Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms. - Rich Caruana; J. David Schaffer (ML, 1988)

The automatic training of rule bases that use numerical uncertainty representations. - Rich Caruana (Int. J. Approx. Reason., 1988)

Biases in the Crossover Landscape. - Larry J. Eshelman; Rich Caruana; J. David Schaffer (ICGA, 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 (ICGA, 1989)

Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms. - Rich Caruana; J. David Schaffer; Larry J. Eshelman (ML, 1989)

Representation and Hidden Bias II: Eliminating Defining Length Bias in Genetic Search via Shuffle Crossover. - Rich Caruana; Larry J. Eshelman; J. David Schaffer (IJCAI, 1989)

Multitask Learning: A Knowledge-Based Source of Inductive Bias. - Rich Caruana (ICML, 1993)

Greedy Attribute Selection. - Rich Caruana; Dayne Freitag (ICML, 1994)

Learning Many Related Tasks at the Same Time with Backpropagation. - Rich Caruana (NIPS, 1994)

Experience with a Learning Personal Assistant. - Tom M. Mitchell; Rich Caruana; Dayne Freitag; John P. McDermott; David Zabowski (Commun. ACM, 1994)

Removing the Genetics from the Standard Genetic Algorithm. - Shumeet Baluja; Rich Caruana (ICML, 1995)

Using the Future to Sort Out the Present: Rankprop and Multitask Learning for Medical Risk Evaluation. - Rich Caruana; Shumeet Baluja; Tom M. Mitchell (NIPS, 1995)

Algorithms and Applications for Multitask Learning. - Rich Caruana (ICML, 1996)

A Dozen Tricks with Multitask Learning. - Rich Caruana (Neural Networks: Tricks of the Trade, 1996)

Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs. - Rich Caruana; Virginia R. de Sa (NIPS, 1996)

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; et al. (Artif. Intell. Medicine, 1997)

Multitask Learning. - Rich Caruana (Mach. Learn., 1997)

Multitask Learning. - Rich Caruana (Learning to Learn, 1998)

Multitask pattern recognition for autonomous robots. - Rich Caruana; Joseph O'Sullivan (IROS, 1998)

Case-based explanation of non-case-based learning methods. - Rich Caruana; Hooshang Kangarloo; John D. N. Dionisio; Usha Sinha; David B. Johnson 003 (AMIA, 1999)

Case-Based Explanation for Artificial Neural Nets. - Rich Caruana (ANNIMAB, 2000)

FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness. - Joseph O'Sullivan; John Langford 001; Rich Caruana; Avrim Blum (ICML, 2000)

Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping. - Rich Caruana; Steve Lawrence; C. Lee Giles (NIPS, 2000)

Bridging the lexical chasm: statistical approaches to answer-finding. - Adam L. Berger; Rich Caruana; David Cohn; Dayne Freitag; Vibhu O. Mittal (SIGIR, 2000)

A Non-Parametric EM-Style Algorithm for Imputing Missing Values. - Rich Caruana (AISTATS, 2001)

(Not) Bounding the True Error. - John Langford 001; Rich Caruana (NIPS, 2001)

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 (AMIA, 2002)

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 (AMIA, 2003)

Benefitting from the Variables that Variable Selection Discards. - Rich Caruana; Virginia R. de Sa (J. Mach. Learn. Res., 2003)

Ensemble selection from libraries of models. - Rich Caruana; Alexandru Niculescu-Mizil; Geoff Crew; Alex Ksikes (ICML, 2004)

Data mining in metric space: an empirical analysis of supervised learning performance criteria. - Rich Caruana; Alexandru Niculescu-Mizil (KDD, 2004)

An Empirical Evaluation of Supervised Learning for ROC Area. - Rich Caruana; Alexandru Niculescu-Mizil (ROCAI, 2004)

KDD-Cup 2004: results and analysis. - Rich Caruana; Thorsten Joachims; Lars Backstrom (SIGKDD Explor., 2004)

Predicting good probabilities with supervised learning. - Alexandru Niculescu-Mizil; Rich Caruana (ICML, 2005)

Optimizing to Arbitrary NLP Metrics using Ensemble Selection. - Art Munson; Claire Cardie; Rich Caruana (HLT/EMNLP, 2005)

Obtaining Calibrated Probabilities from Boosting. - Alexandru Niculescu-Mizil; Rich Caruana (UAI, 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)

Efficiently exploring architectural design spaces via predictive modeling. - Engin Ipek; Sally A. McKee; Rich Caruana; Bronis R. de Supinski; Martin Schulz 001 (ASPLOS, 2006)

Meta Clustering. - Rich Caruana; Mohamed Farid Elhawary; Nam Nguyen 001; Casey Smith (ICDM, 2006)

Getting the Most Out of Ensemble Selection. - Rich Caruana; Art Munson; Alexandru Niculescu-Mizil (ICDM, 2006)

An empirical comparison of supervised learning algorithms. - Rich Caruana; Alexandru Niculescu-Mizil (ICML, 2006)

C2FS: An Algorithm for Feature Selection in Cascade Neural Networks. - Lars Backstrom; Rich Caruana (IJCNN, 2006)

Model compression. - Cristian Bucila; Rich Caruana; Alexandru Niculescu-Mizil (KDD, 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 002; Wesley M. Hochachka; Steve Kelling (KDD, 2006)

Classifier Loss Under Metric Uncertainty. - David B. Skalak; Alexandru Niculescu-Mizil; Rich Caruana (ECML, 2007)

Additive Groves of Regression Trees. - Daria Sorokina; Rich Caruana; Mirek Riedewald (ECML, 2007)

Consensus Clusterings. - Nam Nguyen 001; Rich Caruana (ICDM, 2007)

Predicting parallel application performance via machine learning approaches. - Karan Singh; Engin Ipek; Sally A. McKee; Bronis R. de Supinski; Martin Schulz 001; Rich Caruana (Concurr. Comput. Pract. Exp., 2007)

An empirical evaluation of supervised learning in high dimensions. - Rich Caruana; Nikolaos Karampatziakis; Ainur Yessenalina (ICML, 2008)

Detecting statistical interactions with additive groves of trees. - Daria Sorokina; Rich Caruana; Mirek Riedewald; Daniel Fink 002 (ICML, 2008)

Self-Optimizing Memory Controllers: A Reinforcement Learning Approach. - Engin Ipek; Onur Mutlu; José F. Martínez; Rich Caruana (ISCA, 2008)

Classification with partial labels. - Nam Nguyen 001; Rich Caruana (KDD, 2008)

Improving Classification with Pairwise Constraints: A Margin-Based Approach. - Nam Nguyen 001; Rich Caruana (ECML/PKDD (2), 2008)

Efficient architectural design space exploration via predictive modeling. - Engin Ipek; Sally A. McKee; Karan Singh; Rich Caruana; Bronis R. de Supinski; Martin Schulz 001 (ACM Trans. Archit. Code Optim., 2008)

Detecting and Interpreting Variable Interactions in Observational Ornithology Data. - Daria Sorokina; Rich Caruana; Mirek Riedewald; Wesley M. Hochachka; Steve Kelling (ICDM Workshops, 2009)

On Feature Selection, Bias-Variance, and Bagging. - M. Arthur Munson; Rich Caruana (ECML/PKDD (2), 2009)

Bagging gradient-boosted trees for high precision, low variance ranking models. - Yasser Ganjisaffar; Rich Caruana; Cristina Videira Lopes (SIGIR, 2011)

Learning speaker, addressee and overlap detection models from multimodal streams. - Oriol Vinyals; Dan Bohus; Rich Caruana (ICMI, 2012)

Intelligible models for classification and regression. - Yin Lou; Rich Caruana; Johannes Gehrke (KDD, 2012)

Inductive Transfer for Bayesian Network Structure Learning. - Alexandru Niculescu-Mizil; Rich Caruana (ICML Unsupervised and Transfer Learning, 2012)

Special issue on best of SIGKDD 2011. - Joydeep Ghosh; Padhraic Smyth; Andrew Tomkins; Rich Caruana (ACM Trans. Knowl. Discov. Data, 2012)

A Dozen Tricks with Multitask Learning. - Rich Caruana (Neural Networks: Tricks of the Trade (2nd ed.), 2012)

Clustering: probably approximately useless? - Rich Caruana (CIKM, 2013)

Accurate intelligible models with pairwise interactions. - Yin Lou; Rich Caruana; Johannes Gehrke; Giles Hooker (KDD, 2013)

Using multiple samples to learn mixture models. - Jason D. Lee; Ran Gilad-Bachrach; Rich Caruana (NIPS, 2013)

Learning Likely Locations. - John Krumm; Rich Caruana; Scott Counts (UMAP, 2013)

Introduction to the Special Issue ACM SIGKDD 2012. - Deepak Agarwal; Rich Caruana; Jian Pei 001; Ke Wang 001 (ACM Trans. Knowl. Discov. Data, 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 (Mining Social Networks and Security Informatics, 2013)

Active Learning with Model Selection. - Alnur Ali; Rich Caruana; Ashish Kapoor (AAAI, 2014)

Gauss meets Canadian traveler: shortest-path problems with correlated natural dynamics. - Debadeepta Dey; Andrey Kolobov; Rich Caruana; Ece Kamar; Eric Horvitz; Ashish Kapoor (AAMAS, 2014)

Structured labeling for facilitating concept evolution in machine learning. - Todd Kulesza; Saleema Amershi; Rich Caruana; Danyel Fisher; Denis Xavier Charles (CHI, 2014)

Do Deep Nets Really Need to be Deep? - Jimmy Ba; Rich Caruana (NIPS, 2014)

Sparse Partially Linear Additive Models. - Yin Lou; Jacob Bien; Rich Caruana; Johannes Gehrke (CoRR, 2014)

Implicit Preference Labels for Learning Highly Selective Personalized Rankers. - Paul N. Bennett; Milad Shokouhi; Rich Caruana (ICTIR, 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 (KDD, 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)

Detecting Migrating Birds at Night. - Jia-Bin Huang 001; Rich Caruana; Andrew Farnsworth; Steve Kelling; Narendra Ahuja (CVPR, 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 (ICML, 2016)

Improving Document Ranking with Dual Word Embeddings. - Eric T. Nalisnick; Bhaskar Mitra 001; Nick Craswell; Rich Caruana (WWW (Companion Volume), 2016)

Discovering Blind Spots of Predictive Models: Representations and Policies for Guided Exploration. - Himabindu Lakkaraju; Ece Kamar; Rich Caruana; Eric Horvitz (CoRR, 2016)

A Dual Embedding Space Model for Document Ranking. - Bhaskar Mitra 001; Eric T. Nalisnick; Nick Craswell; Rich Caruana (CoRR, 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)

Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration. - Himabindu Lakkaraju; Ece Kamar; Rich Caruana; Eric Horvitz (AAAI, 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 (ICLR (Poster), 2017)

Interpretable & Explorable Approximations of Black Box Models. - Himabindu Lakkaraju; Ece Kamar; Rich Caruana; Jure Leskovec (CoRR, 2017)

Detecting Bias in Black-Box Models Using Transparent Model Distillation. - Sarah Tan; Rich Caruana; Giles Hooker; Yin Lou (CoRR, 2017)

Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation. - Sarah Tan; Rich Caruana; Giles Hooker; Yin Lou (AIES, 2018)

Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling. - Charles Sutton; Timothy Hobson; James Geddes; Rich Caruana (KDD, 2018)

Transparent Model Distillation. - Sarah Tan; Rich Caruana; Giles Hooker; Albert Gordo (CoRR, 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)

Faithful and Customizable Explanations of Black Box Models. - Himabindu Lakkaraju; Ece Kamar; Rich Caruana; Jure Leskovec (AIES, 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 (CHI, 2019)

Friends Don't Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning. - Richard Caruana (KDD, 2019)

Axiomatic Interpretability for Multiclass Additive Models. - Xuezhou Zhang; Sarah Tan; Paul Koch; Yin Lou; Urszula Chajewska; Rich Caruana (KDD, 2019)

Efficient Forward Architecture Search. - Hanzhang Hu; John Langford 001; Rich Caruana; Saurajit Mukherjee; Eric Horvitz; Debadeepta Dey (NeurIPS, 2019)

InterpretML: A Unified Framework for Machine Learning Interpretability. - Harsha Nori; Samuel Jenkins; Paul Koch; Rich Caruana (CoRR, 2019)

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 (AISTATS, 2020)

Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges. - Rich Caruana; Scott M. Lundberg; Marco Túlio Ribeiro; Harsha Nori; Samuel Jenkins (KDD, 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)

Neural Additive Models: Interpretable Machine Learning with Neural Nets. - Rishabh Agarwal; Nicholas Frosst; Xuezhou Zhang; Rich Caruana; Geoffrey E. Hinton (CoRR, 2020)

On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks. - Benjamin J. Lengerich; Eric P. Xing; Rich Caruana (CoRR, 2020)

Data-Driven Patterns in Protective Effects of Ibuprofen and Ketorolac on Hospitalized Covid-19 Patients. - Rich Caruana; Benjamin J. Lengerich; Yindalon Aphinyanaphongs (AMIA, 2021)

Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. - Harsha Nori; Rich Caruana; Zhiqi Bu; Judy Hanwen Shen; Janardhan Kulkarni (ICML, 2021)

How Interpretable and Trustworthy are GAMs? - Chun-Hao Chang; Sarah Tan; Benjamin J. Lengerich; Anna Goldenberg; Rich Caruana (KDD, 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 (DaSH@KDD, 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 (NeurIPS, 2021)

Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data. - Zhi Chen 009; Sarah Tan; Harsha Nori; Kori Inkpen; Yin Lou; Rich Caruana (PKDD/ECML Workshops (1), 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)

Extracting Clinician's Goals by What-if Interpretable Modeling. - Chun-Hao Chang; George-Alexandru Adam; Rich Caruana; Anna Goldenberg (CoRR, 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)

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)

Dropout as a Regularizer of Interaction Effects. - Benjamin J. Lengerich; Eric P. Xing; Rich Caruana (AISTATS, 2022)

Differentially Private Estimation of Heterogeneous Causal Effects. - Fengshi Niu; Harsha Nori; Brian Quistorff; Rich Caruana; Donald Ngwe; Aadharsh Kannan (CLeaR, 2022)

NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning. - Chun-Hao Chang; Rich Caruana; Anna Goldenberg (ICLR, 2022)

Why Data Scientists Prefer Glassbox Machine Learning: Algorithms, Differential Privacy, Editing and Bias Mitigation. - Rich Caruana; Harsha Nori (KDD, 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 (KDD, 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)

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)

Interpretable machine learning for predicting pathologic complete response in patients treated with chemoradiation therapy for rectal adenocarcinoma. - Du Wang; Sang Ho Lee 001; Huaizhi Geng; Haoyu Zhong; John Plastaras; Andrzej Wojcieszynski; Richard Caruana; Ying Xiao (Frontiers Artif. Intell., 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)

GAM Coach: Towards Interactive and User-centered Algorithmic Recourse. - Zijie J. Wang; Jennifer Wortman Vaughan; Rich Caruana; Duen Horng Chau (CHI, 2023)

Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? - Zhi Chen 009; Sarah Tan; Urszula Chajewska; Cynthia Rudin; Rich Caruana (CHIL, 2023)

Extending Explainable Boosting Machines to Scientific Image Data. - Daniel Schug; Sai S. Yerramreddy; Rich Caruana; Craig Greenberg; Justyna P. Zwolak (CoRR, 2023)

Diagnosis Uncertain Models For Medical Risk Prediction. - Alexander Peysakhovich; Rich Caruana; Yin Aphinyanaphongs (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)

Explaining high-dimensional text classifiers. - Odelia Melamed; Rich Caruana (CoRR, 2023)

Considerations when learning additive explanations for black-box models. - Sarah Tan; Giles Hooker; Paul Koch; Albert Gordo; Rich Caruana (Mach. Learn., 2023)

Rethinking Interpretability in the Era of Large Language Models. - Chandan Singh; Jeevana Priya Inala; Michel Galley; Rich Caruana; Jianfeng Gao 001 (CoRR, 2024)

Data Science with LLMs and Interpretable Models. - Sebastian Bordt; Benjamin J. Lengerich; Harsha Nori; Rich Caruana (CoRR, 2024)

Elephants Never Forget: Testing Language Models for Memorization of Tabular Data. - Sebastian Bordt; Harsha Nori; Rich Caruana (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)

GAMformer: In-Context Learning for Generalized Additive Models. - Andreas Mueller; Julien Siems; Harsha Nori; David Salinas; Arber Zela; Rich Caruana; Frank Hutter (CoRR, 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., 2024)

The Most Important Features in Generalized Additive Models Might Be Groups of Features. - 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 (CoRR, 2025)