World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
59227
World Ranking
2506
National Ranking
1249

Overview

Ron Kohavi is affiliated with Microsoft in the United States and specializes in several areas within mathematics, decision sciences, and computer science. Their research spans subfields such as statistics and probability, artificial intelligence, statistics, probability and uncertainty, management science and operations research, and information systems and management.

The scientist's main research topics include:

  • Statistical Methods in Clinical Trials
  • Advanced Causal Inference Techniques
  • Statistical Methods and Inference
  • Scientific Computing and Data Management
  • Advanced Statistical Process Monitoring
  • Big Data and Business Intelligence
  • Statistical Methods and Bayesian Inference

Kohavi has coauthored extensively with colleagues such as Diane Tang and Ya Xu, each with 32 joint publications, followed by Alex Deng with 3, and Nicholas Larsen and Jonathan W. Stallrich with 2 coauthored works each.

Their recent publications include:

  • "Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology" (2023) published in The American Statistician
  • "Online randomized controlled experiments at scale: lessons and extensions to medicine" (2020) published in Trials
  • "A/B Testing Intuition Busters" (2022) published in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • "Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology" (2022) published in arXiv (Cornell University)
  • "Index" (2020) published by Cambridge University Press eBooks

Among their book publications, Kohavi has contributed to Cambridge University Press with the title "Trustworthy Online Controlled Experiments" (2020), which has accrued significant citations.

Frequent venues where this scientist publishes include The American Statistician, Trials, Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, arXiv, and Cambridge University Press eBooks.

Best Publications

  • A study of cross-validation and bootstrap for accuracy estimation and model selection

    Ron Kohavi

  • Wrappers for feature subset selection

    Ron Kohavi;George H. John

  • Irrelevant features and the subset selection problem

    George H. John;Ron Kohavi;Karl Pfleger

  • Supervised and unsupervised discretization of continuous features

    James Dougherty;Ron Kohavi;Mehran Sahami

  • E-Commerce Recommendation Applications

    J. Ben Schafer;Joseph A. Konstan;John Riedl

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

    Eric Bauer;Ron Kohavi

  • Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid

    Ron Kohavi

  • The Case against Accuracy Estimation for Comparing Induction Algorithms

    Foster J. Provost;Tom Fawcett;Ron Kohavi

  • The power of decision tables

    Ron Kohavi

  • Bias plus variance decomposition for zero-one loss functions

    Ron Kohavi;David Wolpert

  • Controlled experiments on the web: survey and practical guide

    Ron Kohavi;Roger Longbotham;Dan Sommerfield;Randal M. Henne

  • Real world performance of association rule algorithms

    Zijian Zheng;Ron Kohavi;Llew Mason

  • Data Mining Using MLC a Machine Learning Library in C

    Ron Kohavi;Dan Sommerfield;James Dougherty

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

    Ron Kohavi;Randal M. Henne;Dan Sommerfield

  • Feature subset selection using the wrapper method: overfltting and dynamic search space topology

    Ron Kohavi;Dan Sommerfield

  • Emerging trends in business analytics

    Ron Kohavi;Neal J. Rothleder;Evangelos Simoudis

  • Wrappers for Performance Enhancements and Oblivious Decision Graphs.

    Ron Kohavi

  • Error-based and entropy-based discretization of continuous features

    Ron Kohavi;Mehran Sahami

  • Online controlled experiments at large scale

    Ron Kohavi;Alex Deng;Brian Frasca;Toby Walker

  • Guest Editors‘ Introduction: On Applied Research in MachineLearning

    Foster Provost;Ron Kohavi

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

    R. Kohavi;D. Sommerfield;J. Dougherty

  • Feature Selection for Knowledge Discovery and Data Mining

    Ron Kohavi;George John

Frequent Co-Authors

Foster Provost
Foster Provost New York University
Jaideep Srivastava
Jaideep Srivastava University of Minnesota
Myra Spiliopoulou
Myra Spiliopoulou Otto-von-Guericke University Magdeburg
Carla E. Brodley
Carla E. Brodley Northeastern University
Mehran Sahami
Mehran Sahami Stanford University
Johannes Gehrke
Johannes Gehrke Microsoft (United States)
William DuMouchel
William DuMouchel Oracle (United States)
Pat Langley
Pat Langley Stanford University
David H. Wolpert
David H. Wolpert Santa Fe Institute
Nir Friedman
Nir Friedman Weizmann Institute of Science

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