Rich Caruana

ORCID

Publication Activity (10 Years)

Years Active: 1987-2024
Publications (10 Years): 52

Top Venues

Publications

Chandan Singh, Jeevana Priya Inala, Michel Galley, Rich Caruana, Jianfeng Gao
Rethinking Interpretability in the Era of Large Language Models. CoRR (2024)
Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Ian S. Painter, Vivienne Souter, Rich Caruana
Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes. J. Heal. Informatics Res. 8 (1) (2024)
Benjamin J. Lengerich, Sebastian Bordt, Harsha Nori, Mark E. Nunnally, Yin Aphinyanaphongs, Manolis Kellis, Rich Caruana
LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs. CoRR (2023)
Sarah Tan, Giles Hooker, Paul Koch, Albert Gordo, Rich Caruana
Considerations when learning additive explanations for black-box models. Mach. Learn. 112 (9) (2023)
Daniel Schug, Sai S. Yerramreddy, Rich Caruana, Craig Greenberg, Justyna P. Zwolak
Extending Explainable Boosting Machines to Scientific Image Data. CoRR (2023)
Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse. CHI (2023)
Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CHIL (2023)
Odelia Melamed, Rich Caruana
Explaining high-dimensional text classifiers. CoRR (2023)
Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Ian Painter, Vivienne Souter, Rich Caruana
Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes. CoRR (2023)
Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CoRR (2023)
Alexander Peysakhovich, Rich Caruana, Yin Aphinyanaphongs
Diagnosis Uncertain Models For Medical Risk Prediction. CoRR (2023)
Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan
Differentially Private Estimation of Heterogeneous Causal Effects. CoRR (2022)
Chun-Hao Chang, Sarah Tan, Benjamin Lengerich, Anna Goldenberg, Rich Caruana
How Interpretable and Trustworthy are GAMs? KDD (2021)
Chun-Hao Chang, George-Alexandru Adam, Rich Caruana, Anna Goldenberg
Extracting Clinician's Goals by What-if Interpretable Modeling. CoRR (2021)
Jonathan A. Weyn, Dale R. Durran, Rich Caruana, Nathaniel Cresswell-Clay
Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models. CoRR (2021)
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. ICML (2021)
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
GAM Changer: Editing Generalized Additive Models with Interactive Visualization. CoRR (2021)
Chun-Hao Chang, Rich Caruana, Anna Goldenberg
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning. CoRR (2021)
Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna M. Wallach, Jennifer Wortman Vaughan
Summarize with Caution: Comparing Global Feature Attributions. IEEE Data Eng. Bull. 44 (4) (2021)
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna M. Wallach, Jennifer Wortman Vaughan
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. DaSH@KDD (2021)
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. CoRR (2021)
Rishabh Agarwal, Nicholas Frosst, Xuezhou Zhang, Rich Caruana, Geoffrey E. Hinton
Neural Additive Models: Interpretable Machine Learning with Neural Nets. CoRR (2020)
Benjamin Lengerich, Eric P. Xing, Rich Caruana
On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks. CoRR (2020)
Chun-Hao Chang, Sarah Tan, Benjamin Lengerich, Anna Goldenberg, Rich Caruana
How Interpretable and Trustworthy are GAMs? CoRR (2020)
Rich Caruana, Scott M. Lundberg, Marco Túlio Ribeiro, Harsha Nori, Samuel Jenkins
Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges. KDD (2020)
Benjamin Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. AISTATS (2020)
Jonathan A. Weyn, Dale R. Durran, Rich Caruana
Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere. CoRR (2020)
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna M. Wallach, Jennifer Wortman Vaughan
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. CHI (2020)
Fred Hohman, Andrew Head, Rich Caruana, Robert DeLine, Steven Mark Drucker
Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models. CHI (2019)
Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana
InterpretML: A Unified Framework for Machine Learning Interpretability. CoRR (2019)
Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey
Efficient Forward Architecture Search. CoRR (2019)
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec
Faithful and Customizable Explanations of Black Box Models. AIES (2019)
Benjamin Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. CoRR (2019)
Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana
Axiomatic Interpretability for Multiclass Additive Models. KDD (2019)
Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey
Efficient Forward Architecture Search. NeurIPS (2019)
Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana
Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models. CoRR (2018)
Charles Sutton, Timothy Hobson, James Geddes, Rich Caruana
Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling. KDD (2018)
Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation. AIES (2018)
Sarah Tan, Rich Caruana, Giles Hooker, Albert Gordo
Transparent Model Distillation. CoRR (2018)
Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou
Detecting Bias in Black-Box Models Using Transparent Model Distillation. CoRR (2017)
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec
Interpretable & Explorable Approximations of Black Box Models. CoRR (2017)
Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou, Özlem Aslan, Shengjie Wang, Abdelrahman Mohamed, Matthai Philipose, Matthew Richardson, Rich Caruana
Do Deep Convolutional Nets Really Need to be Deep and Convolutional? ICLR (Poster) (2017)
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz
Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration. AAAI (2017)
Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou, Özlem Aslan, Shengjie Wang, Rich Caruana, Abdelrahman Mohamed, Matthai Philipose, Matthew Richardson
Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)? CoRR (2016)
Bhaskar Mitra, Eric T. Nalisnick, Nick Craswell, Rich Caruana
A Dual Embedding Space Model for Document Ranking. CoRR (2016)
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz
Discovering Blind Spots of Predictive Models: Representations and Policies for Guided Exploration. CoRR (2016)
Shengjie Wang, Abdel-rahman Mohamed, Rich Caruana, Jeff A. Bilmes, Matthai Philipose, Matthew Richardson, Krzysztof J. Geras, Gregor Urban, Özlem Aslan
Analysis of Deep Neural Networks with Extended Data Jacobian Matrix. ICML (2016)
Jia-Bin Huang, Rich Caruana, Andrew Farnsworth, Steve Kelling, Narendra Ahuja
Detecting Migrating Birds at Night. CVPR (2016)
Eric T. Nalisnick, Bhaskar Mitra, Nick Craswell, Rich Caruana
Improving Document Ranking with Dual Word Embeddings. WWW (Companion Volume) (2016)
Paul N. Bennett, Milad Shokouhi, Rich Caruana
Implicit Preference Labels for Learning Highly Selective Personalized Rankers. ICTIR (2015)
Krzysztof J. Geras, Abdel-rahman Mohamed, Rich Caruana, Gregor Urban, Shengjie Wang, Özlem Aslan, Matthai Philipose, Matthew Richardson, Charles Sutton
Compressing LSTMs into CNNs. CoRR (2015)
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, Noemie Elhadad
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission. KDD (2015)
Debadeepta Dey, Andrey Kolobov, Rich Caruana, Ece Kamar, Eric Horvitz, Ashish Kapoor
Gauss meets Canadian traveler: shortest-path problems with correlated natural dynamics. AAMAS (2014)
Todd Kulesza, Saleema Amershi, Rich Caruana, Danyel Fisher, Denis Xavier Charles
Structured labeling for facilitating concept evolution in machine learning. CHI (2014)
Jimmy Ba, Rich Caruana
Do Deep Nets Really Need to be Deep? NIPS (2014)
Alnur Ali, Rich Caruana, Ashish Kapoor
Active Learning with Model Selection. AAAI (2014)
Yin Lou, Jacob Bien, Rich Caruana, Johannes Gehrke
Sparse Partially Linear Additive Models. CoRR (2014)
John Krumm, Rich Caruana, Scott Counts
Learning Likely Locations. UMAP (2013)
Yin Lou, Rich Caruana, Johannes Gehrke, Giles Hooker
Accurate intelligible models with pairwise interactions. KDD (2013)
Rich Caruana
Clustering: probably approximately useless? CIKM (2013)
Lei Jimmy Ba, Rich Caruana
Do Deep Nets Really Need to be Deep? CoRR (2013)
Deepak Agarwal, Rich Caruana, Jian Pei, Ke Wang
Introduction to the Special Issue ACM SIGKDD 2012. ACM Trans. Knowl. Discov. Data 7 (3) (2013)
Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana
Using Multiple Samples to Learn Mixture Models. CoRR (2013)
Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana
Using multiple samples to learn mixture models. NIPS (2013)
Oriol Vinyals, Dan Bohus, Rich Caruana
Learning speaker, addressee and overlap detection models from multimodal streams. ICMI (2012)
Joydeep Ghosh, Padhraic Smyth, Andrew Tomkins, Rich Caruana
Special issue on best of SIGKDD 2011. ACM Trans. Knowl. Discov. Data 6 (4) (2012)
Yin Lou, Rich Caruana, Johannes Gehrke
Intelligible models for classification and regression. KDD (2012)
Alexandru Niculescu-Mizil, Rich Caruana
Inductive Transfer for Bayesian Network Structure Learning. ICML Unsupervised and Transfer Learning (2012)
Alexandru Niculescu-Mizil, Rich Caruana
Obtaining Calibrated Probabilities from Boosting CoRR (2012)
Yasser Ganjisaffar, Rich Caruana, Cristina Videira Lopes
Bagging gradient-boosted trees for high precision, low variance ranking models. SIGIR (2011)
M. Arthur Munson, Rich Caruana
On Feature Selection, Bias-Variance, and Bagging. ECML/PKDD (2) (2009)
Daria Sorokina, Rich Caruana, Mirek Riedewald, Wesley M. Hochachka, Steve Kelling
Detecting and Interpreting Variable Interactions in Observational Ornithology Data. ICDM Workshops (2009)
Rich Caruana, Nikolaos Karampatziakis, Ainur Yessenalina
An empirical evaluation of supervised learning in high dimensions. ICML (2008)
Daria Sorokina, Rich Caruana, Mirek Riedewald, Daniel Fink
Detecting statistical interactions with additive groves of trees. ICML (2008)
Nam Nguyen, Rich Caruana
Improving Classification with Pairwise Constraints: A Margin-Based Approach. ECML/PKDD (2) (2008)
Nam Nguyen, Rich Caruana
Classification with partial labels. KDD (2008)
Engin Ipek, Sally A. McKee, Karan Singh, Rich Caruana, Bronis R. de Supinski, Martin Schulz
Efficient architectural design space exploration via predictive modeling. ACM Trans. Archit. Code Optim. 4 (4) (2008)
Engin Ipek, Onur Mutlu, José F. Martínez, Rich Caruana
Self-Optimizing Memory Controllers: A Reinforcement Learning Approach. ISCA (2008)
Nam Nguyen, Rich Caruana
Consensus Clusterings. ICDM (2007)
Karan Singh, Engin Ipek, Sally A. McKee, Bronis R. de Supinski, Martin Schulz, Rich Caruana
Predicting parallel application performance via machine learning approaches. Concurr. Comput. Pract. Exp. 19 (17) (2007)
David B. Skalak, Alexandru Niculescu-Mizil, Rich Caruana
Classifier Loss Under Metric Uncertainty. ECML (2007)
Daria Sorokina, Rich Caruana, Mirek Riedewald
Additive Groves of Regression Trees. ECML (2007)
Alexandru Niculescu-Mizil, Rich Caruana
Inductive Transfer for Bayesian Network Structure Learning. AISTATS (2007)
Rich Caruana, Mohamed Farid Elhawary, Nam Nguyen, Casey Smith
Meta Clustering. ICDM (2006)
Rich Caruana, Art Munson, Alexandru Niculescu-Mizil
Getting the Most Out of Ensemble Selection. ICDM (2006)
Cristian Bucila, Rich Caruana, Alexandru Niculescu-Mizil
Model compression. KDD (2006)
Rich Caruana, Alexandru Niculescu-Mizil
An empirical comparison of supervised learning algorithms. ICML (2006)
Lars Backstrom, Rich Caruana
C2FS: An Algorithm for Feature Selection in Cascade Neural Networks. IJCNN (2006)
Engin Ipek, Sally A. McKee, Rich Caruana, Bronis R. de Supinski, Martin Schulz
Efficiently exploring architectural design spaces via predictive modeling. ASPLOS (2006)
Rich Caruana, Mohamed Farid Elhawary, Art Munson, Mirek Riedewald, Daria Sorokina, Daniel Fink, Wesley M. Hochachka, Steve Kelling
Mining citizen science data to predict orevalence of wild bird species. KDD (2006)
Alexandru Niculescu-Mizil, Rich Caruana
Predicting good probabilities with supervised learning. ICML (2005)
Gregory F. Cooper, Vijoy Abraham, Constantin F. Aliferis, John M. Aronis, Bruce G. Buchanan, Rich Caruana, Michael J. Fine, Janine E. Janosky, Gary Livingston, Tom M. Mitchell
Predicting dire outcomes of patients with community acquired pneumonia. J. Biomed. Informatics 38 (5) (2005)
Alexandru Niculescu-Mizil, Rich Caruana
Obtaining Calibrated Probabilities from Boosting. UAI (2005)
Art Munson, Claire Cardie, Rich Caruana
Optimizing to Arbitrary NLP Metrics using Ensemble Selection. HLT/EMNLP (2005)
Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, Alex Ksikes
Ensemble selection from libraries of models. ICML (2004)
Rich Caruana, Alexandru Niculescu-Mizil
An Empirical Evaluation of Supervised Learning for ROC Area. ROCAI (2004)
Rich Caruana, Thorsten Joachims, Lars Backstrom
KDD-Cup 2004: results and analysis. SIGKDD Explor. 6 (2) (2004)
Rich Caruana, Alexandru Niculescu-Mizil
Data mining in metric space: an empirical analysis of supervised learning performance criteria. KDD (2004)
Rich Caruana, Alexandru Niculescu-Mizil
Data Mining in Metric Space: An Empirical Analysis of Supervised Learning Performance Criteria. ROCAI (2004)
Rich Caruana, Radu Stefan Niculescu, R. Bharat Rao, Cynthia Simms
Evaluating the C-section Rate of Different Physician Practices: Using Machine Learning to Model Standard Practice. AMIA (2003)

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Publication Activity (10 Years)

Top Topics

Top Venues

Rich Caruana