis Mihdi Afnan, et al. ∙

research

∙ 03/23/2021

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 ...

30 Alina Jade Barnett, et al. ∙

research

∙ 03/20/2021

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Interpretability in machine learning (ML) is crucial for high stakes dec...

80 Cynthia Rudin, et al. ∙

research

∙ 01/06/2021

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

dame-flame is a Python package for performing matching for observational...

21 Neha R. Gupta, et al. ∙

research

∙ 12/08/2020

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 Yingfan Wang, et al. ∙

research

∙ 11/22/2020

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

Single-particle cryo-electron microscopy (cryo-EM) is an emerging imagin...

3 Qinwen Huang, et al. ∙

research

∙ 09/06/2020

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...

0 Marco Morucci, et al. ∙

research

∙ 07/17/2020

Bandits for BMO Functions

We study the bandit problem where the underlying expected reward is a Bo...

0 Tianyu Wang, et al. ∙

research

∙ 06/15/2020

Generalized Optimal Sparse Decision Trees

Decision tree optimization is notoriously difficult from a computational...

9 Jimmy Lin, et al. ∙

research

∙ 05/08/2020

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

In recent years, academics and investigative journalists have criticized...

16 Caroline Wang, et al. ∙

research

∙ 03/08/2020

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-...

0 Sachit Menon, et al. ∙

research

∙ 03/03/2020

Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation

We propose a matching method for observational data that matches units w...

0 Marco Morucci, et al. ∙

research

∙ 03/02/2020

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

We propose a matching method that recovers direct treatment effects from...

0 M. Usaid Awan, et al. ∙

research

∙ 02/05/2020

Concept Whitening for Interpretable Image Recognition

What does a neural network encode about a concept as we traverse through...

5 Zhi Chen, et al. ∙

research

∙ 08/05/2019

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...

0 Lesia Semenova, et al. ∙

research

∙ 07/04/2019

Reducing Exploration of Dying Arms in Mortal Bandits

Mortal bandits have proven to be extremely useful for providing news art...

0 Stefano Tracà, et al. ∙

research

∙ 06/27/2019

Interpretable Almost-Matching-Exactly With Instrumental Variables

Uncertainty in the estimation of the causal effect in observational stud...

0 M. Usaid Awan, et al. ∙

research

∙ 06/25/2019

Interpretable Image Recognition with Hierarchical Prototypes

Vision models are interpretable when they classify objects on the basis ...

2 Peter Hase, et al. ∙

research

∙ 06/04/2019

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...

0 Cynthia Rudin, et al. ∙

research

∙ 04/29/2019

Optimal Sparse Decision Trees

Decision tree algorithms have been among the most popular algorithms for...

6 Xiyang Hu, et al. ∙

research

∙ 01/26/2019

A Practical Bandit Method with Advantages in Neural Network Tuning

Stochastic bandit algorithms can be used for challenging non-convex opti...

0 Tianyu Wang, et al. ∙

research

∙ 01/10/2019

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...

0 Jiayun Dong, et al. ∙

research

∙ 12/05/2018

Hypothesis Tests That Are Robust to Choice of Matching Method

A vast number of causal inference studies test hypotheses on treatment e...

0 Marco Morucci, et al. ∙

research

∙ 11/30/2018

An Interpretable Model with Globally Consistent Explanations for Credit Risk

We propose a possible solution to a public challenge posed by the Fair I...

0 Chaofan Chen, et al. ∙

research

∙ 11/26/2018

Please Stop Explaining Black Box Models for High Stakes Decisions

There are black box models now being used for high stakes decision-makin...

0 Cynthia Rudin, et al. ∙

research

∙ 11/18/2018

MALTS: Matching After Learning to Stretch

We introduce a flexible framework for matching in causal inference that ...

0 Harsh Parikh, et al. ∙

Cynthia Rudin

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