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ORCIDRich Caruana
Richard Caruana – Richard A. Caruana
Person information
- affiliation: Microsoft Research, Redmond, WA, USA
- affiliation: Cornell University, Ithaca, NY, USA
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2020 – today
- 2024
[j16]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): 65-87 (2024)- [i41]Chandan Singh, Jeevana Priya Inala, Michel Galley, Rich Caruana, Jianfeng Gao:
Rethinking Interpretability in the Era of Large Language Models. CoRR abs/2402.01761 (2024) - [i40]Sebastian Bordt, Benjamin J. Lengerich, Harsha Nori, Rich Caruana:
Data Science with LLMs and Interpretable Models. CoRR abs/2402.14474 (2024) - [i39]Sebastian Bordt, Harsha Nori, Rich Caruana:
Elephants Never Forget: Testing Language Models for Memorization of Tabular Data. CoRR abs/2403.06644 (2024) - [i38]Sebastian Bordt, Harsha Nori, Vanessa Rodrigues, Besmira Nushi, Rich Caruana:
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models. CoRR abs/2404.06209 (2024) - [i37]Andreas Mueller, Julien Siems, Harsha Nori, David Salinas, Arber Zela, Rich Caruana, Frank Hutter:
GAMformer: In-Context Learning for Generalized Additive Models. CoRR abs/2410.04560 (2024) - 2023
- [j15]Sarah Tan, Giles Hooker, Paul Koch, Albert Gordo, Rich Caruana:
Considerations when learning additive explanations for black-box models. Mach. Learn. 112(9): 3333-3359 (2023) - [c90]Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau:
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse. CHI 2023: 835:1-835:20 - [c89]Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana:
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CHIL 2023: 86-99 - [i36]Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau:
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse. CoRR abs/2302.14165 (2023) - [i35]Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana:
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CoRR abs/2304.11749 (2023) - [i34]Daniel Schug, Sai S. Yerramreddy, Rich Caruana, Craig Greenberg, Justyna P. Zwolak:
Extending Explainable Boosting Machines to Scientific Image Data. CoRR abs/2305.16526 (2023) - [i33]Alexander Peysakhovich, Rich Caruana, Yin Aphinyanaphongs:
Diagnosis Uncertain Models For Medical Risk Prediction. CoRR abs/2306.17337 (2023) - [i32]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 abs/2308.01157 (2023) - [i31]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. CoRR abs/2310.10203 (2023) - [i30]Odelia Melamed, Rich Caruana:
Explaining high-dimensional text classifiers. CoRR abs/2311.13454 (2023) - 2022
- [j14]Du Wang, Sang Ho Lee, Huaizhi Geng, Haoyu Zhong, John Plastaras, Andrzej Wojcieszynski, Richard Caruana, Ying Xiao:
Interpretable machine learning for predicting pathologic complete response in patients treated with chemoradiation therapy for rectal adenocarcinoma. Frontiers Artif. Intell. 5 (2022) - [j13]Benjamin J. Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs, Caleb Ellington, Rich Caruana:
Automated interpretable discovery of heterogeneous treatment effectiveness: A COVID-19 case study. J. Biomed. Informatics 130: 104086 (2022) - [c88]Benjamin J. Lengerich, Eric P. Xing, Rich Caruana:
Dropout as a Regularizer of Interaction Effects. AISTATS 2022: 7550-7564 - [c87]Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan:
Differentially Private Estimation of Heterogeneous Causal Effects. CLeaR 2022: 618-633 - [c86]Chun-Hao Chang, Rich Caruana, Anna Goldenberg:
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning. ICLR 2022 - [c85]Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana:
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values. KDD 2022: 4132-4142 - [c84]Rich Caruana, Harsha Nori:
Why Data Scientists Prefer Glassbox Machine Learning: Algorithms, Differential Privacy, Editing and Bias Mitigation. KDD 2022: 4776-4777 - [i29]Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan:
Differentially Private Estimation of Heterogeneous Causal Effects. CoRR abs/2202.11043 (2022) - [i28]Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana:
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values. CoRR abs/2206.15465 (2022) - [i27]Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Kristin Sitcov, Vivienne Souter, Rich Caruana:
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes. CoRR abs/2207.05322 (2022) - [i26]Benjamin J. Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs, Rich Caruana:
Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML. CoRR abs/2211.08991 (2022) - 2021
- [j12]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): 14-27 (2021) - [c83]Rich Caruana, Benjamin J. Lengerich, Yindalon Aphinyanaphongs:
Data-Driven Patterns in Protective Effects of Ibuprofen and Ketorolac on Hospitalized Covid-19 Patients. AMIA 2021 - [c82]Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni:
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. ICML 2021: 8227-8237 - [c81]Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana:
How Interpretable and Trustworthy are GAMs? KDD 2021: 95-105 - [c80]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 - [c79]Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Benjamin J. Lengerich, Rich Caruana, Geoffrey E. Hinton:
Neural Additive Models: Interpretable Machine Learning with Neural Nets. NeurIPS 2021: 4699-4711 - [c78]Zhi Chen, Sarah Tan, Harsha Nori, Kori Inkpen, Yin Lou, Rich Caruana:
Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data. PKDD/ECML Workshops (1) 2021: 534-551 - [i25]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 abs/2102.05107 (2021) - [i24]Chun-Hao Chang, Rich Caruana, Anna Goldenberg:
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning. CoRR abs/2106.01613 (2021) - [i23]Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni:
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. CoRR abs/2106.09680 (2021) - [i22]Chun-Hao Chang, George-Alexandru Adam, Rich Caruana, Anna Goldenberg:
Extracting Clinician's Goals by What-if Interpretable Modeling. CoRR abs/2110.15165 (2021) - [i21]Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana:
GAM Changer: Editing Generalized Additive Models with Interactive Visualization. CoRR abs/2112.03245 (2021) - 2020
- [c77]Benjamin J. 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: 2402-2412 - [c76]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: 1-14 - [c75]Rich Caruana, Scott M. Lundberg, Marco Túlio Ribeiro, Harsha Nori, Samuel Jenkins:
Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges. KDD 2020: 3511-3512 - [i20]Jonathan A. Weyn, Dale R. Durran, Rich Caruana:
Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere. CoRR abs/2003.11927 (2020) - [i19]Rishabh Agarwal, Nicholas Frosst, Xuezhou Zhang, Rich Caruana, Geoffrey E. Hinton:
Neural Additive Models: Interpretable Machine Learning with Neural Nets. CoRR abs/2004.13912 (2020) - [i18]Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana:
How Interpretable and Trustworthy are GAMs? CoRR abs/2006.06466 (2020) - [i17]Benjamin J. Lengerich, Eric P. Xing, Rich Caruana:
On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks. CoRR abs/2007.00823 (2020)
2010 – 2019
- 2019
- [c74]Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec:
Faithful and Customizable Explanations of Black Box Models. AIES 2019: 131-138 - [c73]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: 579 - [c72]Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana:
Axiomatic Interpretability for Multiclass Additive Models. KDD 2019: 226-234 - [c71]Richard Caruana:
Friends Don't Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning. KDD 2019: 3174 - [c70]Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey:
Efficient Forward Architecture Search. NeurIPS 2019: 10122-10131 - [i16]Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey:
Efficient Forward Architecture Search. CoRR abs/1905.13360 (2019) - [i15]Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana:
InterpretML: A Unified Framework for Machine Learning Interpretability. CoRR abs/1909.09223 (2019) - [i14]Benjamin J. 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 abs/1911.04974 (2019) - 2018
- [c69]Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou:
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation. AIES 2018: 303-310 - [c68]Charles Sutton, Timothy Hobson, James Geddes, Rich Caruana:
Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling. KDD 2018: 2279-2288 - [i13]Sarah Tan, Rich Caruana, Giles Hooker, Albert Gordo:
Transparent Model Distillation. CoRR abs/1801.08640 (2018) - [i12]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 abs/1810.09092 (2018) - 2017
- [c67]Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz:
Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration. AAAI 2017: 2124-2132 - [c66]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 - [i11]Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec:
Interpretable & Explorable Approximations of Black Box Models. CoRR abs/1707.01154 (2017) - [i10]Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou:
Detecting Bias in Black-Box Models Using Transparent Model Distillation. CoRR abs/1710.06169 (2017) - 2016
- [c65]Jia-Bin Huang, Rich Caruana, Andrew Farnsworth, Steve Kelling, Narendra Ahuja:
Detecting Migrating Birds at Night. CVPR 2016: 2091-2099 - [c64]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: 718-726 - [c63]Eric T. Nalisnick, Bhaskar Mitra, Nick Craswell, Rich Caruana:
Improving Document Ranking with Dual Word Embeddings. WWW (Companion Volume) 2016: 83-84 - [i9]Bhaskar Mitra, Eric T. Nalisnick, Nick Craswell, Rich Caruana:
A Dual Embedding Space Model for Document Ranking. CoRR abs/1602.01137 (2016) - [i8]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 abs/1603.05691 (2016) - [i7]Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz:
Discovering Blind Spots of Predictive Models: Representations and Policies for Guided Exploration. CoRR abs/1610.09064 (2016) - 2015
- [c62]Paul N. Bennett, Milad Shokouhi, Rich Caruana:
Implicit Preference Labels for Learning Highly Selective Personalized Rankers. ICTIR 2015: 291-300 - [c61]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: 1721-1730 - [i6]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 abs/1511.06433 (2015) - 2014
- [c60]Alnur Ali, Rich Caruana, Ashish Kapoor:
Active Learning with Model Selection. AAAI 2014: 1673-1679 - [c59]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: 1101-1108 - [c58]Todd Kulesza, Saleema Amershi, Rich Caruana, Danyel Fisher, Denis Xavier Charles:
Structured labeling for facilitating concept evolution in machine learning. CHI 2014: 3075-3084 - [c57]Jimmy Ba, Rich Caruana:
Do Deep Nets Really Need to be Deep? NIPS 2014: 2654-2662 - [i5]Yin Lou, Jacob Bien, Rich Caruana, Johannes Gehrke:
Sparse Partially Linear Additive Models. CoRR abs/1407.4729 (2014) - 2013
- [j11]Deepak Agarwal, Rich Caruana, Jian Pei, Ke Wang:
Introduction to the Special Issue ACM SIGKDD 2012. ACM Trans. Knowl. Discov. Data 7(3): 9:1-9:2 (2013) - [c56]Rich Caruana:
Clustering: probably approximately useless? CIKM 2013: 1259-1260 - [c55]Yin Lou, Rich Caruana, Johannes Gehrke, Giles Hooker:
Accurate intelligible models with pairwise interactions. KDD 2013: 623-631 - [c54]Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana:
Using multiple samples to learn mixture models. NIPS 2013: 324-332 - [c53]John Krumm, Rich Caruana, Scott Counts:
Learning Likely Locations. UMAP 2013: 64-76 - [p4]Sara Javanmardi, David W. McDonald, Rich Caruana, Sholeh Forouzan, Cristina V. Lopes:
Learning to Detect Vandalism in Social Content Systems: A Study on Wikipedia - Vandalism Detection in Wikipedia. Mining Social Networks and Security Informatics 2013: 203-225 - [i4]Richard A. Caruana:
The Automatic Training of Rule Bases that Use Numerical Uncertainty Representations. CoRR abs/1304.2733 (2013) - [i3]Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana:
Using Multiple Samples to Learn Mixture Models. CoRR abs/1311.7184 (2013) - [i2]Lei Jimmy Ba, Rich Caruana:
Do Deep Nets Really Need to be Deep? CoRR abs/1312.6184 (2013) - 2012
- [j10]Joydeep Ghosh, Padhraic Smyth, Andrew Tomkins, Rich Caruana:
Special issue on best of SIGKDD 2011. ACM Trans. Knowl. Discov. Data 6(4): 14:1-14:2 (2012) - [c52]Oriol Vinyals, Dan Bohus, Rich Caruana:
Learning speaker, addressee and overlap detection models from multimodal streams. ICMI 2012: 417-424 - [c51]Yin Lou, Rich Caruana, Johannes Gehrke:
Intelligible models for classification and regression. KDD 2012: 150-158 - [c50]Alexandru Niculescu-Mizil, Rich Caruana:
Inductive Transfer for Bayesian Network Structure Learning. ICML Unsupervised and Transfer Learning 2012: 167-180 - [p3]Rich Caruana:
A Dozen Tricks with Multitask Learning. Neural Networks: Tricks of the Trade (2nd ed.) 2012: 163-189 - [i1]Alexandru Niculescu-Mizil, Rich Caruana:
Obtaining Calibrated Probabilities from Boosting. CoRR abs/1207.1403 (2012) - 2011
- [c49]Yasser Ganjisaffar, Rich Caruana, Cristina Videira Lopes:
Bagging gradient-boosted trees for high precision, low variance ranking models. SIGIR 2011: 85-94
2000 – 2009
- 2009
- [c48]Daria Sorokina, Rich Caruana, Mirek Riedewald, Wesley M. Hochachka, Steve Kelling:
Detecting and Interpreting Variable Interactions in Observational Ornithology Data. ICDM Workshops 2009: 64-69 - [c47]M. Arthur Munson, Rich Caruana:
On Feature Selection, Bias-Variance, and Bagging. ECML/PKDD (2) 2009: 144-159 - 2008
- [j9]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): 1:1-1:34 (2008) - [c46]Rich Caruana, Nikolaos Karampatziakis, Ainur Yessenalina:
An empirical evaluation of supervised learning in high dimensions. ICML 2008: 96-103 - [c45]Daria Sorokina, Rich Caruana, Mirek Riedewald, Daniel Fink:
Detecting statistical interactions with additive groves of trees. ICML 2008: 1000-1007 - [c44]Engin Ipek, Onur Mutlu, José F. Martínez, Rich Caruana:
Self-Optimizing Memory Controllers: A Reinforcement Learning Approach. ISCA 2008: 39-50 - [c43]Nam Nguyen, Rich Caruana:
Classification with partial labels. KDD 2008: 551-559 - [c42]Nam Nguyen, Rich Caruana:
Improving Classification with Pairwise Constraints: A Margin-Based Approach. ECML/PKDD (2) 2008: 113-124 - 2007
- [j8]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): 2219-2235 (2007) - [c41]David B. Skalak, Alexandru Niculescu-Mizil, Rich Caruana:
Classifier Loss Under Metric Uncertainty. ECML 2007: 310-322 - [c40]Daria Sorokina, Rich Caruana, Mirek Riedewald:
Additive Groves of Regression Trees. ECML 2007: 323-334 - [c39]Nam Nguyen, Rich Caruana:
Consensus Clusterings. ICDM 2007: 607-612 - [c38]Alexandru Niculescu-Mizil, Rich Caruana:
Inductive Transfer for Bayesian Network Structure Learning. AISTATS 2007: 339-346 - [e1]Pavel Berkhin, Rich Caruana, Xindong Wu:
Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, California, USA, August 12-15, 2007. ACM 2007, ISBN 978-1-59593-609-7 [contents] - 2006
- [c37]Engin Ipek, Sally A. McKee, Rich Caruana, Bronis R. de Supinski, Martin Schulz:
Efficiently exploring architectural design spaces via predictive modeling. ASPLOS 2006: 195-206 - [c36]Rich Caruana, Mohamed Farid Elhawary, Nam Nguyen, Casey Smith:
Meta Clustering. ICDM 2006: 107-118 - [c35]Rich Caruana, Art Munson, Alexandru Niculescu-Mizil:
Getting the Most Out of Ensemble Selection. ICDM 2006: 828-833 - [c34]Rich Caruana, Alexandru Niculescu-Mizil:
An empirical comparison of supervised learning algorithms. ICML 2006: 161-168 - [c33]Lars Backstrom, Rich Caruana:
C2FS: An Algorithm for Feature Selection in Cascade Neural Networks. IJCNN 2006: 4748-4753 - [c32]Cristian Bucila, Rich Caruana, Alexandru Niculescu-Mizil:
Model compression. KDD 2006: 535-541 - [c31]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: 909-915 - 2005
- [j7]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): 347-366 (2005) - [c30]Alexandru Niculescu-Mizil, Rich Caruana:
Predicting good probabilities with supervised learning. ICML 2005: 625-632 - [c29]Art Munson, Claire Cardie, Rich Caruana:
Optimizing to Arbitrary NLP Metrics using Ensemble Selection. HLT/EMNLP 2005: 539-546 - [c28]Alexandru Niculescu-Mizil, Rich Caruana:
Obtaining Calibrated Probabilities from Boosting. UAI 2005: 413- - 2004
- [j6]Rich Caruana, Thorsten Joachims, Lars Backstrom:
KDD-Cup 2004: results and analysis. SIGKDD Explor. 6(2): 95-108 (2004) - [c27]Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, Alex Ksikes:
Ensemble selection from libraries of models. ICML 2004 - [c26]Rich Caruana, Alexandru Niculescu-Mizil:
Data mining in metric space: an empirical analysis of supervised learning performance criteria. KDD 2004: 69-78 - [c25]Rich Caruana, Alexandru Niculescu-Mizil:
An Empirical Evaluation of Supervised Learning for ROC Area. ROCAI 2004: 1-8 - [c24]Rich Caruana, Alexandru Niculescu-Mizil:
Data Mining in Metric Space: An Empirical Analysis of Supervised Learning Performance Criteria. ROCAI 2004: 9-18 - 2003
- [j5]Rich Caruana, Virginia R. de Sa:
Benefitting from the Variables that Variable Selection Discards. J. Mach. Learn. Res. 3: 1245-1264 (2003) - [c23]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 - 2002
- [c22]Rich Caruana, Radu Stefan Niculescu, R. Bharat Rao, Cynthia Simms:
Machine learning for sub-population assessment: evaluating the C-section rate of different physician practices. AMIA 2002 - 2001
- [c21]Rich Caruana:
A Non-Parametric EM-Style Algorithm for Imputing Missing Values. AISTATS 2001: 35-40 - [c20]John Langford, Rich Caruana:
(Not) Bounding the True Error. NIPS 2001: 809-816 - 2000
- [c19]Rich Caruana:
Case-Based Explanation for Artificial Neural Nets. ANNIMAB 2000: 303-308 - [c18]Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum:
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness. ICML 2000: 703-710 - [c17]Rich Caruana, Steve Lawrence, C. Lee Giles:
Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping. NIPS 2000: 402-408 - [c16]Adam L. Berger, Rich Caruana, David Cohn, Dayne Freitag, Vibhu O. Mittal:
Bridging the lexical chasm: statistical approaches to answer-finding. SIGIR 2000: 192-199
1990 – 1999
- 1999
- [c15]Rich Caruana, Hooshang Kangarloo, John D. N. Dionisio, Usha Sinha, David B. Johnson:
Case-based explanation of non-case-based learning methods. AMIA 1999 - 1998
- [c14]Rich Caruana, Joseph O'Sullivan:
Multitask pattern recognition for autonomous robots. IROS 1998: 13-18 - [p2]Rich Caruana:
Multitask Learning. Learning to Learn 1998: 95-133 - 1997
- [j4]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:
An evaluation of machine-learning methods for predicting pneumonia mortality. Artif. Intell. Medicine 9(2): 107-138 (1997) - [j3]Rich Caruana:
Multitask Learning. Mach. Learn. 28(1): 41-75 (1997) - 1996
- [c13]Rich Caruana:
Algorithms and Applications for Multitask Learning. ICML 1996: 87-95 - [c12]Rich Caruana, Virginia R. de Sa:
Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs. NIPS 1996: 389-395 - [p1]Rich Caruana:
A Dozen Tricks with Multitask Learning. Neural Networks: Tricks of the Trade 1996: 165-191 - 1995
- [c11]Shumeet Baluja, Rich Caruana:
Removing the Genetics from the Standard Genetic Algorithm. ICML 1995: 38-46 - [c10]Rich Caruana, Shumeet Baluja, Tom M. Mitchell:
Using the Future to Sort Out the Present: Rankprop and Multitask Learning for Medical Risk Evaluation. NIPS 1995: 959-965 - 1994
- [j2]Tom M. Mitchell, Rich Caruana, Dayne Freitag, John P. McDermott, David Zabowski:
Experience with a Learning Personal Assistant. Commun. ACM 37(7): 80-91 (1994) - [c9]Rich Caruana, Dayne Freitag:
Greedy Attribute Selection. ICML 1994: 28-36 - [c8]Rich Caruana:
Learning Many Related Tasks at the Same Time with Backpropagation. NIPS 1994: 657-664 - 1993
- [c7]Rich Caruana:
Multitask Learning: A Knowledge-Based Source of Inductive Bias. ICML 1993: 41-48
1980 – 1989
- 1989
- [c6]Larry J. Eshelman, Rich Caruana, J. David Schaffer:
Biases in the Crossover Landscape. ICGA 1989: 10-19 - [c5]J. David Schaffer, Rich Caruana, Larry J. Eshelman, Rajarshi Das:
A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization. ICGA 1989: 51-60 - [c4]Rich Caruana, J. David Schaffer, Larry J. Eshelman:
Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms. ML 1989: 375-378 - [c3]Rich Caruana, Larry J. Eshelman, J. David Schaffer:
Representation and Hidden Bias II: Eliminating Defining Length Bias in Genetic Search via Shuffle Crossover. IJCAI 1989: 750-755 - 1988
- [j1]Rich Caruana:
The automatic training of rule bases that use numerical uncertainty representations. Int. J. Approx. Reason. 2(3): 330-331 (1988) - [c2]Rich Caruana, J. David Schaffer:
Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms. ML 1988: 153-161 - 1987
- [c1]Rich Caruana:
The Automatic Training of Rule Bases that Use Numerical Uncertainty Representations. UAI 1987: 347-356
Coauthor Index
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