Ethan Sterling

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All published works

Action	Title	Year	Authors
+	Interpretable Ranking with Generalized Additive Models	2021	Honglei Zhuang Xuanhui Wang Michael Bendersky Alexander Grushetsky Yonghui Wu Petr Mitrichev Ethan Sterling Nathan Bell Walker Ravina Hai Qian
+	Distilling Interpretable Models into Human-Readable Code.	2021	Walker Ravina Ethan Sterling Olexiy Oryeshko Nathan Bell Honglei Zhuang Xuanhui Wang Yonghui Wu Alexander Grushetsky
+	Distilling Interpretable Models into Human-Readable Code	2021	Walker Ravina Ethan Sterling Olexiy Oryeshko Nathan Bell Honglei Zhuang Xuanhui Wang Yonghui Wu Alexander Grushetsky
+	Interpretable Learning-to-Rank with Generalized Additive Models	2020	Honglei Zhuang Xuanhui Wang Michael Bendersky Alexander Grushetsky Yonghui Wu Petr Mitrichev Ethan Sterling Nathan Bell Walker Ravina Hai Qian

Common Coauthors

Coauthor	Papers Together
Nathan Bell	4
Honglei Zhuang	4
Alexander Grushetsky	4
Xuanhui Wang	4
Walker Ravina	4
Yonghui Wu	3
Petr Mitrichev	2
Hai Qian	2
Michael Bendersky	2
Olexiy Oryeshko	2
Yonghui Wu	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	Techniques for interpretable machine learning	2019	Mengnan Du Ninghao Liu Xia Hu	3
+	Generalized Additive Models	1986	Trevor Hastie Robert Tibshirani	3
+ PDF Chat	Component selection and smoothing in multivariate nonparametric regression	2006	Yi Lin Hao Helen Zhang	2
+	Distilling the Knowledge in a Neural Network	2015	Geoffrey E. Hinton Oriol Vinyals Jay B. Dean	2
+	Learning Important Features Through Propagating Activation Differences	2017	Avanti Shrikumar Peyton Greenside Anshul Kundaje	2
+ PDF Chat	Axiomatic Interpretability for Multiclass Additive Models	2019	Xuezhou Zhang Sarah Tan Paul Koch Yin Lou Urszula Chajewska Rich Caruana	2
+ PDF Chat	High-dimensional additive modeling	2009	Lukas Meier Sara van de Geer Peter Bühlmann	2
+	A comparison of methods for the fitting of generalized additive models	2007	Harald Binder Gerhard Tutz	2
+ PDF Chat	Sparse Partially Linear Additive Models	2015	Yin Lou Jacob Bien Rich Caruana Johannes Gehrke	2
+	Beyond Sparsity: Tree Regularization of Deep Models for Interpretability	2017	Mike Wu Michael C. Hughes Sonali Parbhoo Maurizio Zazzi Volker Röth Finale Doshi‐Velez	2
+ PDF Chat	Accurate intelligible models with pairwise interactions	2013	Yin Lou Rich Caruana Johannes Gehrke Giles Hooker	2
+ PDF Chat	Sparse Additive Models	2009	Pradeep Ravikumar John Lafferty Han Liu Larry Wasserman	2
+	The Frontiers of Fairness in Machine Learning	2018	Alexandra Chouldechova Aaron Roth	2
+	Introducing LETOR 4.0 Datasets	2013	Tao Qin Tieyan Liu	2
+	TF-Ranking	2019	Rama Kumar Pasumarthi Sebastian Bruch Xuanhui Wang Cheng Li Michael Bendersky Marc Najork Jan Pfeifer Nadav Golbandi Rohan Anil S. Wolf	2
+	Is Attention Interpretable?	2019	Sofia Serrano Noah A. Smith	1
+	"Why Should I Trust You?": Explaining the Predictions of Any Classifier	2016	Marco Túlio Ribeiro Sameer Singh Carlos Guestrin	1
+	A Unified Approach to Interpreting Model Predictions	2017	Scott Lundberg Su‐In Lee	1
+ PDF Chat	Explaining Explanations: An Overview of Interpretability of Machine Learning	2018	Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal	1
+	Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments	2017	Alexandra Chouldechova	1
+	Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead	2018	Cynthia Rudin	1
+ PDF Chat	Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation	2018	Sarah Tan Rich Caruana Giles Hooker Yin Lou	1
+	Interpretable Ranking with Generalized Additive Models	2021	Honglei Zhuang Xuanhui Wang Michael Bendersky Alexander Grushetsky Yonghui Wu Petr Mitrichev Ethan Sterling Nathan Bell Walker Ravina Hai Qian	1
+	ADADELTA: An Adaptive Learning Rate Method	2012	Matthew D. Zeiler	1
+	Methods and Models for Interpretable Linear Classification	2014	Berk Ustun Cynthia Rudin	1
+	Generalized Additive Model Selection	2015	Alexandra Chouldechova Trevor Hastie	1
+	Generalized Functional ANOVA Diagnostics for High-Dimensional Functions of Dependent Variables	2007	Giles Hooker	1
+	Estimating regression models with unknown break‐points	2003	Vito M. R. Muggeo	1
+	“Why Should I Trust You?”: Explaining the Predictions of Any Classifier	2016	Marco Ribeiro Sameer Singh Carlos Guestrin	1
+	Towards A Rigorous Science of Interpretable Machine Learning	2017	Finale Doshi‐Velez Been Kim	1
+	Axiomatic Attribution for Deep Networks	2017	Mukund Sundararajan Ankur Taly Qiqi Yan	1
+	A Unified Approach to Interpreting Model Predictions	2017	Scott Lundberg Su‐In Lee	1
+	segmented: An R package to Fit Regression Models with Broken-Line Relationships	2008	Vito M. R. Muggeo	1
+	Manipulating and Measuring Model Interpretability	2018	Forough Poursabzi-Sangdeh Daniel G. Goldstein Jake M. Hofman Jennifer Wortman Vaughan Hanna Wallach	1
+	Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning	2018	Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal	1
+	Posthoc Interpretability of Learning to Rank Models using Secondary Training Data	2018	Jaspreet Singh Avishek Anand	1
+ PDF Chat	Data‐adaptive additive modeling	2018	Ashley Petersen Daniela Witten	1
+	An Interpretable Model with Globally Consistent Explanations for Credit Risk	2018	Chaofan Chen Kangcheng Lin Cynthia Rudin Yaron Shaposhnik Sijia Wang Tong Wang	1