Rich Caruana

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Microsoft

Efficient Forward Architecture Search

We propose a neural architecture search (NAS) algorithm, Petridish, to i...
05/31/2019 ∙ by Hanzhang Hu, et al. ∙ 4 ∙ share

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Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning

This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine ...
11/27/2017 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ share

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Detecting Bias in Black-Box Models Using Transparent Model Distillation

Black-box risk scoring models permeate our lives, yet are typically prop...
10/17/2017 ∙ by Sarah Tan, et al. ∙ 0 ∙ share

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Interpretable & Explorable Approximations of Black Box Models

We propose Black Box Explanations through Transparent Approximations (BE...
07/04/2017 ∙ by Himabindu Lakkaraju, et al. ∙ 0 ∙ share

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Do Deep Nets Really Need to be Deep?

Currently, deep neural networks are the state of the art on problems suc...
12/21/2013 ∙ by Lei Jimmy Ba, et al. ∙ 0 ∙ share

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Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration

Predictive models deployed in the real world may assign incorrect labels...
10/28/2016 ∙ by Himabindu Lakkaraju, et al. ∙ 0 ∙ share

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Do Deep Convolutional Nets Really Need to be Deep and Convolutional?

Yes, they do. This paper provides the first empirical demonstration that...
03/17/2016 ∙ by Gregor Urban, et al. ∙ 0 ∙ share

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Sparse Partially Linear Additive Models

The generalized partially linear additive model (GPLAM) is a flexible an...
07/17/2014 ∙ by Yin Lou, et al. ∙ 0 ∙ share

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Using Multiple Samples to Learn Mixture Models

In the mixture models problem it is assumed that there are K distributio...
11/28/2013 ∙ by Jason D. Lee, et al. ∙ 0 ∙ share

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Transparent Model Distillation

Model distillation was originally designed to distill knowledge from a l...
01/26/2018 ∙ by Sarah Tan, et al. ∙ 0 ∙ share

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Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models

Generalized additive models (GAMs) are favored in many regression and bi...
10/22/2018 ∙ by Xuezhou Zhang, et al. ∙ 0 ∙ share

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InterpretML: A Unified Framework for Machine Learning Interpretability

InterpretML is an open-source Python package which exposes machine learn...
09/19/2019 ∙ by Harsha Nori, et al. ∙ 0 ∙ share

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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...
11/12/2019 ∙ by Benjamin Lengerich, et al. ∙ 0 ∙ share

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Rich Caruana

Featured Co-authors

Jure Leskovec

Jason D. Lee

Andrew Gordon Wilson

John Langford

Jason Yosinski

Jacob Bien

Giles Hooker

Eric Horvitz

Debadeepta Dey

Ece Kamar

Johannes Gehrke

Efficient Forward Architecture Search

Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning

Detecting Bias in Black-Box Models Using Transparent Model Distillation

Interpretable & Explorable Approximations of Black Box Models

Do Deep Nets Really Need to be Deep?

Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration

Do Deep Convolutional Nets Really Need to be Deep and Convolutional?

Sparse Partially Linear Additive Models

Using Multiple Samples to Learn Mixture Models

Transparent Model Distillation

Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models

InterpretML: A Unified Framework for Machine Learning Interpretability

Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models