Rich Caruana | DeepAI

DeepAI

Featured Co-authors

Eric P Xing
145 publications
Jure Leskovec
128 publications
Andrew Gordon Wilson
82 publications
Jason D. Lee
82 publications
John Langford
56 publications
Duen Horng Chau
56 publications
Abdelrahman Mohamed
38 publications
Eric Horvitz
37 publications
Himabindu Lakkaraju
36 publications
Janardhan Kulkarni
31 publications
Jacob Bien
30 publications

∙ 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...

Benjamin Lengerich, et al. ∙

∙ 07/12/2022

Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes

Most pregnancies and births result in a good outcome, but complications ...

Tomas M. Bosschieter, et al. ∙

∙ 06/30/2022

Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values

Machine learning (ML) interpretability techniques can reveal undesirable...

Zijie J. Wang, et al. ∙

∙ 02/22/2022

Differentially Private Estimation of Heterogeneous Causal Effects

Estimating heterogeneous treatment effects in domains such as healthcare...

Fengshi Niu, et al. ∙

∙ 12/06/2021

GAM Changer: Editing Generalized Additive Models with Interactive Visualization

Recent strides in interpretable machine learning (ML) research reveal th...

Zijie J. Wang, et al. ∙

∙ 10/28/2021

Extracting Clinician's Goals by What-if Interpretable Modeling

Although reinforcement learning (RL) has tremendous success in many fiel...

Chun-Hao Chang, et al. ∙

∙ 06/17/2021

Accuracy, Interpretability, and Differential Privacy via Explainable Boosting

We show that adding differential privacy to Explainable Boosting Machine...

Harsha Nori, et al. ∙

∙ 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...

Chun-Hao Chang, et al. ∙

∙ 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...

Jonathan A. Weyn, et al. ∙

∙ 07/02/2020

On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks

We examine Dropout through the perspective of interactions: learned effe...

Benjamin Lengerich, et al. ∙

∙ 06/11/2020

How Interpretable and Trustworthy are GAMs?

Generalized additive models (GAMs) have become a leading model class for...

Chun-Hao Chang, et al. ∙

∙ 04/29/2020

Neural Additive Models: Interpretable Machine Learning with Neural Nets

Deep neural networks (DNNs) are powerful black-box predictors that have ...

Rishabh Agarwal, et al. ∙

∙ 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...

Benjamin Lengerich, et al. ∙

∙ 09/19/2019

InterpretML: A Unified Framework for Machine Learning Interpretability

InterpretML is an open-source Python package which exposes machine learn...

Harsha Nori, et al. ∙

∙ 05/31/2019

Efficient Forward Architecture Search

We propose a neural architecture search (NAS) algorithm, Petridish, to i...

Hanzhang Hu, et al. ∙

∙ 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...

Xuezhou Zhang, et al. ∙

∙ 01/26/2018

Transparent Model Distillation

Model distillation was originally designed to distill knowledge from a l...

Sarah Tan, et al. ∙

∙ 11/27/2017

Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning

This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine ...

Andrew Gordon Wilson, et al. ∙

∙ 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...

Sarah Tan, et al. ∙

∙ 07/04/2017

Interpretable & Explorable Approximations of Black Box Models

We propose Black Box Explanations through Transparent Approximations (BE...

Himabindu Lakkaraju, et al. ∙

∙ 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...

Himabindu Lakkaraju, et al. ∙

∙ 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...

Gregor Urban, et al. ∙

∙ 07/17/2014

Sparse Partially Linear Additive Models

The generalized partially linear additive model (GPLAM) is a flexible an...

Yin Lou, et al. ∙

∙ 12/21/2013

Do Deep Nets Really Need to be Deep?

Currently, deep neural networks are the state of the art on problems suc...

Lei Jimmy Ba, et al. ∙

∙ 11/28/2013

Using Multiple Samples to Learn Mixture Models

In the mixture models problem it is assumed that there are K distributio...

Jason D. Lee, et al. ∙