Ron Kohavi

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Irrelevant Features and the Subset Selection Problem

Ron Kohavi, George H. John, George H. John, 1994, ICML.

Feature Subset Selection as Search with Probabilistic Estimates

Ron Kohavi, 1994 .

Bias Plus Variance Decomposition for Zero-One Loss Functions

Ron Kohavi, David H. Wolpert, D. Wolpert, 1996, ICML.

Online Controlled Experiments and A / B Tests

Ron Kohavi, R. Longbotham, 2015 .

Online Controlled Experiments and A/B Testing

Ron Kohavi, Roger Longbotham, Ron Kohavi, 2017, Encyclopedia of Machine Learning and Data Mining.

An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Eric Bauer, Ron Kohavi, Ron Kohavi, 1999, Machine Learning.

Visualizing the Simple Bayesian Classi er

Ron Kohavi, B. Becker, D. Sommer, 1997 .

Online randomized controlled experiments at scale: lessons and extensions to medicine

Ron Kohavi, J. Ioannidis, Diane Tang, 2020, Trials.

A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection

Ron Kohavi, Ron Kohavi, 1995, IJCAI.

Wrappers for Feature Subset Selection

Ron Kohavi, George H. John, Ron Kohavi, 1997, Artif. Intell..

Trustworthy online controlled experiments: five puzzling outcomes explained

Ron Kohavi, Brian Frasca, Roger Longbotham, 2012, KDD.

Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology

Ron Kohavi, Dan Sommerfield, Ron Kohavi, 1995, KDD.

Useful Feature Subsets and Rough Set Reducts

Ron Kohavi, Brian Frasca, Ron Kohavi, 1994 .

Real world performance of association rule algorithms

Ron Kohavi, Zijian Zheng, Llew Mason, 2001, KDD '01.

The Case against Accuracy Estimation for Comparing Induction Algorithms

Ron Kohavi, Tom Fawcett, Foster J. Provost, 1998, ICML.

Data Mining Using MLC a Machine Learning Library in C++

Ron Kohavi, Dan Sommerfield, James Dougherty, 1996, Int. J. Artif. Intell. Tools.

Online Experiments: Lessons Learned

Ron Kohavi, Roger Longbotham, Ron Kohavi, 2007, Computer.

Practical guide to controlled experiments on the web: listen to your customers not to the hippo

Ron Kohavi, Dan Sommerfield, Randal M. Henne, 2007, KDD '07.

Wrappers for feature selection

Ron Kohavi, George H. John, Ron Kohavi, 1997 .

Option Decision Trees with Majority Votes

Ron Kohavi, Clayton Kunz, Ron Kohavi, 1997, ICML.

Decision tree discovery

R. Quinlan, Ron Kohavi, 1999 .

KDD-Cup 2000 organizers' report: peeling the onion

Carla E. Brodley, Ron Kohavi, Brian Frasca, 2000, SKDD.

Improving the sensitivity of online controlled experiments by utilizing pre-experiment data

Ron Kohavi, Alex Deng, Ya Xu, 2013, WSDM.

Wrappers for performance enhancement and oblivious decision graphs

Ron Kohavi, Ron Kohavi, 1995 .

Automatic Parameter Selection by Minimizing Estimated Error

Ron Kohavi, George H. John, Ron Kohavi, 1995, ICML.

Research Note on Decision Lists

Ron Kohavi, S. Benson, 1993, Machine Learning.

Supervised and Unsupervised Discretization of Continuous Features

Ron Kohavi, Mehran Sahami, James Dougherty, 1995, ICML.

Controlled experiments on the web: survey and practical guide

Ron Kohavi, Roger Longbotham, Dan Sommerfield, 2009, Data Mining and Knowledge Discovery.

Ten Supplementary Analyses to Improve E-commerce Web Sites

Ron Kohavi, R. Parekh, 2003 .

Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid

Ron Kohavi, Ron Kohavi, 1996, KDD.

KDD-2004 : proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 22-25, 2004, Seattle, Washington, USA

Ron Kohavi, 2004 .

To appear in the Third International Workshop on Rough Sets and Soft Computing (RSSC 94) Useful Feature Subsets and Rough Set Reducts

Ron Kohavi, Brian Frasca, 2015 .

C 5 . 1 . 5 Bayesian Classi cation

N. Friedman, Ron Kohavi, 1999 .

1 THE WRAPPER APPROACH

Ron Kohavi, H. George, JohnData, 1997 .

Ethics in Controlled Experiments

Ron Kohavi, Diane Tang, Ron Kohavi, 2020 .

Appears in ECML-98 as a research note Pruning Decision Trees with Misclassi cation Costs

Ron Kohavi, C. Brodley, Clifford Brunk, 2015 .

Appears in Ecml-98 as a Research Note a Longer Version Is Available as Ece Tr 98-3, Purdue University Pruning Decision Trees with Misclassiication Costs 1 Pruning Decision Trees

Ron Kohavi, C. Brodley, Clifford Brunk, 2008 .

Empirical Methods for Artiicial Intelligence

P. Cohen, Ron Kohavi, 2007 .

To appear in the Third International Workshop on Rough Sets and Soft Computing (RSSC 94) A Third Dimension to Rough Sets

Ron Kohavi, 2015 .

Introductory Topics for Everyone

Ron Kohavi, Diane Tang, Ya Xu, 2020, Trustworthy Online Controlled Experiments.

Sgimlc ++ Utilities 2.0

Ron Kohavi, Dan Sommerreld, 1996 .

Visualizing the simple Baysian classifier

Ron Kohavi, Barry G. Becker, Dan Sommerfield, 2001 .

A Simple Method for Subset Selection Which Assists Learning Schemes in the Execution Of

Ron Kohavi, K. Ppeger, G H John, 1995 .

Feature Selection for Knowledge Discovery and Data Mining

Ron Kohavi, George H. John, Ron Kohavi, 1998 .

Special Issue on Applications of data mining to electronic commerce

Ron Kohavi, Foster Provost, Foster Provost, 2001 .

Improving simple Bayes

Ron Kohavi, Barry G. Becker, Dan Sommerfield, 1997 .

Knowledge discovery and data mining cup part of SIGKDD 2000

Carla E. Brodley, Ron Kohavi, Ron Kohavi, 2000 .

Data Mining using MLC

Ron Kohavi, Dan Sommerfield, James Dougherty, 1996 .

Trustworthy Online Controlled Experiments

Ron Kohavi, Diane Tang, Ya Xu, 2020 .

Integrating E-Commerce and Data Mining

Ron Kohavi, Llew Mason, Zijian Zheng, 2000 .

The Surprising Power of Online Experiments

Ron Kohavi, Stefan H. Thomke, Ron Kohavi, 2017 .

Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points

Jaideep Srivastava, Myra Spiliopoulou, Ron Kohavi, 2001 .

Book Review: Empirical Methods for Artificial Intelligence

Ron Kohavi, Ron Kohavi, 1996, Int. J. Neural Syst..

Mining e-commerce data: the good, the bad, and the ugly

Ron Kohavi, Ron Kohavi, 2001, KDD '01.

Web mining for e-commerce (workshop session) (title only)

Jaideep Srivastava, Myra Spiliopoulou, Ron Kohavi, 2000, Knowledge Discovery and Data Mining.

B o t t o m-U p Induction of Oblivious Read-Once Decision Graphs

Ron Kohavi, 2005 .

A/B Testing Intuition Busters: Common Misunderstandings in Online Controlled Experiments

Ron Kohavi, Alex Deng, Lukas Vermeer, 2022, KDD.

Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology

Nathaniel T. Stevens, Jonathan W. Stallrich, Ron Kohavi, 2022, 2212.11366.

Data Mining Using MLC a Machine Learning Library in C++

Ron Kohavi, D. Sommerfield, James Dougherty, 1996, Int. J. Artif. Intell. Tools.

Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology

Nathaniel T. Stevens, Jonathan W. Stallrich, Ron Kohavi, 2022, 2212.11366.

Data Mining and Visualization

Ron Kohavi, Ron Kohavi, 2000 .

Targeting Business Users with Decision Table Classifiers

Ron Kohavi, Dan Sommerfield, Ron Kohavi, 1998, KDD.

Observational Causal Studies

Ron Kohavi, Diane Tang, Ya Xu, 2020 .

Choosing a Randomization Unit

Ron Kohavi, Diane Tang, Ya Xu, 2020, Trustworthy Online Controlled Experiments.

Complementary and Alternative Techniques to Controlled Experiments

Ron Kohavi, Ya Xu, D. Tang, 2020 .

Measuring Long-Term Treatment Effects

Ron Kohavi, Diane Tang, Ya Xu, 2020 .

Advanced Topics for Analyzing Experiments

Ron Kohavi, Diane Tang, Ya Xu, 2020 .

Sample Ratio Mismatch and Other Trust-Related Guardrail Metrics

Ron Kohavi, Diane Tang, Ya Xu, 2020 .

The Statistics behind Online Controlled Experiments

Ron Kohavi, Diane Tang, Ya Xu, 2020 .

Client-Side Experiments

Ron Kohavi, Diane Tang, Ya Xu, 2020, Trustworthy Online Controlled Experiments.

Variance Estimation and Improved Sensitivity: Pitfalls and Solutions

Ron Kohavi, Ya Xu, D. Tang, 2020, Trustworthy Online Controlled Experiments.

Metrics for Experimentation and the Overall Evaluation Criterion

Ron Kohavi, Diane Tang, Ya Xu, 2020 .

Experimentation Platform and Culture

Ron Kohavi, Diane Tang, Ya Xu, 2020 .

Twyman’s Law and Experimentation Trustworthiness

Ron Kohavi, Diane Tang, Ya Xu, 2020 .

Institutional Memory and Meta-Analysis

Ron Kohavi, Diane Tang, Ya Xu, 2020 .

The A/A Test

Ron Kohavi, Diane Tang, Ya Xu, 2020, Trustworthy Online Controlled Experiments.

Advanced Topics for Building an Experimentation Platform

Ron Kohavi, Diane Tang, Ya Xu, 2020, Trustworthy Online Controlled Experiments.

Triggering for Improved Sensitivity

Ron Kohavi, Diane Tang, Ya Xu, 2020, Trustworthy Online Controlled Experiments.

Selected Topics for Everyone

Ron Kohavi, Diane Tang, Ya Xu, 2020, Trustworthy Online Controlled Experiments.

Unexpected results in online controlled experiments

Ron Kohavi, Roger Longbotham, Ron Kohavi, 2011, SKDD.

Seven pitfalls to avoid when running controlled experiments on the web

Ron Kohavi, Thomas Crook, Brian Frasca, 2009, KDD.

Lessons and Challenges from Mining Retail E-Commerce Data

Rajesh Parekh, Ron Kohavi, Zijian Zheng, 2004, Machine Learning.

The Power of Decision Tables

Ron Kohavi, Ron Kohavi, 1995, ECML.

Oblivious Decision Trees, Graphs, and Top-Down Pruning

Ron Kohavi, Chia-Hsin Li, Ron Kohavi, 1995, IJCAI.

Lazy Decision Trees

Ron Kohavi, Jerome H. Friedman, Yeogirl Yun, 1996, AAAI/IAAI, Vol. 1.

The Wrapper Approach

Ron Kohavi, George H. John, Ron Kohavi, 1998 .

AIJ special issue on relevance Wrappers for Feature Subset Selection

George H. John, Ron Kohavi, 1996 .

Data mining tasks and methods: Classification: decision-tree discovery

Ron Kohavi, J. Ross Quinlan, J. R. Quinlan, 2002 .

Online Experimentation at Microsoft

Ron Kohavi, Juan Lavista Ferres, Brian Frasca, 2009 .

Online controlled experiments at large scale

Ron Kohavi, Brian Frasca, Alex Deng, 2013, KDD.

Emerging trends in business analytics

Ron Kohavi, Evangelos Simoudis, Neal Rothleder, 2002, CACM.

IN BUSINESS ANALYTICS

Ron Kohavi, Neal J. Rothleder, Evangelos Simoudis, 2002 .

Int Reduction

Ron Kohavi, 1999 .

D2.1.2 Mlc ++ D2.1.2.1 Motivation for Mlc ++

Ron Kohavi, D. Sommerfield, 1998 .

MineSet: An Integrated System for Data Mining

Ron Kohavi, James Kelly, Clifford Brunk, 1997, KDD.

Data mining using /spl Mscr//spl Lscr//spl Cscr/++ a machine learning library in C++

Ron Kohavi, Dan Sommerfield, James Dougherty, 1996, Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence.

Error-Based and Entropy-Based Discretization of Continuous Features

Ron Kohavi, Mehran Sahami, Ron Kohavi, 1996, KDD.

Guest Editors' Introduction: On Applied Research in Machine Learning

Ron Kohavi, Foster J. Provost, Ron Kohavi, 1998, Machine Learning.

Mining e-commerce data: the good, the bad, and the ugly

Ron Kohavi, Ron Kohavi, 2001, KDD '01.

Pruning Decision Trees with Misclassification Costs

Carla E. Brodley, Ron Kohavi, Clayton Kunz, 1998, ECML.

Integrating e-commerce and data mining: architecture and challenges

Ron Kohavi, Zijian Zheng, Llew Mason, 2000, Proceedings 2001 IEEE International Conference on Data Mining.

The Utility of Feature Weighting in Nearest-Neighbor Algorithms

Ron Kohavi, Pat Langley, Yeogirl Yun, 1997 .

Data mining tasks and methods: Classification: Bayesian classification

Ron Kohavi, Nir Friedman, N. Friedman, 2002 .

Top Challenges from the first Practical Online Controlled Experiments Summit

Ya Xu, Somit Gupta, Diane Tang, 2019, SKDD.

Running and Analyzing Experiments

Ron Kohavi, Diane Tang, Ya Xu, 2020 .

Case studies: Commercial, multiple mining tasks systems: mineSet

Ron Kohavi, Cliff Brunk, Ron Kohavi, 2002 .

Case studies: Public domain, multiple mining tasks systems: MLC++

Ron Kohavi, Daniel A. Sommerfield, Ron Kohavi, 2002 .

Data Mining with MineSet: What Worked, What Did Not, and What Might

Ron Kohavi, Ron Kohavi, 1998 .

MLC++: a machine learning library in C++

Ron Kohavi, David Manley, George H. John, 1994, Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94.

Pitfalls of long-term online controlled experiments

Ron Kohavi, Brian Frasca, Pavel Dmitriev, 2016, 2016 IEEE International Conference on Big Data (Big Data).

Seven rules of thumb for web site experimenters

Ron Kohavi, Roger Longbotham, Alex Deng, 2014, KDD.

Online Experiments: Practical Lessons

Ron Kohavi, Roger Longbotham, Toby Walker, 2010, Computer.

E-Commerce Recommendation Applications

John Riedl, Joseph A. Konstan, J. Ben Schafer, 2004, Data Mining and Knowledge Discovery.

Bottom-Up Induction of Oblivious Read-Once Decision Graphs: Strengths and Limitations

Ron Kohavi, Ron Kohavi, 1994, AAAI.

Applications of Data Mining to Electronic Commerce

Ron Kohavi, Foster J. Provost, Ron Kohavi, 2000, Springer US.

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

Won Y. Kim, Ron Kohavi, W. DuMouchel, 2004, KDD.

Applications of Data Mining to Electronic Commerce

Ron Kohavi, Foster J. Provost, Ron Kohavi, 2000, Data Mining and Knowledge Discovery.

Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology

Nathaniel T. Stevens, Jonathan W. Stallrich, Ron Kohavi, 2022, The American Statistician.

Bottom-Up Induction of Oblivious Read-Once Decision Graphs

Ron Kohavi, 1994, ECML.

A/B Testing at Scale: Accelerating Software Innovation

Ron Kohavi, Pavel Dmitriev, Alex Deng, 2017, SIGIR.

Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology

Nathaniel T. Stevens, Jonathan W. Stallrich, Ron Kohavi, 2022, The American Statistician.

Autmatic parameter selection by minimizing estimated error

George H. John, Ron Kohavi, 1995, ICML 1995.

A/B Testing Intuition Busters: Common Misunderstandings in Online Controlled Experiments

Ron Kohavi, Alex Deng, Lukas Vermeer, 2022, KDD.

Trustworthy online controlled experiments: five puzzling outcomes explained

Ron Kohavi, R. Longbotham, Ya Xu, 2012, KDD.

Emerging Trends

Ron Kohavi, Neal Rothleder, Evangelos Simoudis, 2012 .

Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid

Ron Kohavi, 1996, KDD.