Rich Caruana

According to our database, Rich Caruana authored at least 133 papers between 1988 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes.

Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Ian S. Painter, Vivienne Souter, Rich Caruana

J. Heal. Informatics Res., March, 2024

GAMformer: In-Context Learning for Generalized Additive Models.

Andreas Mueller, Julien Siems, Harsha Nori, David Salinas, Arber Zela, Rich Caruana, Frank Hutter

CoRR, 2024

Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models.

Sebastian Bordt, Harsha Nori, Vanessa Rodrigues, Besmira Nushi, Rich Caruana

CoRR, 2024

Elephants Never Forget: Testing Language Models for Memorization of Tabular Data.

Sebastian Bordt, Harsha Nori, Rich Caruana

CoRR, 2024

Data Science with LLMs and Interpretable Models.

Sebastian Bordt, Benjamin J. Lengerich, Harsha Nori, Rich Caruana

CoRR, 2024

Rethinking Interpretability in the Era of Large Language Models.

Chandan Singh, Jeevana Priya Inala, Michel Galley, Rich Caruana, Jianfeng Gao

CoRR, 2024

2023

Considerations when learning additive explanations for black-box models.

Sarah Tan, Giles Hooker, Paul Koch, Albert Gordo, Rich Caruana

Mach. Learn., September, 2023

Explaining high-dimensional text classifiers.

Odelia Melamed, Rich Caruana

CoRR, 2023

LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs.

Benjamin J. Lengerich, Sebastian Bordt, Harsha Nori, Mark E. Nunnally, Yin Aphinyanaphongs, Manolis Kellis, Rich Caruana

CoRR, 2023

Diagnosis Uncertain Models For Medical Risk Prediction.

Alexander Peysakhovich, Rich Caruana, Yin Aphinyanaphongs

CoRR, 2023

Extending Explainable Boosting Machines to Scientific Image Data.

Daniel Schug, Sai S. Yerramreddy, Rich Caruana, Craig Greenberg, Justyna P. Zwolak

CoRR, 2023

Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?

Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana

Proceedings of the Conference on Health, Inference, and Learning, 2023

GAM Coach: Towards Interactive and User-centered Algorithmic Recourse.

Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

2022

Automated interpretable discovery of heterogeneous treatment effectiveness: A COVID-19 case study.

Benjamin J. Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs, Caleb Ellington, Rich Caruana

J. Biomed. Informatics, 2022

Interpretable machine learning for predicting pathologic complete response in patients treated with chemoradiation therapy for rectal adenocarcinoma.

Du Wang, Sang Ho Lee, Huaizhi Geng, Haoyu Zhong, John Plastaras, Andrzej Wojcieszynski, Richard Caruana, Ying Xiao

Frontiers Artif. Intell., 2022

Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML.

Benjamin J. Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs, Rich Caruana

CoRR, 2022

Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes.

Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Kristin Sitcov, Vivienne Souter, Rich Caruana

CoRR, 2022

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

Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Why Data Scientists Prefer Glassbox Machine Learning: Algorithms, Differential Privacy, Editing and Bias Mitigation.

Rich Caruana, Harsha Nori

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning.

Chun-Hao Chang, Rich Caruana, Anna Goldenberg

Proceedings of the Tenth International Conference on Learning Representations, 2022

Differentially Private Estimation of Heterogeneous Causal Effects.

Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

Dropout as a Regularizer of Interaction Effects.

Benjamin J. Lengerich, Eric P. Xing, Rich Caruana

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Summarize with Caution: Comparing Global Feature Attributions.

Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna M. Wallach, Jennifer Wortman Vaughan

IEEE Data Eng. Bull., 2021

GAM Changer: Editing Generalized Additive Models with Interactive Visualization.

Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana

CoRR, 2021

Extracting Clinician's Goals by What-if Interpretable Modeling.

Chun-Hao Chang, George-Alexandru Adam, Rich Caruana, Anna Goldenberg

CoRR, 2021

Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models.

Jonathan A. Weyn, Dale R. Durran, Rich Caruana, Nathaniel Cresswell-Clay

CoRR, 2021

Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data.

Zhi Chen, Sarah Tan, Harsha Nori, Kori Inkpen, Yin Lou, Rich Caruana

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021

Neural Additive Models: Interpretable Machine Learning with Neural Nets.

Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Benjamin J. Lengerich, Rich Caruana, Geoffrey E. Hinton

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning.

Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna M. Wallach, Jennifer Wortman Vaughan

Proceedings of the 3rd Workshop on Data Science with Human in the Loop, 2021

How Interpretable and Trustworthy are GAMs?

Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Accuracy, Interpretability, and Differential Privacy via Explainable Boosting.

Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni

Proceedings of the 38th International Conference on Machine Learning, 2021

Data-Driven Patterns in Protective Effects of Ibuprofen and Ketorolac on Hospitalized Covid-19 Patients.

Rich Caruana, Benjamin J. Lengerich, Yindalon Aphinyanaphongs

Proceedings of the AMIA 2021, American Medical Informatics Association Annual Symposium, San Diego, CA, USA, October 30, 2021, 2021

2020

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

Benjamin J. Lengerich, Eric P. Xing, Rich Caruana

CoRR, 2020

Neural Additive Models: Interpretable Machine Learning with Neural Nets.

Rishabh Agarwal, Nicholas Frosst, Xuezhou Zhang, Rich Caruana, Geoffrey E. Hinton

CoRR, 2020

Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere.

Jonathan A. Weyn, Dale R. Durran, Rich Caruana

CoRR, 2020

Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges.

Rich Caruana, Scott M. Lundberg, Marco Túlio Ribeiro, Harsha Nori, Samuel Jenkins

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

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

Benjamin J. Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

InterpretML: A Unified Framework for Machine Learning Interpretability.

Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana

CoRR, 2019

Efficient Forward Architecture Search.

Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Axiomatic Interpretability for Multiclass Additive Models.

Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Friends Don't Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning.

Richard Caruana

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models.

Fred Hohman, Andrew Head, Rich Caruana, Robert DeLine, Steven Mark Drucker

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019

Faithful and Customizable Explanations of Black Box Models.

Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec

Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2018

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

Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana

CoRR, 2018

Transparent Model Distillation.

Sarah Tan, Rich Caruana, Giles Hooker, Albert Gordo

CoRR, 2018

Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling.

Charles Sutton, Timothy Hobson, James Geddes, Rich Caruana

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation.

Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou

Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018

2017

Detecting Bias in Black-Box Models Using Transparent Model Distillation.

Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou

CoRR, 2017

Interpretable & Explorable Approximations of Black Box Models.

Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec

CoRR, 2017

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

Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou, Özlem Aslan, Shengjie Wang, Abdelrahman Mohamed, Matthai Philipose, Matthew Richardson, Rich Caruana

Proceedings of the 5th International Conference on Learning Representations, 2017

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

Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)?

Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou, Özlem Aslan, Shengjie Wang, Rich Caruana, Abdelrahman Mohamed, Matthai Philipose, Matthew Richardson

CoRR, 2016

A Dual Embedding Space Model for Document Ranking.

Bhaskar Mitra, Eric T. Nalisnick, Nick Craswell, Rich Caruana

CoRR, 2016

Discovering Blind Spots of Predictive Models: Representations and Policies for Guided Exploration.

Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz

CoRR, 2016

Improving Document Ranking with Dual Word Embeddings.

Eric T. Nalisnick, Bhaskar Mitra, Nick Craswell, Rich Caruana

Proceedings of the 25th International Conference on World Wide Web, 2016

Analysis of Deep Neural Networks with Extended Data Jacobian Matrix.

Shengjie Wang, Abdel-rahman Mohamed, Rich Caruana, Jeff A. Bilmes, Matthai Philipose, Matthew Richardson, Krzysztof J. Geras, Gregor Urban, Özlem Aslan

Proceedings of the 33nd International Conference on Machine Learning, 2016

Detecting Migrating Birds at Night.

Jia-Bin Huang, Rich Caruana, Andrew Farnsworth, Steve Kelling, Narendra Ahuja

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

2015

Compressing LSTMs into CNNs.

Krzysztof J. Geras, Abdel-rahman Mohamed, Rich Caruana, Gregor Urban, Shengjie Wang, Özlem Aslan, Matthai Philipose, Matthew Richardson, Charles Sutton

CoRR, 2015

Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission.

Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, Noemie Elhadad

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Implicit Preference Labels for Learning Highly Selective Personalized Rankers.

Paul N. Bennett, Milad Shokouhi, Rich Caruana

Proceedings of the 2015 International Conference on The Theory of Information Retrieval, 2015

2014

Sparse Partially Linear Additive Models.

Yin Lou, Jacob Bien, Rich Caruana, Johannes Gehrke

CoRR, 2014

Do Deep Nets Really Need to be Deep?

Jimmy Ba, Rich Caruana

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Structured labeling for facilitating concept evolution in machine learning.

Todd Kulesza, Saleema Amershi, Rich Caruana, Danyel Fisher, Denis Xavier Charles

Proceedings of the CHI Conference on Human Factors in Computing Systems, 2014

Gauss meets Canadian traveler: shortest-path problems with correlated natural dynamics.

Debadeepta Dey, Andrey Kolobov, Rich Caruana, Ece Kamar, Eric Horvitz, Ashish Kapoor

Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2014

Active Learning with Model Selection.

Alnur Ali, Rich Caruana, Ashish Kapoor

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013

Learning to Detect Vandalism in Social Content Systems: A Study on Wikipedia - Vandalism Detection in Wikipedia.

Sara Javanmardi, David W. McDonald, Rich Caruana, Sholeh Forouzan, Cristina V. Lopes

Proceedings of the Mining Social Networks and Security Informatics, 2013

Introduction to the Special Issue ACM SIGKDD 2012.

Deepak Agarwal, Rich Caruana, Jian Pei, Ke Wang

ACM Trans. Knowl. Discov. Data, 2013

Learning Likely Locations.

John Krumm, Rich Caruana, Scott Counts

Proceedings of the User Modeling, Adaptation, and Personalization, 2013

Using multiple samples to learn mixture models.

Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Accurate intelligible models with pairwise interactions.

Yin Lou, Rich Caruana, Johannes Gehrke, Giles Hooker

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Clustering: probably approximately useless?

Rich Caruana

Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, 2013

2012

A Dozen Tricks with Multitask Learning.

Rich Caruana

Proceedings of the Neural Networks: Tricks of the Trade - Second Edition, 2012

Special issue on best of SIGKDD 2011.

Joydeep Ghosh, Padhraic Smyth, Andrew Tomkins, Rich Caruana

ACM Trans. Knowl. Discov. Data, 2012

Inductive Transfer for Bayesian Network Structure Learning.

Alexandru Niculescu-Mizil, Rich Caruana

Proceedings of the Unsupervised and Transfer Learning, 2012

Intelligible models for classification and regression.

Yin Lou, Rich Caruana, Johannes Gehrke

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

Learning speaker, addressee and overlap detection models from multimodal streams.

Oriol Vinyals, Dan Bohus, Rich Caruana

Proceedings of the International Conference on Multimodal Interaction, 2012

2011

Bagging gradient-boosted trees for high precision, low variance ranking models.

Yasser Ganjisaffar, Rich Caruana, Cristina Videira Lopes

Proceedings of the Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2011

2009

On Feature Selection, Bias-Variance, and Bagging.

M. Arthur Munson, Rich Caruana

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009

Detecting and Interpreting Variable Interactions in Observational Ornithology Data.

Daria Sorokina, Rich Caruana, Mirek Riedewald, Wesley M. Hochachka, Steve Kelling

Proceedings of the ICDM Workshops 2009, 2009

2008

Efficient architectural design space exploration via predictive modeling.

Engin Ipek, Sally A. McKee, Karan Singh, Rich Caruana, Bronis R. de Supinski, Martin Schulz

ACM Trans. Archit. Code Optim., 2008

Improving Classification with Pairwise Constraints: A Margin-Based Approach.

Nam Nguyen, Rich Caruana

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

Classification with partial labels.

Nam Nguyen, Rich Caruana

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008

Self-Optimizing Memory Controllers: A Reinforcement Learning Approach.

Engin Ipek, Onur Mutlu, José F. Martínez, Rich Caruana

Proceedings of the 35th International Symposium on Computer Architecture (ISCA 2008), 2008

Detecting statistical interactions with additive groves of trees.

Daria Sorokina, Rich Caruana, Mirek Riedewald, Daniel Fink

Proceedings of the Machine Learning, 2008

An empirical evaluation of supervised learning in high dimensions.

Rich Caruana, Nikolaos Karampatziakis, Ainur Yessenalina

Proceedings of the Machine Learning, 2008

2007

Predicting parallel application performance via machine learning approaches.

Karan Singh, Engin Ipek, Sally A. McKee, Bronis R. de Supinski, Martin Schulz, Rich Caruana

Concurr. Comput. Pract. Exp., 2007

Consensus Clusterings.

Nam Nguyen, Rich Caruana

Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), 2007

Additive Groves of Regression Trees.

Daria Sorokina, Rich Caruana, Mirek Riedewald

Proceedings of the Machine Learning: ECML 2007, 2007

Classifier Loss Under Metric Uncertainty.

David B. Skalak, Alexandru Niculescu-Mizil, Rich Caruana

Proceedings of the Machine Learning: ECML 2007, 2007

2006

Mining citizen science data to predict orevalence of wild bird species.

Rich Caruana, Mohamed Farid Elhawary, Art Munson, Mirek Riedewald, Daria Sorokina, Daniel Fink, Wesley M. Hochachka, Steve Kelling

Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006

Model compression.

Cristian Bucila, Rich Caruana, Alexandru Niculescu-Mizil

Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006

C2FS: An Algorithm for Feature Selection in Cascade Neural Networks.

Lars Backstrom, Rich Caruana

Proceedings of the International Joint Conference on Neural Networks, 2006

An empirical comparison of supervised learning algorithms.

Rich Caruana, Alexandru Niculescu-Mizil

Proceedings of the Machine Learning, 2006

Getting the Most Out of Ensemble Selection.

Rich Caruana, Art Munson, Alexandru Niculescu-Mizil

Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 2006

Meta Clustering.

Rich Caruana, Mohamed Farid Elhawary, Nam Nguyen, Casey Smith

Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 2006

Efficiently exploring architectural design spaces via predictive modeling.

Engin Ipek, Sally A. McKee, Rich Caruana, Bronis R. de Supinski, Martin Schulz

Proceedings of the 12th International Conference on Architectural Support for Programming Languages and Operating Systems, 2006

2005

Predicting dire outcomes of patients with community acquired pneumonia.

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

J. Biomed. Informatics, 2005

Obtaining Calibrated Probabilities from Boosting.

Alexandru Niculescu-Mizil, Rich Caruana

Proceedings of the UAI '05, 2005

Optimizing to Arbitrary NLP Metrics using Ensemble Selection.

Art Munson, Claire Cardie, Rich Caruana

Proceedings of the HLT/EMNLP 2005, 2005

Predicting good probabilities with supervised learning.

Alexandru Niculescu-Mizil, Rich Caruana

Proceedings of the Machine Learning, 2005

2004

KDD-Cup 2004: results and analysis.

Rich Caruana, Thorsten Joachims, Lars Backstrom

SIGKDD Explor., 2004

An Empirical Evaluation of Supervised Learning for ROC Area.

Rich Caruana, Alexandru Niculescu-Mizil

Proceedings of the ROC Analysis in Artificial Intelligence, 1st International Workshop, 2004

Data mining in metric space: an empirical analysis of supervised learning performance criteria.

Rich Caruana, Alexandru Niculescu-Mizil

Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004

Ensemble selection from libraries of models.

Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, Alex Ksikes

Proceedings of the Machine Learning, 2004

2003

Benefitting from the Variables that Variable Selection Discards.

Rich Caruana, Virginia R. de Sa

J. Mach. Learn. Res., 2003

Evaluating the C-section Rate of Different Physician Practices: Using Machine Learning to Model Standard Practice.

Rich Caruana, Radu Stefan Niculescu, R. Bharat Rao, Cynthia Simms

Proceedings of the AMIA 2003, 2003

2002

Machine learning for sub-population assessment: evaluating the C-section rate of different physician practices.

Rich Caruana, Radu Stefan Niculescu, R. Bharat Rao, Cynthia Simms

Proceedings of the AMIA 2002, 2002

2001

(Not) Bounding the True Error.

John Langford, Rich Caruana

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

A Non-Parametric EM-Style Algorithm for Imputing Missing Values.

Rich Caruana

Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001

2000

Bridging the lexical chasm: statistical approaches to answer-finding.

Adam L. Berger, Rich Caruana, David Cohn, Dayne Freitag, Vibhu O. Mittal

Proceedings of the SIGIR 2000: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2000

Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping.

Rich Caruana, Steve Lawrence, C. Lee Giles

Proceedings of the Advances in Neural Information Processing Systems 13, 2000

FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness.

Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum

Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29, 2000

Case-Based Explanation for Artificial Neural Nets.

Rich Caruana

Proceedings of the Artificial Neural Networks in Medicine and Biology, 2000

1999

Case-based explanation of non-case-based learning methods.

Rich Caruana, Hooshang Kangarloo, John D. N. Dionisio, Usha Sinha, David B. Johnson

Proceedings of the AMIA 1999, 1999

1998

Multitask pattern recognition for autonomous robots.

Rich Caruana, Joseph O'Sullivan

Proceedings of the Proceedings 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, 1998

Multitask Learning.

Rich Caruana

Proceedings of the Learning to Learn., 1998

1997

Multitask Learning.

Rich Caruana

Mach. Learn., 1997

An evaluation of machine-learning methods for predicting pneumonia mortality.

Gregory F. Cooper, Constantin F. Aliferis, Richard Ambrosino, John M. Aronis, Bruce G. Buchanan, Rich Caruana, Michael J. Fine, Clark Glymour, Geoffrey J. Gordon, Barbara H. Hanusa, Janine E. Janosky, Christopher Meek, Tom M. Mitchell, Thomas S. Richardson, Peter Spirtes

Artif. Intell. Medicine, 1997

1996

Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs.

Rich Caruana, Virginia R. de Sa

Proceedings of the Advances in Neural Information Processing Systems 9, 1996

A Dozen Tricks with Multitask Learning.

Rich Caruana

Proceedings of the Neural Networks: Tricks of the Trade, 1996

Algorithms and Applications for Multitask Learning.

Rich Caruana

Proceedings of the Machine Learning, 1996

1995

Using the Future to Sort Out the Present: Rankprop and Multitask Learning for Medical Risk Evaluation.

Rich Caruana, Shumeet Baluja, Tom M. Mitchell

Proceedings of the Advances in Neural Information Processing Systems 8, 1995

Removing the Genetics from the Standard Genetic Algorithm.

Shumeet Baluja, Rich Caruana

Proceedings of the Machine Learning, 1995

1994

Experience with a Learning Personal Assistant.

Tom M. Mitchell, Rich Caruana, Dayne Freitag, John P. McDermott, David Zabowski

Commun. ACM, 1994

Learning Many Related Tasks at the Same Time with Backpropagation.

Rich Caruana

Proceedings of the Advances in Neural Information Processing Systems 7, 1994

Greedy Attribute Selection.

Rich Caruana, Dayne Freitag

Proceedings of the Machine Learning, 1994

1993

Multitask Learning: A Knowledge-Based Source of Inductive Bias.

Rich Caruana

Proceedings of the Machine Learning, 1993

1989

Representation and Hidden Bias II: Eliminating Defining Length Bias in Genetic Search via Shuffle Crossover.

Rich Caruana, Larry J. Eshelman, J. David Schaffer

Proceedings of the 11th International Joint Conference on Artificial Intelligence. Detroit, 1989

Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms.

Rich Caruana, J. David Schaffer, Larry J. Eshelman

Proceedings of the Sixth International Workshop on Machine Learning (ML 1989), 1989

A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization.

J. David Schaffer, Rich Caruana, Larry J. Eshelman, Rajarshi Das

Proceedings of the 3rd International Conference on Genetic Algorithms, 1989

Biases in the Crossover Landscape.

Larry J. Eshelman, Rich Caruana, J. David Schaffer

Proceedings of the 3rd International Conference on Genetic Algorithms, 1989

1988

The automatic training of rule bases that use numerical uncertainty representations.

Rich Caruana

Int. J. Approx. Reason., 1988

Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms.

Rich Caruana, J. David Schaffer

Proceedings of the Machine Learning, 1988