Rich Caruana - RankMe

151 papers A* 44A 10B 2C 2Misc 5Journal 57Unranked 27

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	The Most Important Features in Generalized Additive Models Might Be Groups of Features. CoRR Tomas M. Bosschieter, Luis Franca, Jessica Wolk, Yiyuan Wu, Bella Mehta, Joseph Dehoney, Orsolya Kiss, Fiona C. Baker, Qingyu Zhao, Rich Caruana, Kilian M. Pohl
2025	J	`jnl`	The hidden risk of round numbers and sharp thresholds in clinical practice. npj Digit. Medicine Benjamin J. Lengerich, Rich Caruana, Mark E. Nunnally, Manolis Kellis
2024	J	`jnl`	Data Science with LLMs and Interpretable Models. CoRR Sebastian Bordt, Benjamin J. Lengerich, Harsha Nori, Rich Caruana
2024	J	`jnl`	Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models. CoRR Sebastian Bordt, Harsha Nori, Vanessa Rodrigues, Besmira Nushi, Rich Caruana
2024	J	`jnl`	Elephants Never Forget: Testing Language Models for Memorization of Tabular Data. CoRR Sebastian Bordt, Harsha Nori, Rich Caruana
2024	J	`jnl`	GAMformer: In-Context Learning for Generalized Additive Models. CoRR Andreas Mueller, Julien Siems, Harsha Nori, David Salinas, Arber Zela, Rich Caruana, Frank Hutter
2024	J	`jnl`	Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes. J. Heal. Informatics Res. Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Ian S. Painter, Vivienne Souter, Rich Caruana
2024	J	`jnl`	Rethinking Interpretability in the Era of Large Language Models. CoRR Chandan Singh, Jeevana Priya Inala, Michel Galley, Rich Caruana, Jianfeng Gao
2023	J	`jnl`	Considerations when learning additive explanations for black-box models. Mach. Learn. Sarah Tan, Giles Hooker, Paul Koch, Albert Gordo, Rich Caruana
2023	J	`jnl`	Diagnosis Uncertain Models For Medical Risk Prediction. CoRR Alexander Peysakhovich, Rich Caruana, Yin Aphinyanaphongs
2023	J	`jnl`	Explaining high-dimensional text classifiers. CoRR Odelia Melamed, Rich Caruana
2023	J	`jnl`	Extending Explainable Boosting Machines to Scientific Image Data. CoRR Daniel Schug, Sai S. Yerramreddy, Rich Caruana, Craig Greenberg, Justyna P. Zwolak
2023	A*	`conf`	GAM Coach: Towards Interactive and User-centered Algorithmic Recourse. CHI Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau
2023	J	`jnl`	GAM Coach: Towards Interactive and User-centered Algorithmic Recourse. CoRR Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau
2023	J	`jnl`	Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes. CoRR Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Ian S. Painter, Vivienne Souter, Rich Caruana
2023	J	`jnl`	LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs. CoRR Benjamin J. Lengerich, Sebastian Bordt, Harsha Nori, Mark E. Nunnally, Yin Aphinyanaphongs, Manolis Kellis, Rich Caruana
2023	—	`conf`	Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CHIL Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana
2023	J	`jnl`	Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CoRR Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana
2022	J	`jnl`	Automated interpretable discovery of heterogeneous treatment effectiveness: A COVID-19 case study. J. Biomed. Informatics Benjamin J. Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs, Caleb Ellington, Rich Caruana
2022	—	`conf`	Differentially Private Estimation of Heterogeneous Causal Effects. CLeaR Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan
2022	J	`jnl`	Differentially Private Estimation of Heterogeneous Causal Effects. CoRR Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan
2022	A	`conf`	Dropout as a Regularizer of Interaction Effects. AISTATS Benjamin J. Lengerich, Eric P. Xing, Rich Caruana
2022	J	`jnl`	Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML. CoRR Benjamin J. Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs, Rich Caruana
2022	A*	`conf`	Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values. KDD Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
2022	J	`jnl`	Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values. CoRR Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
2022	A*	`conf`	NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning. ICLR Chun-Hao Chang, Rich Caruana, Anna Goldenberg
2022	J	`jnl`	Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes. CoRR Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Kristin Sitcov, Vivienne Souter, Rich Caruana
2022	A*	`conf`	Why Data Scientists Prefer Glassbox Machine Learning: Algorithms, Differential Privacy, Editing and Bias Mitigation. KDD Rich Caruana, Harsha Nori
2021	A*	`conf`	Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. ICML Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni
2021	J	`jnl`	Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. CoRR Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni
2021	Misc	`conf`	Data-Driven Patterns in Protective Effects of Ibuprofen and Ketorolac on Hospitalized Covid-19 Patients. AMIA Rich Caruana, Benjamin J. Lengerich, Yindalon Aphinyanaphongs
2021	J	`jnl`	Extracting Clinician's Goals by What-if Interpretable Modeling. CoRR Chun-Hao Chang, George-Alexandru Adam, Rich Caruana, Anna Goldenberg
2021	J	`jnl`	GAM Changer: Editing Generalized Additive Models with Interactive Visualization. CoRR Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
2021	A*	`conf`	How Interpretable and Trustworthy are GAMs? KDD Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana
2021	—	`conf`	Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. DaSH@KDD Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna M. Wallach, Jennifer Wortman Vaughan
2021	J	`jnl`	NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning. CoRR Chun-Hao Chang, Rich Caruana, Anna Goldenberg
2021	A*	`conf`	Neural Additive Models: Interpretable Machine Learning with Neural Nets. NeurIPS Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Benjamin J. Lengerich, Rich Caruana, Geoffrey E. Hinton
2021	J	`jnl`	Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models. CoRR Jonathan A. Weyn, Dale R. Durran, Rich Caruana, Nathaniel Cresswell-Clay
2021	J	`jnl`	Summarize with Caution: Comparing Global Feature Attributions. IEEE Data Eng. Bull. Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna M. Wallach, Jennifer Wortman Vaughan
2021	—	`conf`	Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data. PKDD/ECML Workshops (1) Zhi Chen, Sarah Tan, Harsha Nori, Kori Inkpen, Yin Lou, Rich Caruana
2020	J	`jnl`	How Interpretable and Trustworthy are GAMs? CoRR Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana
2020	J	`jnl`	Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere. CoRR Jonathan A. Weyn, Dale R. Durran, Rich Caruana
2020	A*	`conf`	Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges. KDD Rich Caruana, Scott M. Lundberg, Marco Túlio Ribeiro, Harsha Nori, Samuel Jenkins
2020	A*	`conf`	Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. CHI Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna M. Wallach, Jennifer Wortman Vaughan
2020	J	`jnl`	Neural Additive Models: Interpretable Machine Learning with Neural Nets. CoRR Rishabh Agarwal, Nicholas Frosst, Xuezhou Zhang, Rich Caruana, Geoffrey E. Hinton
2020	J	`jnl`	On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks. CoRR Benjamin J. Lengerich, Eric P. Xing, Rich Caruana
2020	A	`conf`	Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. AISTATS Benjamin J. Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana
2019	A*	`conf`	Axiomatic Interpretability for Multiclass Additive Models. KDD Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana
2019	A*	`conf`	Efficient Forward Architecture Search. NeurIPS Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey
2019	J	`jnl`	Efficient Forward Architecture Search. CoRR Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey
2019	C	`conf`	Faithful and Customizable Explanations of Black Box Models. AIES Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec
2019	A*	`conf`	Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models. CHI Fred Hohman, Andrew Head, Rich Caruana, Robert DeLine, Steven Mark Drucker
2019	J	`jnl`	InterpretML: A Unified Framework for Machine Learning Interpretability. CoRR Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana
2019	J	`jnl`	Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. CoRR Benjamin J. Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana
2018	A*	`conf`	Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling. KDD Charles Sutton, Timothy Hobson, James Geddes, Rich Caruana
2018	C	`conf`	Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation. AIES Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou
2018	J	`jnl`	Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models. CoRR Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana
2018	J	`jnl`	Transparent Model Distillation. CoRR Sarah Tan, Rich Caruana, Giles Hooker, Albert Gordo
2017	J	`jnl`	Detecting Bias in Black-Box Models Using Transparent Model Distillation. CoRR Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou
2017	—	`conf`	Do Deep Convolutional Nets Really Need to be Deep and Convolutional? ICLR (Poster) Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou, Özlem Aslan, Shengjie Wang, Abdelrahman Mohamed, Matthai Philipose, Matthew Richardson, Rich Caruana
2017	A*	`conf`	Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration. AAAI Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz
2017	J	`jnl`	Interpretable & Explorable Approximations of Black Box Models. CoRR Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec
2016	J	`jnl`	A Dual Embedding Space Model for Document Ranking. CoRR Bhaskar Mitra, Eric T. Nalisnick, Nick Craswell, Rich Caruana
2016	A*	`conf`	Analysis of Deep Neural Networks with Extended Data Jacobian Matrix. ICML Shengjie Wang, Abdel-rahman Mohamed, Rich Caruana, Jeff A. Bilmes, Matthai Philipose, Matthew Richardson, Krzysztof J. Geras, Gregor Urban, Özlem Aslan
2016	A*	`conf`	Detecting Migrating Birds at Night. CVPR Jia-Bin Huang, Rich Caruana, Andrew Farnsworth, Steve Kelling, Narendra Ahuja
2016	J	`jnl`	Discovering Blind Spots of Predictive Models: Representations and Policies for Guided Exploration. CoRR Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz
2016	J	`jnl`	Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)? CoRR Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou, Özlem Aslan, Shengjie Wang, Rich Caruana, Abdelrahman Mohamed, Matthai Philipose, Matthew Richardson
2016	—	`conf`	Improving Document Ranking with Dual Word Embeddings. WWW (Companion Volume) Eric T. Nalisnick, Bhaskar Mitra, Nick Craswell, Rich Caruana
2015	J	`jnl`	Compressing LSTMs into CNNs. CoRR Krzysztof J. Geras, Abdel-rahman Mohamed, Rich Caruana, Gregor Urban, Shengjie Wang, Özlem Aslan, Matthai Philipose, Matthew Richardson, Charles Sutton
2015	Misc	`conf`	Implicit Preference Labels for Learning Highly Selective Personalized Rankers. ICTIR Paul N. Bennett, Milad Shokouhi, Rich Caruana
2015	A*	`conf`	Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission. KDD Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, Noemie Elhadad
2014	A*	`conf`	Active Learning with Model Selection. AAAI Alnur Ali, Rich Caruana, Ashish Kapoor
2014	—	`conf`	Do Deep Nets Really Need to be Deep? NIPS Jimmy Ba, Rich Caruana
2014	A	`conf`	Gauss meets Canadian traveler: shortest-path problems with correlated natural dynamics. AAMAS Debadeepta Dey, Andrey Kolobov, Rich Caruana, Ece Kamar, Eric Horvitz, Ashish Kapoor
2014	J	`jnl`	Sparse Partially Linear Additive Models. CoRR Yin Lou, Jacob Bien, Rich Caruana, Johannes Gehrke
2014	A*	`conf`	Structured labeling for facilitating concept evolution in machine learning. CHI Todd Kulesza, Saleema Amershi, Rich Caruana, Danyel Fisher, Denis Xavier Charles
2013	A*	`conf`	Accurate intelligible models with pairwise interactions. KDD Yin Lou, Rich Caruana, Johannes Gehrke, Giles Hooker
2013	A	`conf`	Clustering: probably approximately useless? CIKM Rich Caruana
2013	J	`jnl`	Do Deep Nets Really Need to be Deep? CoRR Lei Jimmy Ba, Rich Caruana
2013	J	`jnl`	Introduction to the Special Issue ACM SIGKDD 2012. ACM Trans. Knowl. Discov. Data Deepak Agarwal, Rich Caruana, Jian Pei, Ke Wang
2013	A	`conf`	Learning Likely Locations. UMAP John Krumm, Rich Caruana, Scott Counts
2013	—	`ch.`	Learning to Detect Vandalism in Social Content Systems: A Study on Wikipedia - Vandalism Detection in Wikipedia. Mining Social Networks and Security Informatics Sara Javanmardi, David W. McDonald, Rich Caruana, Sholeh Forouzan, Cristina V. Lopes
2013	J	`jnl`	Using Multiple Samples to Learn Mixture Models. CoRR Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana
2013	—	`conf`	Using multiple samples to learn mixture models. NIPS Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana
2012	—	`ch.`	A Dozen Tricks with Multitask Learning. Neural Networks: Tricks of the Trade (2nd ed.) Rich Caruana
2012	—	`conf`	Inductive Transfer for Bayesian Network Structure Learning. ICML Unsupervised and Transfer Learning Alexandru Niculescu-Mizil, Rich Caruana
2012	A*	`conf`	Intelligible models for classification and regression. KDD Yin Lou, Rich Caruana, Johannes Gehrke
2012	B	`conf`	Learning speaker, addressee and overlap detection models from multimodal streams. ICMI Oriol Vinyals, Dan Bohus, Rich Caruana
2012	J	`jnl`	Obtaining Calibrated Probabilities from Boosting CoRR Alexandru Niculescu-Mizil, Rich Caruana
2012	J	`jnl`	Special issue on best of SIGKDD 2011. ACM Trans. Knowl. Discov. Data Joydeep Ghosh, Padhraic Smyth, Andrew Tomkins, Rich Caruana
2011	A*	`conf`	Bagging gradient-boosted trees for high precision, low variance ranking models. SIGIR Yasser Ganjisaffar, Rich Caruana, Cristina Videira Lopes
2009	—	`conf`	Detecting and Interpreting Variable Interactions in Observational Ornithology Data. ICDM Workshops Daria Sorokina, Rich Caruana, Mirek Riedewald, Wesley M. Hochachka, Steve Kelling
2009	—	`conf`	On Feature Selection, Bias-Variance, and Bagging. ECML/PKDD (2) M. Arthur Munson, Rich Caruana
2008	A*	`conf`	An empirical evaluation of supervised learning in high dimensions. ICML Rich Caruana, Nikolaos Karampatziakis, Ainur Yessenalina
2008	A*	`conf`	Classification with partial labels. KDD Nam Nguyen, Rich Caruana
2008	A*	`conf`	Detecting statistical interactions with additive groves of trees. ICML Daria Sorokina, Rich Caruana, Mirek Riedewald, Daniel Fink
2008	J	`jnl`	Efficient architectural design space exploration via predictive modeling. ACM Trans. Archit. Code Optim. Engin Ipek, Sally A. McKee, Karan Singh, Rich Caruana, Bronis R. de Supinski, Martin Schulz
2008	—	`conf`	Improving Classification with Pairwise Constraints: A Margin-Based Approach. ECML/PKDD (2) Nam Nguyen, Rich Caruana
2008	A*	`conf`	Self-Optimizing Memory Controllers: A Reinforcement Learning Approach. ISCA Engin Ipek, Onur Mutlu, José F. Martínez, Rich Caruana
2007	—	`conf`	Additive Groves of Regression Trees. ECML Daria Sorokina, Rich Caruana, Mirek Riedewald
2007	—	`conf`	Classifier Loss Under Metric Uncertainty. ECML David B. Skalak, Alexandru Niculescu-Mizil, Rich Caruana
2007	A*	`conf`	Consensus Clusterings. ICDM Nam Nguyen, Rich Caruana
2007	A	`conf`	Inductive Transfer for Bayesian Network Structure Learning. AISTATS Alexandru Niculescu-Mizil, Rich Caruana
2007	J	`jnl`	Predicting parallel application performance via machine learning approaches. Concurr. Comput. Pract. Exp. Karan Singh, Engin Ipek, Sally A. McKee, Bronis R. de Supinski, Martin Schulz, Rich Caruana
2007	A*	`ed.`	Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, California, USA, August 12-15, 2007 KDD Pavel Berkhin, Rich Caruana, Xindong Wu
2006	A*	`conf`	An empirical comparison of supervised learning algorithms. ICML Rich Caruana, Alexandru Niculescu-Mizil
2006	B	`conf`	C2FS: An Algorithm for Feature Selection in Cascade Neural Networks. IJCNN Lars Backstrom, Rich Caruana
2006	A*	`conf`	Efficiently exploring architectural design spaces via predictive modeling. ASPLOS Engin Ipek, Sally A. McKee, Rich Caruana, Bronis R. de Supinski, Martin Schulz
2006	A*	`conf`	Getting the Most Out of Ensemble Selection. ICDM Rich Caruana, Art Munson, Alexandru Niculescu-Mizil
2006	A*	`conf`	Meta Clustering. ICDM Rich Caruana, Mohamed Farid Elhawary, Nam Nguyen, Casey Smith
2006	A*	`conf`	Mining citizen science data to predict orevalence of wild bird species. KDD Rich Caruana, Mohamed Farid Elhawary, Art Munson, Mirek Riedewald, Daria Sorokina, Daniel Fink, Wesley M. Hochachka, Steve Kelling
2006	A*	`conf`	Model compression. KDD Cristian Bucila, Rich Caruana, Alexandru Niculescu-Mizil
2005	A	`conf`	Obtaining Calibrated Probabilities from Boosting. UAI Alexandru Niculescu-Mizil, Rich Caruana
2005	—	`conf`	Optimizing to Arbitrary NLP Metrics using Ensemble Selection. HLT/EMNLP Art Munson, Claire Cardie, Rich Caruana
2005	J	`jnl`	Predicting dire outcomes of patients with community acquired pneumonia. J. Biomed. Informatics 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
2005	A*	`conf`	Predicting good probabilities with supervised learning. ICML Alexandru Niculescu-Mizil, Rich Caruana
2004	—	`conf`	An Empirical Evaluation of Supervised Learning for ROC Area. ROCAI Rich Caruana, Alexandru Niculescu-Mizil
2004	—	`conf`	Data Mining in Metric Space: An Empirical Analysis of Supervised Learning Performance Criteria. ROCAI Rich Caruana, Alexandru Niculescu-Mizil
2004	A*	`conf`	Data mining in metric space: an empirical analysis of supervised learning performance criteria. KDD Rich Caruana, Alexandru Niculescu-Mizil
2004	A*	`conf`	Ensemble selection from libraries of models. ICML Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, Alex Ksikes
2004	J	`jnl`	KDD-Cup 2004: results and analysis. SIGKDD Explor. Rich Caruana, Thorsten Joachims, Lars Backstrom
2003	J	`jnl`	Benefitting from the Variables that Variable Selection Discards. J. Mach. Learn. Res. Rich Caruana, Virginia R. de Sa
2003	Misc	`conf`	Evaluating the C-section Rate of Different Physician Practices: Using Machine Learning to Model Standard Practice. AMIA Rich Caruana, Radu Stefan Niculescu, R. Bharat Rao, Cynthia Simms
2002	Misc	`conf`	Machine learning for sub-population assessment: evaluating the C-section rate of different physician practices. AMIA Rich Caruana, Radu Stefan Niculescu, R. Bharat Rao, Cynthia Simms
2001	—	`conf`	(Not) Bounding the True Error. NIPS John Langford, Rich Caruana
2001	A	`conf`	A Non-Parametric EM-Style Algorithm for Imputing Missing Values. AISTATS Rich Caruana
2000	A*	`conf`	Bridging the lexical chasm: statistical approaches to answer-finding. SIGIR Adam L. Berger, Rich Caruana, David Cohn, Dayne Freitag, Vibhu O. Mittal
2000	—	`conf`	Case-Based Explanation for Artificial Neural Nets. ANNIMAB Rich Caruana
2000	A*	`conf`	FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness. ICML Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum
2000	—	`conf`	Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping. NIPS Rich Caruana, Steve Lawrence, C. Lee Giles
1999	Misc	`conf`	Case-based explanation of non-case-based learning methods. AMIA Rich Caruana, Hooshang Kangarloo, John D. N. Dionisio, Usha Sinha, David B. Johnson
1998	—	`ch.`	Multitask Learning. Learning to Learn Rich Caruana
1998	A	`conf`	Multitask pattern recognition for autonomous robots. IROS Rich Caruana, Joseph O'Sullivan
1997	J	`jnl`	An evaluation of machine-learning methods for predicting pneumonia mortality. Artif. Intell. Medicine 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
1997	J	`jnl`	Multitask Learning. Mach. Learn. Rich Caruana
1996	—	`ch.`	A Dozen Tricks with Multitask Learning. Neural Networks: Tricks of the Trade Rich Caruana
1996	A*	`conf`	Algorithms and Applications for Multitask Learning. ICML Rich Caruana
1996	—	`conf`	Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs. NIPS Rich Caruana, Virginia R. de Sa
1995	A*	`conf`	Removing the Genetics from the Standard Genetic Algorithm. ICML Shumeet Baluja, Rich Caruana
1995	—	`conf`	Using the Future to Sort Out the Present: Rankprop and Multitask Learning for Medical Risk Evaluation. NIPS Rich Caruana, Shumeet Baluja, Tom M. Mitchell
1994	J	`jnl`	Experience with a Learning Personal Assistant. Commun. ACM Tom M. Mitchell, Rich Caruana, Dayne Freitag, John P. McDermott, David Zabowski
1994	A*	`conf`	Greedy Attribute Selection. ICML Rich Caruana, Dayne Freitag
1994	—	`conf`	Learning Many Related Tasks at the Same Time with Backpropagation. NIPS Rich Caruana
1993	A*	`conf`	Multitask Learning: A Knowledge-Based Source of Inductive Bias. ICML Rich Caruana
1989	—	`conf`	A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization. ICGA J. David Schaffer, Rich Caruana, Larry J. Eshelman, Rajarshi Das
1989	—	`conf`	Biases in the Crossover Landscape. ICGA Larry J. Eshelman, Rich Caruana, J. David Schaffer
1989	A*	`conf`	Representation and Hidden Bias II: Eliminating Defining Length Bias in Genetic Search via Shuffle Crossover. IJCAI Rich Caruana, Larry J. Eshelman, J. David Schaffer
1989	—	`conf`	Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms. ML Rich Caruana, J. David Schaffer, Larry J. Eshelman
1988	—	`conf`	Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms. ML Rich Caruana, J. David Schaffer
1988	J	`jnl`	The automatic training of rule bases that use numerical uncertainty representations. Int. J. Approx. Reason. Rich Caruana
1987	A	`conf`	The Automatic Training of Rule Bases that Use Numerical Uncertainty Representations. UAI Rich Caruana