| 2023 | CHI | GAM Coach: Towards Interactive and User-centered Algorithmic Recourse. | Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau |
| 2022 | AISTATS | Dropout as a Regularizer of Interaction Effects. | Benjamin J. Lengerich, Eric P. Xing, Rich Caruana |
| 2022 | ICLR | NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning. | Chun-Hao Chang, Rich Caruana, Anna Goldenberg |
| 2022 | KDD | Why Data Scientists Prefer Glassbox Machine Learning: Algorithms, Differential Privacy, Editing and Bias Mitigation. | Rich Caruana, Harsha Nori |
| 2022 | KDD | 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 |
| 2021 | AMIA | Data-Driven Patterns in Protective Effects of Ibuprofen and Ketorolac on Hospitalized Covid-19 Patients. | Rich Caruana, Benjamin J. Lengerich, Yindalon Aphinyanaphongs |
| 2021 | ICML | Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. | Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni |
| 2021 | KDD | How Interpretable and Trustworthy are GAMs? | Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana |
| 2021 | KDD | 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 |
| 2020 | AISTATS | 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 |
| 2020 | CHI | 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 |
| 2020 | KDD | Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges. | Rich Caruana, Scott M. Lundberg, Marco Tlio Ribeiro, Harsha Nori, Samuel Jenkins |
| 2019 | AIES | Faithful and Customizable Explanations of Black Box Models. | Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec |
| 2019 | CHI | Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models. | Fred Hohman, Andrew Head, Rich Caruana, Robert DeLine, Steven Mark Drucker |
| 2019 | KDD | Axiomatic Interpretability for Multiclass Additive Models. | Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana |
| 2018 | AIES | Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation. | Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou |
| 2018 | KDD | Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling. | Charles Sutton, Timothy Hobson, James Geddes, Rich Caruana |
| 2017 | AAAI | Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration. | Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz |
| 2017 | ICLR | Do Deep Convolutional Nets Really Need to be Deep and Convolutional? | Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou, zlem Aslan, Shengjie Wang, Abdelrahman Mohamed, Matthai Philipose, Matthew Richardson, Rich Caruana |
| 2016 | CVPR | Detecting Migrating Birds at Night. | Jia-Bin Huang, Rich Caruana, Andrew Farnsworth, Steve Kelling, Narendra Ahuja |
| 2016 | ICML | 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, zlem Aslan |
| 2016 | WWW | Improving Document Ranking with Dual Word Embeddings. | Eric T. Nalisnick, Bhaskar Mitra, Nick Craswell, Rich Caruana |
| 2015 | ICTIR | Implicit Preference Labels for Learning Highly Selective Personalized Rankers. | Paul N. Bennett, Milad Shokouhi, Rich Caruana |
| 2015 | KDD | Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission. | Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, Noemie Elhadad |
| 2014 | AAAI | Active Learning with Model Selection. | Alnur Ali, Rich Caruana, Ashish Kapoor |
| 2014 | CHI | Structured labeling for facilitating concept evolution in machine learning. | Todd Kulesza, Saleema Amershi, Rich Caruana, Danyel Fisher, Denis Xavier Charles |
| 2013 | CIKM | Clustering: probably approximately useless? | Rich Caruana |
| 2013 | KDD | Accurate intelligible models with pairwise interactions. | Yin Lou, Rich Caruana, Johannes Gehrke, Giles Hooker |
| 2012 | ICMI | Learning speaker, addressee and overlap detection models from multimodal streams. | Oriol Vinyals, Dan Bohus, Rich Caruana |
| 2012 | KDD | Intelligible models for classification and regression. | Yin Lou, Rich Caruana, Johannes Gehrke |
| 2011 | SIGIR | Bagging gradient-boosted trees for high precision, low variance ranking models. | Yasser Ganjisaffar, Rich Caruana, Cristina Videira Lopes |
| 2009 | ICDM | Detecting and Interpreting Variable Interactions in Observational Ornithology Data. | Daria Sorokina, Rich Caruana, Mirek Riedewald, Wesley M. Hochachka, Steve Kelling |
| 2008 | ICML | An empirical evaluation of supervised learning in high dimensions. | Rich Caruana, Nikolaos Karampatziakis, Ainur Yessenalina |
| 2008 | ICML | Detecting statistical interactions with additive groves of trees. | Daria Sorokina, Rich Caruana, Mirek Riedewald, Daniel Fink |
| 2008 | ISCA | Self-Optimizing Memory Controllers: A Reinforcement Learning Approach. | Engin Ipek, Onur Mutlu, Jos F. Martnez, Rich Caruana |
| 2008 | KDD | Classification with partial labels. | Nam Nguyen, Rich Caruana |
| 2007 | ICDM | Consensus Clusterings. | Nam Nguyen, Rich Caruana |
| 2006 | ASPLOS | Efficiently exploring architectural design spaces via predictive modeling. | Engin Ipek, Sally A. McKee, Rich Caruana, Bronis R. de Supinski, Martin Schulz |
| 2006 | ICDM | Meta Clustering. | Rich Caruana, Mohamed Farid Elhawary, Nam Nguyen, Casey Smith |
| 2006 | ICDM | Getting the Most Out of Ensemble Selection. | Rich Caruana, Art Munson, Alexandru Niculescu-Mizil |
| 2006 | ICML | An empirical comparison of supervised learning algorithms. | Rich Caruana, Alexandru Niculescu-Mizil |
| 2006 | IJCNN | C2FS: An Algorithm for Feature Selection in Cascade Neural Networks. | Lars Backstrom, Rich Caruana |
| 2006 | KDD | Model compression. | Cristian Bucila, Rich Caruana, Alexandru Niculescu-Mizil |
| 2006 | KDD | 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 |
| 2005 | ICML | Predicting good probabilities with supervised learning. | Alexandru Niculescu-Mizil, Rich Caruana |
| 2005 | NAACL | Optimizing to Arbitrary NLP Metrics using Ensemble Selection. | Art Munson, Claire Cardie, Rich Caruana |
| 2005 | UAI | Obtaining Calibrated Probabilities from Boosting. | Alexandru Niculescu-Mizil, Rich Caruana |
| 2004 | ICML | Ensemble selection from libraries of models. | Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, Alex Ksikes |
| 2004 | KDD | Data mining in metric space: an empirical analysis of supervised learning performance criteria. | Rich Caruana, Alexandru Niculescu-Mizil |
| 2003 | AMIA | 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 |
| 2002 | AMIA | 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 |
| 2001 | AISTATS | A Non-Parametric EM-Style Algorithm for Imputing Missing Values. | Rich Caruana |
| 2000 | ICML | FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness. | Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum |
| 2000 | SIGIR | Bridging the lexical chasm: statistical approaches to answer-finding. | Adam L. Berger, Rich Caruana, David Cohn, Dayne Freitag, Vibhu O. Mittal |
| 1999 | AMIA | Case-based explanation of non-case-based learning methods. | Rich Caruana, Hooshang Kangarloo, John D. N. Dionisio, Usha Sinha, David B. Johnson |
| 1998 | IROS | Multitask pattern recognition for autonomous robots. | Rich Caruana, Joseph O'Sullivan |
| 1996 | ICML | Algorithms and Applications for Multitask Learning. | Rich Caruana |
| 1995 | ICML | Removing the Genetics from the Standard Genetic Algorithm. | Shumeet Baluja, Rich Caruana |
| 1994 | ICML | Greedy Attribute Selection. | Rich Caruana, Dayne Freitag |
| 1993 | ICML | Multitask Learning: A Knowledge-Based Source of Inductive Bias. | Rich Caruana |
| 1989 | ICML | Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms. | Rich Caruana, J. David Schaffer, Larry J. Eshelman |
| 1989 | IJCAI | Representation and Hidden Bias II: Eliminating Defining Length Bias in Genetic Search via Shuffle Crossover. | Rich Caruana, Larry J. Eshelman, J. David Schaffer |
| 1988 | ICML | Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms. | Rich Caruana, J. David Schaffer |
| 1987 | UAI | The Automatic Training of Rule Bases that Use Numerical Uncertainty Representations. | Rich Caruana |