Rich Caruana

According to our database1, Rich Caruana authored at least 91 papers between 1987 and 2018.

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Bibliography

2018
Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models.
CoRR, 2018

Transparent Model Distillation.
CoRR, 2018

Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

2017
Detecting Bias in Black-Box Models Using Transparent Model Distillation.
CoRR, 2017

Interpretable & Explorable Approximations of Black Box Models.
CoRR, 2017

Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)?
CoRR, 2016

A Dual Embedding Space Model for Document Ranking.
CoRR, 2016

Discovering Blind Spots of Predictive Models: Representations and Policies for Guided Exploration.
CoRR, 2016

Improving Document Ranking with Dual Word Embeddings.
Proceedings of the 25th International Conference on World Wide Web, 2016

Analysis of Deep Neural Networks with Extended Data Jacobian Matrix.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Detecting Migrating Birds at Night.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

2015
Compressing LSTMs into CNNs.
CoRR, 2015

Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission.
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Implicit Preference Labels for Learning Highly Selective Personalized Rankers.
Proceedings of the 2015 International Conference on The Theory of Information Retrieval, 2015

2014
Sparse Partially Linear Additive Models.
CoRR, 2014

Do Deep Nets Really Need to be Deep?
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Structured labeling for facilitating concept evolution in machine learning.
Proceedings of the CHI Conference on Human Factors in Computing Systems, 2014

Gauss meets Canadian traveler: shortest-path problems with correlated natural dynamics.
Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2014

Active Learning with Model Selection.
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013
Learning to Detect Vandalism in Social Content Systems: A Study on Wikipedia - Vandalism Detection in Wikipedia.
Proceedings of the Mining Social Networks and Security Informatics, 2013

Introduction to the Special Issue ACM SIGKDD 2012.
TKDD, 2013

Using Multiple Samples to Learn Mixture Models.
CoRR, 2013

Learning Likely Locations.
Proceedings of the User Modeling, Adaptation, and Personalization, 2013

Using multiple samples to learn mixture models.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Accurate intelligible models with pairwise interactions.
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Clustering: probably approximately useless?
Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, 2013

2012
A Dozen Tricks with Multitask Learning.
Proceedings of the Neural Networks: Tricks of the Trade - Second Edition, 2012

Special issue on best of SIGKDD 2011.
TKDD, 2012

Inductive Transfer for Bayesian Network Structure Learning.
Proceedings of the Unsupervised and Transfer Learning, 2012

Obtaining Calibrated Probabilities from Boosting
CoRR, 2012

Intelligible models for classification and regression.
Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

Learning speaker, addressee and overlap detection models from multimodal streams.
Proceedings of the International Conference on Multimodal Interaction, 2012

2011
Bagging gradient-boosted trees for high precision, low variance ranking models.
Proceedings of the Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2011

2009
On Feature Selection, Bias-Variance, and Bagging.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009

Detecting and Interpreting Variable Interactions in Observational Ornithology Data.
Proceedings of the ICDM Workshops 2009, 2009

2008
Efficient architectural design space exploration via predictive modeling.
TACO, 2008

Improving Classification with Pairwise Constraints: A Margin-Based Approach.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

Classification with partial labels.
Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008

Self-Optimizing Memory Controllers: A Reinforcement Learning Approach.
Proceedings of the 35th International Symposium on Computer Architecture (ISCA 2008), 2008

Detecting statistical interactions with additive groves of trees.
Proceedings of the Machine Learning, 2008

An empirical evaluation of supervised learning in high dimensions.
Proceedings of the Machine Learning, 2008

2007
Inductive Transfer for Bayesian Network Structure Learning.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Predicting parallel application performance via machine learning approaches.
Concurrency and Computation: Practice and Experience, 2007

Consensus Clusterings.
Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), 2007

Additive Groves of Regression Trees.
Proceedings of the Machine Learning: ECML 2007, 2007

Classifier Loss Under Metric Uncertainty.
Proceedings of the Machine Learning: ECML 2007, 2007

2006
Mining citizen science data to predict orevalence of wild bird species.
Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006

Model compression.
Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006

C2FS: An Algorithm for Feature Selection in Cascade Neural Networks.
Proceedings of the International Joint Conference on Neural Networks, 2006

An empirical comparison of supervised learning algorithms.
Proceedings of the Machine Learning, 2006

Getting the Most Out of Ensemble Selection.
Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 2006

Meta Clustering.
Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 2006

Efficiently exploring architectural design spaces via predictive modeling.
Proceedings of the 12th International Conference on Architectural Support for Programming Languages and Operating Systems, 2006

2005
Predicting dire outcomes of patients with community acquired pneumonia.
Journal of Biomedical Informatics, 2005

Obtaining Calibrated Probabilities from Boosting.
Proceedings of the UAI '05, 2005

Optimizing to Arbitrary NLP Metrics using Ensemble Selection.
Proceedings of the HLT/EMNLP 2005, 2005

Predicting good probabilities with supervised learning.
Proceedings of the Machine Learning, 2005

2004
KDD-Cup 2004: results and analysis.
SIGKDD Explorations, 2004

Data Mining in Metric Space: An Empirical Analysis of Supervised Learning Performance Criteria.
Proceedings of the ROC Analysis in Artificial Intelligence, 1st International Workshop, 2004

An Empirical Evaluation of Supervised Learning for ROC Area.
Proceedings of the ROC Analysis in Artificial Intelligence, 1st International Workshop, 2004

Data mining in metric space: an empirical analysis of supervised learning performance criteria.
Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004

Ensemble selection from libraries of models.
Proceedings of the Machine Learning, 2004

2003
Benefitting from the Variables that Variable Selection Discards.
Journal of Machine Learning Research, 2003

Evaluating the C-section Rate of Different Physician Practices: Using Machine Learning to Model Standard Practice.
Proceedings of the AMIA 2003, 2003

2002
Machine learning for sub-population assessment: evaluating the C-section rate of different physician practices.
Proceedings of the AMIA 2002, 2002

2001
(Not) Bounding the True Error.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

A Non-Parametric EM-Style Algorithm for Imputing Missing Values.
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001

2000
Bridging the lexical chasm: statistical approaches to answer-finding.
Proceedings of the SIGIR 2000: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2000

Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness.
Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29, 2000

1999
Case-based explanation of non-case-based learning methods.
Proceedings of the AMIA 1999, 1999

1998
Multitask pattern recognition for autonomous robots.
Proceedings of the Proceedings 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, 1998

1997
Multitask Learning.
Machine Learning, 1997

An evaluation of machine-learning methods for predicting pneumonia mortality.
Artificial Intelligence in Medicine, 1997

1996
Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs.
Proceedings of the Advances in Neural Information Processing Systems 9, 1996

A Dozen Tricks with Multitask Learning.
Proceedings of the Neural Networks: Tricks of the Trade, 1996

Algorithms and Applications for Multitask Learning.
Proceedings of the Machine Learning, 1996

1995
Using the Future to Sort Out the Present: Rankprop and Multitask Learning for Medical Risk Evaluation.
Proceedings of the Advances in Neural Information Processing Systems 8, 1995

Removing the Genetics from the Standard Genetic Algorithm.
Proceedings of the Machine Learning, 1995

1994
Experience with a Learning Personal Assistant.
Commun. ACM, 1994

Learning Many Related Tasks at the Same Time with Backpropagation.
Proceedings of the Advances in Neural Information Processing Systems 7, 1994

Greedy Attribute Selection.
Proceedings of the Machine Learning, 1994

1993
Multitask Learning: A Knowledge-Based Source of Inductive Bias.
Proceedings of the Machine Learning, 1993

1989
Representation and Hidden Bias II: Eliminating Defining Length Bias in Genetic Search via Shuffle Crossover.
Proceedings of the 11th International Joint Conference on Artificial Intelligence. Detroit, 1989

Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms.
Proceedings of the Sixth International Workshop on Machine Learning (ML 1989), 1989

A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization.
Proceedings of the 3rd International Conference on Genetic Algorithms, 1989

Biases in the Crossover Landscape.
Proceedings of the 3rd International Conference on Genetic Algorithms, 1989

1988
The automatic training of rule bases that use numerical uncertainty representations.
Int. J. Approx. Reasoning, 1988

Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms.
Proceedings of the Machine Learning, 1988

1987
The Automatic Training of Rule Bases that Use Numerical Uncertainty Representations.
Proceedings of the UAI '87: Proceedings of the Third Annual Conference on Uncertainty in Artificial Intelligence, 1987


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