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08/02/2023
LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs
We show that large language models (LLMs) are remarkably good at working...
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06/29/2023
Diagnosis Uncertain Models For Medical Risk Prediction
We consider a patient risk models which has access to patient features s...
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05/25/2023
Extending Explainable Boosting Machines to Scientific Image Data
As the deployment of computer vision technology becomes increasingly com...
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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 ...
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02/27/2023
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse
Machine learning (ML) recourse techniques are increasingly used in high-...
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11/15/2022
Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML
Treatment protocols, disease understanding, and viral characteristics ch...
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07/12/2022
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes
Most pregnancies and births result in a good outcome, but complications ...
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06/30/2022
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Machine learning (ML) interpretability techniques can reveal undesirable...
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02/22/2022
Differentially Private Estimation of Heterogeneous Causal Effects
Estimating heterogeneous treatment effects in domains such as healthcare...
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12/06/2021
GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Recent strides in interpretable machine learning (ML) research reveal th...
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10/28/2021
Extracting Clinician's Goals by What-if Interpretable Modeling
Although reinforcement learning (RL) has tremendous success in many fiel...
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06/17/2021
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
We show that adding differential privacy to Explainable Boosting Machine...
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06/03/2021
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning
Deployment of machine learning models in real high-risk settings (e.g. h...
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02/09/2021
Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models
We present an ensemble prediction system using a Deep Learning Weather P...
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07/02/2020
On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks
We examine Dropout through the perspective of interactions: learned effe...
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06/11/2020
How Interpretable and Trustworthy are GAMs?
Generalized additive models (GAMs) have become a leading model class for...
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04/29/2020
Neural Additive Models: Interpretable Machine Learning with Neural Nets
Deep neural networks (DNNs) are powerful black-box predictors that have ...
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11/12/2019
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models
Recent methods for training generalized additive models (GAMs) with pair...
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09/19/2019
InterpretML: A Unified Framework for Machine Learning Interpretability
InterpretML is an open-source Python package which exposes machine learn...
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05/31/2019
Efficient Forward Architecture Search
We propose a neural architecture search (NAS) algorithm, Petridish, to i...
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10/22/2018
Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models
Generalized additive models (GAMs) are favored in many regression and bi...
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01/26/2018
Transparent Model Distillation
Model distillation was originally designed to distill knowledge from a l...
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11/27/2017
Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning
This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine ...
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10/17/2017
Detecting Bias in Black-Box Models Using Transparent Model Distillation
Black-box risk scoring models permeate our lives, yet are typically prop...
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07/04/2017
Interpretable & Explorable Approximations of Black Box Models
We propose Black Box Explanations through Transparent Approximations (BE...
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10/28/2016
Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration
Predictive models deployed in the real world may assign incorrect labels...
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03/17/2016
Do Deep Convolutional Nets Really Need to be Deep and Convolutional?
Yes, they do. This paper provides the first empirical demonstration that...
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07/17/2014
Sparse Partially Linear Additive Models
The generalized partially linear additive model (GPLAM) is a flexible an...
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12/21/2013
Do Deep Nets Really Need to be Deep?
Currently, deep neural networks are the state of the art on problems suc...
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11/28/2013