| 2024 | KDD | False Positives in A/B Tests. | Ron Kohavi, Nanyu Chen |
| 2022 | KDD | A/B Testing Intuition Busters: Common Misunderstandings in Online Controlled Experiments. | Ron Kohavi, Alex Deng, Lukas Vermeer |
| 2017 | SIGIR | A/B Testing at Scale: Accelerating Software Innovation. | Alex Deng, Pavel A. Dmitriev, Somit Gupta, Ron Kohavi, Paul Raff, Lukas Vermeer |
| 2015 | KDD | Online Controlled Experiments: Lessons from Running A/B/n Tests for 12 Years. | Ron Kohavi |
| 2014 | KDD | Seven rules of thumb for web site experimenters. | Ron Kohavi, Alex Deng, Roger Longbotham, Ya Xu |
| 2013 | KDD | Online controlled experiments at large scale. | Ron Kohavi, Alex Deng, Brian Frasca, Toby Walker, Ya Xu, Nils Pohlmann |
| 2013 | WSDM | Improving the sensitivity of online controlled experiments by utilizing pre-experiment data. | Alex Deng, Ya Xu, Ron Kohavi, Toby Walker |
| 2012 | KDD | Trustworthy online controlled experiments: five puzzling outcomes explained. | Ron Kohavi, Alex Deng, Brian Frasca, Roger Longbotham, Toby Walker, Ya Xu |
| 2012 | RecSys | Online controlled experiments: introduction, learnings, and humbling statistics. | Ron Kohavi |
| 2009 | KDD | Seven pitfalls to avoid when running controlled experiments on the web. | Thomas Crook, Brian Frasca, Ron Kohavi, Roger Longbotham |
| 2007 | KDD | Practical guide to controlled experiments on the web: listen to your customers not to the hippo. | Ron Kohavi, Randal M. Henne, Dan Sommerfield |
| 2004 | SDM | Visualizing RFM Segmentation. | Ron Kohavi, Rajesh Parekh |
| 2001 | ICDM | Integrating E-Commerce and Data Mining: Architecture and Challenges. | Suhail Ansari, Ron Kohavi, Llew Mason, Zijian Zheng |
| 2001 | KDD | Mining e-commerce data: the good, the bad, and the ugly. | Ron Kohavi |
| 2001 | KDD | Real world performance of association rule algorithms. | Zijian Zheng, Ron Kohavi, Llew Mason |
| 2001 | PAKDD | Mining E-Commerce Data: The Good, the Bad, and the Ugly. | Ron Kohavi |
| 2000 | KDD | Web mining for e-commerce (workshop session - title only). | Ron Kohavi, Myra Spiliopoulou, Jaideep Srivastava |
| 1998 | ICML | The Case against Accuracy Estimation for Comparing Induction Algorithms. | Foster J. Provost, Tom Fawcett, Ron Kohavi |
| 1998 | KDD | Targeting Business Users with Decision Table Classifiers. | Ron Kohavi, Dan Sommerfield |
| 1997 | ICML | Option Decision Trees with Majority Votes. | Ron Kohavi, Clayton Kunz |
| 1997 | KDD | MineSet: An Integrated System for Data Mining. | Clifford Brunk, James Kelly, Ron Kohavi |
| 1996 | AAAI | Lazy Decision Trees. | Jerome H. Friedman, Ron Kohavi, Yeogirl Yun |
| 1996 | ICML | Bias Plus Variance Decomposition for Zero-One Loss Functions. | Ron Kohavi, David H. Wolpert |
| 1996 | ICTAI | Data Mining Using MLC++: A Machine Learning Library in C++. | Ron Kohavi, Dan Sommerfield, James Dougherty |
| 1996 | KDD | Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. | Ron Kohavi |
| 1996 | KDD | Error-Based and Entropy-Based Discretization of Continuous Features. | Ron Kohavi, Mehran Sahami |
| 1995 | ICML | Supervised and Unsupervised Discretization of Continuous Features. | James Dougherty, Ron Kohavi, Mehran Sahami |
| 1995 | ICML | Automatic Parameter Selection by Minimizing Estimated Error. | Ron Kohavi, George H. John |
| 1995 | IJCAI | A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. | Ron Kohavi |
| 1995 | IJCAI | Oblivious Decision Trees, Graphs, and Top-Down Pruning. | Ron Kohavi, Chia-Hsin Li |
| 1995 | KDD | Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology. | Ron Kohavi, Dan Sommerfield |
| 1994 | AAAI | Bottom-Up Induction of Oblivious Read-Once Decision Graphs: Strengths and Limitations. | Ron Kohavi |
| 1994 | ICML | Irrelevant Features and the Subset Selection Problem. | George H. John, Ron Kohavi, Karl Pfleger |
| 1994 | ICTAI | MLC++: A Machine Learning Library in C++. | Ron Kohavi, George H. John, Richard Long, David Manley, Karl Pfleger |