R. Caruana

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Learning Many Related Tasks at the Same Time with Backpropagation

Rich Caruana, R. Caruana, 1994, NIPS.

Analysis of Deep Neural Networks with Extended Data Jacobian Matrix

Jeff A. Bilmes, Matthew Richardson, Rich Caruana, 2016, ICML.

Introduction in IND and recursive partitioning

Wray L. Buntine, R. Caruana, 1991 .

Learning From Imbalanced Data: Rank Metrics and Extra Tasks

R. Caruana, 2000 .

Multitask Learning

Rich Caruana, R. Caruana, 1997, Machine-mediated learning.

Multitask Learning

Rich Caruana, R. Caruana, 1998, Encyclopedia of Machine Learning and Data Mining.

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

Rich Caruana, R. Caruana, 2001, AISTATS.

KDD-Cup 2004: results and analysis

Thorsten Joachims, Rich Caruana, Lars Backstrom, 2004, SKDD.

Multitask Learning

Rich Caruana, R. Caruana, 1997, Machine Learning.

Experience with a learning personal assistant

Tom M. Mitchell, Rich Caruana, Dayne Freitag, 1994, CACM.

Greedy Attribute Selection

Rich Caruana, Dayne Freitag, Dayne Freitag, 1994, ICML.

Multitask Learning: A Knowledge-Based Source of Inductive Bias

Rich Caruana, R. Caruana, 1993, ICML.

Statistical Machine Learning for Large-Scale Optimization

Kee-Eung Kim, Thomas G. Dietterich, Russell Impagliazzo, 2000 .

Do Deep Nets Really Need to be Deep?

Rich Caruana, Jimmy Ba, Jimmy Ba, 2013, NIPS.

(Not) Bounding the True Error

John Langford, Rich Caruana, J. Langford, 2001, NIPS.

Semi-Supervised Clustering with User Feedback

Andrew McCallum, Rich Caruana, David Cohn, 2003 .

Ensemble selection from libraries of models

Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, 2004, ICML.

FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness

J. Langford, A. Blum, R. Caruana, 2000, ICML.

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

Rajarshi Das, Larry J. Eshelman, J. David Schaffer, 1989, ICGA.

Self-Optimizing Memory Controllers: A Reinforcement Learning Approach

Onur Mutlu, Rich Caruana, Engin Ipek, 2008, 2008 International Symposium on Computer Architecture.

Predicting cesarean delivery with decision tree models.

R. Caruana, C. Sims, L. Meyn, 2000, American journal of obstetrics and gynecology.

Removing the Genetics from the Standard Genetic Algorithm

Rich Caruana, Shumeet Baluja, R. Caruana, 1995, ICML.

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

J. David Schaffer, Rich Caruana, J. D. Schaffer, 1988, ML.

Biases in the Crossover Landscape

Larry J. Eshelman, J. David Schaffer, Rich Caruana, 1989, ICGA.

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

R. Caruana, G. Hooker, S. Tan, 2017 .

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

Larry J. Eshelman, J. David Schaffer, Rich Caruana, 1989, IJCAI.

Using genetic search to exploit the emergent behavior of neural networks

Larry J. Eshelman, J. David Schaffer, J. D. Schaffer, 1990 .

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

Rich Caruana, C. Lee Giles, Steve Lawrence, 2000, NIPS.

Transparent Model Distillation pdf

Albert Gordo, Rich Caruana, Giles Hooker, 2018, ArXiv.

Getting the Most Out of Ensemble Selection

Rich Caruana, Art Munson, Alexandru Niculescu-Mizil, 2006, Sixth International Conference on Data Mining (ICDM'06).

Meta Clustering

Rich Caruana, Nam Nguyen, Casey Smith, 2006, Sixth International Conference on Data Mining (ICDM'06).

Bridging the lexical chasm: statistical approaches to answer-finding

Vibhu O. Mittal, Rich Caruana, Dayne Freitag, 2000, SIGIR '00.

Structured Labeling to Facilitate Concept Evolution in Machine Learning

R. Caruana, Saleema Amershi, Denis Xavier Charles, 2014 .

N EED TO BE D EEP AND C ONVOLUTIONAL ?

S. Kahou, R. Caruana, Shengjie Wang, 2017 .

Neural Additive Models: Interpretable Machine Learning with Neural Nets pdf

Geoffrey E. Hinton, Rich Caruana, Rishabh Agarwal, 2020, NeurIPS.

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

R. Caruana, Charles Sutton, Timothy Hobson, 2018, KDD.

Identifying Temporal Patterns and Key Players in Document Collections

Thorsten Joachims, Johannes Gehrke, Rich Caruana, 1995 .

Statistical Machine Learning for Large-Scale Optimization Contributors

Thomas G. Dietterich, Wray L. Buntine, R. Impagliazzo, 2022 .

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

Benjamin J. Lengerich, R. Caruana, R. Caruana, 2021, Journal of Biomedical Informatics.

Fit Interpretable Machine Learning Models [R package interpret version 0.1.26]

Rich Caruana, Harsha Nori, Paul Koch, 2020 .

A Method for Automatically Finding Interpretations of Reduced Dimension Representations

R. Caruana, Marc Fasnacht, 2004 .

A Survey on Multi Objective Optimization Challenges in Swarm Intelligence

R. Caruana, Yin Lou, 2021, Journal of Computing and Natural Science.

Fast algorithm for the resolution of spectra

Saul I. Shupack, R. Caruana, S. Shupack, 1986 .

Additional capabilities of a fast algorithm for the resolution of spectra

R. Caruana, S. Shupack, Roger B. Searle, 1988 .

Special issue on best of SIGKDD 2011

Andrew Tomkins, Joydeep Ghosh, Padhraic Smyth, 2012, TKDD.

Estimating the Number of Local Minima in Big, Nasty Search Spaces

Rich Caruana, Matthew D. Mullin, M. D. Mullin, 1999 .

Friends Don’t Let Friends Deploy Black-Box Models: The Importance of Transparency and Intelligibility in Machine Learning

Rich Caruana, R. Caruana, 2018 .

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

Zijie J. Wang, R. Caruana, M. Nunnally, 2022, KDD.

Dec . 7 , 2018 Critiquing and Correcting Trends in Machine Learning

Matt J. Kusner, Y. Teh, R. Caruana, 2022 .

Model compression

Rich Caruana, Alexandru Niculescu-Mizil, Cristian Bucila, 2006, KDD '06.

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

Eric Horvitz, Ashish Kapoor, Rich Caruana, 2014, AAMAS.

Predicting dire outcomes of patients with community acquired pneumonia

Constantin F. Aliferis, Tom M. Mitchell, Rich Caruana, 2005, J. Biomed. Informatics.

Sparse Partially Linear Additive Models

Johannes Gehrke, Rich Caruana, Jacob Bien, 2014, ArXiv.

Sub‐Seasonal Forecasting With a Large Ensemble of Deep‐Learning Weather Prediction Models

Jonathan A. Weyn, R. Caruana, D. Durran, 2021, Journal of Advances in Modeling Earth Systems.

Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere pdf

Rich Caruana, Dale R. Durran, Jonathan A. Weyn, 2020, Journal of Advances in Modeling Earth Systems.

How Useful Is Relevance

Rich Caruana, Dayne Freitag, Dayne Freitag, 1994 .

Outside the Machine Learning Blackbox: Supporting Analysts Before and After the Learning Algorithm

Claire Cardie, Rich Caruana, Miles Arthur Munson, 2010 .

Optimizing to Arbitrary NLP Metrics using Ensemble Selection

Claire Cardie, Rich Caruana, Art Munson, 2005, HLT.

Efficient Forward Architecture Search

John Langford, Eric Horvitz, Rich Caruana, 2019, NeurIPS.

Intelligible models for classification and regression

Johannes Gehrke, Rich Caruana, Yin Lou, 2012, KDD.

Distributed tuning of machine learning algorithms using MapReduce Clusters

Rich Caruana, Sara Javanmardi, Cristina Videira Lopes, 2011, LDMTA '11.

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

Cristina V. Lopes, Rich Caruana, Yasser Ganjisaffar, 2011, SIGIR.

Additive Groves of Regression Trees

Rich Caruana, Mirek Riedewald, Daria Sorokina, 2007, ECML.

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

Richard A. Caruana, R. Caruana, 1987, Int. J. Approx. Reason..

An empirical comparison of supervised learning algorithms

Rich Caruana, Alexandru Niculescu-Mizil, R. Caruana, 2006, ICML.

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

Rich Caruana, Alexandru Niculescu-Mizil, R. Caruana, 2004, ROCAI.

Accurate intelligible models with pairwise interactions

Johannes Gehrke, Rich Caruana, Giles Hooker, 2013, KDD.

Improving Classification with Pairwise Constraints: A Margin-Based Approach

Rich Caruana, Nam Nguyen, R. Caruana, 2008, ECML/PKDD.

Classification with partial labels

Rich Caruana, Nam Nguyen, R. Caruana, 2008, KDD.

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

R. Caruana, A. Goldenberg, C. Chang, 2021, ICLR.

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

Johannes Gehrke, Rich Caruana, Noémie Elhadad, 2015, KDD.

An evaluation of machine-learning methods for predicting pneumonia mortality

Constantin F. Aliferis, Tom M. Mitchell, Rich Caruana, 1997, Artif. Intell. Medicine.

Predicting good probabilities with supervised learning

Rich Caruana, Alexandru Niculescu-Mizil, R. Caruana, 2005, ICML.

Obtaining Calibrated Probabilities from Boosting

Rich Caruana, Alexandru Niculescu-Mizil, R. Caruana, 2005, UAI.

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

Harmanpreet Kaur, Rich Caruana, Hanna M. Wallach, 2020, CHI.

InterpretML: A Unified Framework for Machine Learning Interpretability pdf

Rich Caruana, Harsha Nori, Paul Koch, 2019, ArXiv.

Faithful and Customizable Explanations of Black Box Models

Jure Leskovec, Rich Caruana, Himabindu Lakkaraju, 2019, AIES.

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

Benjamin J. Lengerich, R. Caruana, G. Hooker, 2019, AISTATS.

Neural Cognitive Architectures for Never-Ending Learning

Emmanouil Antonios Platanios, Tom M. Mitchell, R. Caruana, 2019 .

Multitask Pattern Recognition for Vision-Based Autonomous Robots

Rich Caruana, Joseph O'Sullivan, R. Caruana, 1998 .

A Dozen Tricks with Multitask Learning

Rich Caruana, R. Caruana, 1996, Neural Networks: Tricks of the Trade.

Algorithms and Applications for Multitask Learning

Rich Caruana, R. Caruana, 1996, ICML.

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

Tom M. Mitchell, Rich Caruana, Shumeet Baluja, 1995, NIPS.

Interpretable & Explorable Approximations of Black Box Models pdf

Jure Leskovec, Rich Caruana, Himabindu Lakkaraju, 2017, ArXiv.

Data-Mining Discovery of Pattern and Process in Ecological Systems

WESLEY M. HOCHACHKA, RICH CARUANA, DANIEL FINK, 2007 .

Data-Intensive Science: A New Paradigm for Biodiversity Studies

Steve Kelling, Rich Caruana, Mirek Riedewald, 2009 .

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

Steven M. Drucker, Rich Caruana, Robert DeLine, 2019, CHI.

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

Larry J. Eshelman, J. David Schaffer, Rich Caruana, 1989, ML.

An empirical evaluation of supervised learning in high dimensions

Rich Caruana, Ainur Yessenalina, Nikolaos Karampatziakis, 2008, ICML '08.

Consensus Clusterings

Rich Caruana, Nam Nguyen, R. Caruana, 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

Learning Global Additive Explanations for Neural Nets Using Model Distillation

Albert Gordo, Rich Caruana, Sarah Tan, 2018 .

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

Eric Horvitz, Rich Caruana, Himabindu Lakkaraju, 2016, AAAI.

Best Of NIPS 2005: Highlights on the 'Inductive Transfer : 10 Years Later' Workshop

Massimiliano Pontil, Rich Caruana, Prasad Tadepalli, 2006 .

Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data

Jonathan A. Weyn, R. Caruana, D. Durran, 2019, Journal of Advances in Modeling Earth Systems.

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

John David N. Dionisio, Hooshang Kangarloo, Rich Caruana, 1999, AMIA.

Augmenting interpretable models with large language models during training

R. Caruana, Armin Askari, Jianfeng Gao, 2022, Nature communications.

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

Eric P. Xing, Rich Caruana, Benjamin J. Lengerich, 2020, ArXiv.

Detecting statistical interactions with additive groves of trees

Rich Caruana, Mirek Riedewald, Daria Sorokina, 2008, ICML '08.

Dropout as a Regularizer of Interaction Effects

Benjamin J. Lengerich, E. Xing, R. Caruana, 2020, AISTATS.

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

Rich Caruana, Virginia R. de Sa, R. Caruana, 1996, NIPS.

Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges

Rich Caruana, Harsha Nori, Samuel Jenkins, 2020, KDD.

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

Richard Caruana, R. Caruana, 2019, KDD.

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

Rich Caruana, Giles Hooker, Sarah Tan, 2017, AIES.

Efficiently exploring architectural design spaces via predictive modeling

Sally A. McKee, Martin Schulz, Bronis R. de Supinski, 2006, ASPLOS XII.

GAM Changer: Editing Generalized Additive Models with Interactive Visualization pdf

Rich Caruana, Duen Horng Chau, Mihaela Vorvoreanu, 2021, ArXiv.

Improving Document Ranking with Dual Word Embeddings

Bhaskar Mitra, Nick Craswell, Rich Caruana, 2016, WWW.

Extending Explainable Boosting Machines to Scientific Image Data

Craig S. Greenberg, Justyna P. Zwolak, R. Caruana, 2023, ArXiv.

Mining citizen science data to predict orevalence of wild bird species

Steve Kelling, Rich Caruana, Mirek Riedewald, 2006, KDD '06.

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

Matthew Richardson, Rich Caruana, Matthai Philipose, 2016, ICLR.

Benefitting from the Variables that Variable Selection Discards

Rich Caruana, Virginia R. de Sa, R. Caruana, 2003, J. Mach. Learn. Res..

A Dual Embedding Space Model for Document Ranking pdf

Bhaskar Mitra, Nick Craswell, Rich Caruana, 2016, ArXiv.

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

R. Caruana, S. Tan, Yin Lou, 2021, PKDD/ECML Workshops.

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

Matthew Richardson, Rich Caruana, Matthai Philipose, 2016, ArXiv.

Active Learning with Model Selection

Ashish Kapoor, Rich Caruana, Alnur Ali, 2014, AAAI.

A method for measuring the relative information content of data from different monitoring protocols

Steve Kelling, Rich Caruana, Daniel Fink, 2010 .

On Feature Selection, Bias-Variance, and Bagging

Rich Caruana, M. Arthur Munson, R. Caruana, 2009, ECML/PKDD.

Classifier Loss Under Metric Uncertainty

Rich Caruana, David B. Skalak, D. B. Skalak, 2007, ECML.

An Empirical Comparison of Supervised Learning Algorithms Using Different Performance Metrics

Rich Caruana, Alex Niculescu-Mizil, R. Caruana, 2005 .

Getting the Most out of your Data: Multitask Bayesian Network Structure Learning, Predicting Good Probabilities and Ensemble Selection

Alexandru Niculescu-Mizil, Richard Caruana, R. Caruana, 2008 .

An Empirical Evaluation of Supervised Learning for ROC Area

Rich Caruana, Alexandru Niculescu-Mizil, R. Caruana, 2004, ROCAI.

How Interpretable and Trustworthy are GAMs?

Rich Caruana, Anna Goldenberg, Chun-Hao Chang, 2020, KDD.

Inductive Transfer for Bayesian Network Structure Learning

Rich Caruana, Alexandru Niculescu-Mizil, R. Caruana, 2007, ICML Unsupervised and Transfer Learning.

Differentially Private Estimation of Heterogeneous Causal Effects

R. Caruana, Donald Ngwe, A. Kannan, 2022, CLeaR.

Blending LSTMs into CNNs

Matthew Richardson, Rich Caruana, Matthai Philipose, 2015, ICLR 2016.

Discovering Unknown Unknowns of Predictive Models

Eric Horvitz, Rich Caruana, Himabindu Lakkaraju, 2016 .

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

Eric Horvitz, Rich Caruana, Himabindu Lakkaraju, 2016, ArXiv.

Axiomatic Interpretability for Multiclass Additive Models

Rich Caruana, Xuezhou Zhang, Paul Koch, 2018, KDD.

Implicit Preference Labels for Learning Highly Selective Personalized Rankers

Milad Shokouhi, Paul N. Bennett, Rich Caruana, 2015, ICTIR.

Using Feature Selection to Find Inputs that Work Better as Extra Outputs

Rich Caruana, Virginia R. de Sa, R. Caruana, 1998 .

Special Issue on Responsible AI and Human-AI Interaction

James R. Foulds, Hanna M. Wallach, Jennifer Wortman Vaughan, 2022 .

Automatic Machine Learning (AutoML)

Balázs Kégl, Isabelle Guyon, Frank Hutter, 2015, ICML 2015.

Considerations when learning additive explanations for black-box models

R. Caruana, G. Hooker, Albert Gordo, 2018, Machine Learning.

Structured labeling for facilitating concept evolution in machine learning

Todd Kulesza, Rich Caruana, Danyel Fisher, 2014, CHI.

Accuracy, Interpretability, and Differential Privacy via Explainable Boosting

R. Caruana, Janardhan Kulkarni, Zhiqi Bu, 2021, ICML.

Case-Based Explanation for Artificial Neural Nets

Rich Caruana, R. Caruana, 2000, ANNIMAB.

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

Cristina V. Lopes, David W. McDonald, Rich Caruana, 2013, Mining Social Networks and Security Informatics.

Auditing Black-Box Models Using Transparent Model Distillation With Side Information

Rich Caruana, Giles Hooker, Sarah Tan, 2017 .

GAM Coach: Towards Interactive and User-centered Algorithmic Recourse

Duen Horng Chau, Zijie J. Wang, R. Caruana, 2023, CHI.

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

Rich Caruana, Xuezhou Zhang, Paul Koch, 2018, ArXiv.

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

Rich Caruana, Anna Goldenberg, George Alexandru Adam, 2021, ArXiv.

Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, California, USA, August 12-15, 2007

Rich Caruana, Xindong Wu, Pavel Berkhin, 2007, KDD.

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

E. Horvitz, R. Caruana, Ece Kamar, 2016, AAAI.

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

R. Caruana, A. Goldenberg, C. Chang, 2021, ICLR.

Artificial intelligence in medicine

Geoffrey J. Gordon, Tom Michael Mitchell, P J Drew, 1989, Singapore medical journal.

Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings

Geoffrey J. Gordon, Johanna D. Moore, Tom Michael Mitchell, 2019, Lecture Notes in Computer Science.

Artificial intelligence in medicine

Diana E. Forsythe, Bruce G. Buchanan, Geoffrey J. Gordon, 1989, Springer US.

Efficient architectural design space exploration via predictive modeling

Sally A. McKee, Martin Schulz, Bronis R. de Supinski, 2008, TACO.

On Feature Selection, Bias-Variance, and Bagging

R. Caruana, M. Arthur Munson, 2009, ECML/PKDD.

KDD-Cup 2004: results and analysis

T. Joachims, R. Caruana, L. Backstrom, 2004, SKDD.