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Thorsten Joachims

Thorsten Joachims

D-Index & Metrics

Computer Science

D-Index
81
Citations
86206
World Ranking
985
National Ranking
529

Research.com Recognitions

  • 2015 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the theory and practice of machine learning and information retrieval
  • 2014 - ACM Fellow For contributions to the theory and practice of machine learning and information retrieval.

Overview

Thorsten Joachims is affiliated with Cornell University in the United States and has contributed extensively to the fields of computer science and decision sciences. Their research intersects various subfields including management science and operations research, artificial intelligence, economics and econometrics, statistics and probability, and computer science applications.

Their work covers a range of topics such as advanced bandit algorithms research, game theory and voting systems, auction theory and applications, topic modeling, recommender systems and techniques, and smart grid energy management.

Joachims has authored numerous papers published in several prominent venues. Notable recent publications include:

  • MOReL: Model-Based Offline Reinforcement Learning (2020), arXiv (Cornell University)
  • Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking (2022), Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • Large language models, social demography, and hegemony: comparing authorship in human and synthetic text (2024), Journal Of Big Data
  • Optimizing Rankings for Recommendation in Matching Markets (2022), Proceedings of the ACM Web Conference 2022
  • Recommendations as treatments (2021), AI Magazine

Joachims frequently collaborates with other researchers including René F. Kizilcec, Joyce Zhou, Jinsook Lee, Yuta Saito, and Lequn Wang.

Their publications are regularly featured in venues such as:

  • arXiv (Cornell University)
  • Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • Zenodo (CERN European Organization for Nuclear Research)
  • Journal Of Big Data
  • Proceedings of the ACM Web Conference 2022

Thorsten Joachims has been recognized by professional organizations with awards including:

  • Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 2015 for contributions to the theory and practice of machine learning and information retrieval
  • ACM Fellow in 2014 for contributions to the theory and practice of machine learning and information retrieval

Best Publications

  • Text Categorization with Suport Vector Machines: Learning with Many Relevant Features

    Thorsten Joachims

  • Making large-scale SVM learning practical

    Thorsten Joachims

  • Making large scale SVM learning practical

    Thorsten Joachims

  • Optimizing search engines using clickthrough data

    Thorsten Joachims

  • Transductive Inference for Text Classification using Support Vector Machines

    Thorsten Joachims

  • Large Margin Methods for Structured and Interdependent Output Variables

    Ioannis Tsochantaridis;Thorsten Joachims;Thomas Hofmann;Yasemin Altun

  • Training linear SVMs in linear time

    Thorsten Joachims

  • A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization

    Thorsten Joachims

  • Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms

    Thorsten Joachims

  • Making large-scale support vector machine learning practical

    Thorsten Joachims

  • Accurately interpreting clickthrough data as implicit feedback

    Thorsten Joachims;Laura Granka;Bing Pan;Helene Hembrooke

  • Support vector machine learning for interdependent and structured output spaces

    Ioannis Tsochantaridis;Thomas Hofmann;Thorsten Joachims;Yasemin Altun

  • Cutting-plane training of structural SVMs

    Thorsten Joachims;Thomas Finley;Chun-Nam John Yu

  • Learning to Classify Text Using Support Vector Machines

    Thorsten Joachims

  • Web Watcher: A Tour Guide for the World Wide Web.

    Thorsten Joachims;Dayne Freitag;Tom M. Mitchell

  • A support vector method for multivariate performance measures

    Thorsten Joachims

  • Eye-tracking analysis of user behavior in WWW search

    Laura A. Granka;Thorsten Joachims

  • WebWatcher : A Learning Apprentice for the World Wide Web

    Robert Armstrong;Dayne Freitag;Thorsten Joachims;Tom Mitchell

  • In Google We Trust: Users’ Decisions on Rank, Position, and Relevance

    Bing Pan;Helene Hembrooke;Thorsten Joachims;Lori Lorigo

  • A support vector method for optimizing average precision

    Yisong Yue;Thomas Finley;Filip Radlinski;Thorsten Joachims

  • Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

    Longbing Cao;Chengqi Zhang;Thorsten Joachims;Geoff Webb

Frequent Co-Authors

Filip Radlinski
Filip Radlinski Google (United States)
Yisong Yue
Yisong Yue California Institute of Technology
Ashutosh Saxena
Ashutosh Saxena Cornell University
Paul N. Bennett
Paul N. Bennett Microsoft (United States)
Susan T. Dumais
Susan T. Dumais Microsoft (United States)
Longbing Cao
Longbing Cao University of Technology Sydney
Tom M. Mitchell
Tom M. Mitchell Carnegie Mellon University
Thomas Hofmann
Thomas Hofmann ETH Zurich
Carmel Domshlak
Carmel Domshlak Technion – Israel Institute of Technology
Robert Kleinberg
Robert Kleinberg Cornell University

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