T. Joachims

发表

Interactively optimizing information retrieval systems as a dueling bandits problem

Thorsten Joachims, Yisong Yue, Yisong Yue, 2009, ICML '09.

Learning diverse rankings with multi-armed bandits

Filip Radlinski, Thorsten Joachims, Robert D. Kleinberg, 2008, ICML '08.

WebWatcher : A Tour Guide for the World Wide Web

T. Joachims, 1997 .

Large Margin Methods for Structured and Interdependent Output Variables

Thomas Hofmann, Thorsten Joachims, Yasemin Altun, 2005, J. Mach. Learn. Res..

Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs

Filip Radlinski, Thorsten Joachims, T. Joachims, 2006, AAAI 2006.

Transductive Inference for Text Classification using Support Vector Machines

Thorsten Joachims, T. Joachims, 1999, ICML.

Text Categorization with Support Vector Machines: Learning with Many Relevant Features

Thorsten Joachims, T. Joachims, 1998, ECML.

Learning a Distance Metric from Relative Comparisons

Thorsten Joachims, Matthew Schultz, T. Joachims, 2003, NIPS.

Training linear SVMs in linear time

Thorsten Joachims, T. Joachims, 2006, KDD '06.

Unstructuring User Preferences: Efficient Non-Parametric Utility Revelation

Carmel Domshlak, Thorsten Joachims, T. Joachims, 2005, UAI.

Making large scale SVM learning practical

Thorsten Joachims, T. Joachims, 1998 .

A support vector method for multivariate performance measures

Thorsten Joachims, T. Joachims, 2005, ICML.

Making large-scale support vector machine learning practical

Thorsten Joachims, T. Joachims, C. Burges, 1999 .

Learning structural SVMs with latent variables

Thorsten Joachims, Chun-Nam John Yu, T. Joachims, 2009, ICML '09.

Support vector machine learning for interdependent and structured output spaces

Thomas Hofmann, Thorsten Joachims, Yasemin Altun, 2004, ICML.

Redundancy, diversity and interdependent document relevance

Filip Radlinski, Ben Carterette, Thorsten Joachims, 2009, SIGF.

KDD-Cup 2004: results and analysis

Thorsten Joachims, Rich Caruana, Lars Backstrom, 2004, SKDD.

Text Categorization with Support Vector Machines: Learning with Many Relevant Features

T. Joachims, AT Dortmund, 1999, ECML.

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

Thorsten Joachims, T. Joachims, 1997, ICML.

WebWatcher : A Learning Apprentice for the World Wide Web

Thorsten Joachims, Tom M. Mitchell, Dayne Freitag, 1995 .

Text categorization with support vector machines

Thorsten Joachims, T. Joachims, 1999 .

Support Vector Machines

T. Joachims, 2002 .

Transductive Learning via Spectral Graph Partitioning

Thorsten Joachims, T. Joachims, 2003, ICML.

Cutting-plane training of structural SVMs

Thorsten Joachims, Thomas Finley, Chun-Nam John Yu, 2009, Machine Learning.

Training Protein Threading Models using Structural SVMs

T. Joachims, C. Yu, 2006 .

1 Making Large-scale Svm Learning Practical

T. Joachims, C. Burges, B. Scholkopf, 1999 .

Counterfactual Risk Minimization: Learning from Logged Bandit Feedback

Thorsten Joachims, Adith Swaminathan, T. Joachims, 2015, ICML.

Taste Over Time: The Temporal Dynamics of User Preferences

Thorsten Joachims, Shuo Chen, Douglas Turnbull, 2013, ISMIR.

Eye tracking and online search: Lessons learned and challenges ahead

Ling Xia, Thorsten Joachims, Fabio Pellacini, 2008, J. Assoc. Inf. Sci. Technol..

Semantic Labeling of 3D Point Clouds for Indoor Scenes

Thorsten Joachims, Hema Swetha Koppula, Ashutosh Saxena, 2011, NIPS.

Learning to Align Sequences: A Maximum-Margin Approach

Thorsten Joachims, Ron Elber, Tamara Galor, 2006 .

The influence of task and gender on search and evaluation behavior using Google

Thorsten Joachims, Bing Pan, Geri Gay, 2006, Inf. Process. Manag..

A support vector method for optimizing average precision

Filip Radlinski, Thorsten Joachims, Yisong Yue, 2007, SIGIR.

Learning to classify text using support vector machines - methods, theory and algorithms

Thorsten Joachims, T. Joachims, 2002, The Kluwer international series in engineering and computer science.

Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring

Katharina Morik, Thorsten Joachims, Peter Brockhausen, 1999, ICML.

Supervised clustering with support vector machines

Thorsten Joachims, Thomas Finley, T. Joachims, 2005, ICML.

Detecting Concept Drift with Support Vector Machines

Thorsten Joachims, Ralf Klinkenberg, T. Joachims, 2000, ICML.

Learning to Diversify from Implicit Feedback

T. Joachims, Karthik Raman, 2012 .

Unbounded Human Learning: Optimal Scheduling for Spaced Repetition

Thorsten Joachims, Igor Labutov, Siddharth Reddy, 2016, KDD.

Evaluating the Robustness of Learning from Implicit Feedback pdf

Filip Radlinski, Thorsten Joachims, T. Joachims, 2006, ArXiv.

Composite Kernels for Hypertext Categorisation

Nello Cristianini, Thorsten Joachims, John Shawe-Taylor, 2001, ICML.

Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory

Thorsten Joachims, Paul N. Bennett, Tobias Schnabel, 2019, WSDM.

Web Watcher: A Tour Guide for the World Wide Web

Thorsten Joachims, Tom M. Mitchell, Dayne Freitag, 1997, IJCAI.

Sparse kernel SVMs via cutting-plane training

Thorsten Joachims, Chun-Nam John Yu, T. Joachims, 2009, Machine Learning.

Latent Skill Embedding for Personalized Lesson Sequence Recommendation pdf

Thorsten Joachims, Igor Labutov, Siddharth Reddy, 2016, ArXiv.

Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel

Johannes Fürnkranz, Thorsten Joachims, T. Joachims, 2010, ICML.

The K-armed Dueling Bandits Problem

Thorsten Joachims, Yisong Yue, Robert D. Kleinberg, 2012, COLT.

Training structural svms with kernels using sampled cuts

Thorsten Joachims, Chun-Nam John Yu, T. Joachims, 2008, KDD.

Policy Learning for Fairness in Ranking

Thorsten Joachims, Ashudeep Singh, T. Joachims, 2019, NeurIPS.

Large-Margin Learning of Submodular Summarization Methods pdf

Thorsten Joachims, Pannagadatta K. Shivaswamy, Ruben Sipos, 2011, ArXiv.

SVM Light: Support Vector Machine

Thorsten Joachims, T. Joachims, 2002 .

Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers

Thorsten Joachims, Aman Agarwal, Tobias Schnabel, 2017, KDD.

Text categorization

Yiming Yang, Thorsten Joachims, T. Joachims, 2008, Scholarpedia.

Eye tracking and online search: Lessons learned and challenges ahead

Thorsten Joachims, Bing Pan, Fabio Pellacini, 2008 .

Error bounds for correlation clustering

Thorsten Joachims, John E. Hopcroft, J. Hopcroft, 2005, ICML.

Error Bounds for Correlation Clustering (DRAFT)

T. Joachims, 2005 .

Learning to rank for information retrieval (LR4IR 2007)

Tie-Yan Liu, Thorsten Joachims, Hang Li, 2007, SIGF.

Identifying Temporal Patterns and Key Players in Document Collections

Thorsten Joachims, Johannes Gehrke, Rich Caruana, 1995 .

The logistic re-? ! ? ! ? ! ? ! review !

T. Joachims, S. Reddy, I. Labutov, 2018 .

A Queueing-Theoretic Foundation for Optimal Spaced Repetition

T. Joachims, S. Reddy, I. Labutov, 2016 .

A Tour Guide for the World Wide Web

T. Joachims, 1997 .

MOReL : Model-Based Offline Reinforcement Learning

T. Joachims, Praneeth Netrapalli, A. Rajeswaran, 2020, NeurIPS.

C OSTS ENSITIVE L EARNING VIA D EEP P OLICY ERM

Christopher De Sa, T. Joachims, Lequn Wang, 2018 .

Accurately Interpreting Clickthrough Data as Implicit Feedback

Laura A. Granka, T. Joachims, Geri Gay, 2017 .

Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion

Daniel N. Hill, T. Joachims, I. Dhillon, 2022, ArXiv.

Fairness in the First Stage of Two-Stage Recommender Systems

T. Joachims, Lequn Wang, 2022, ArXiv.

Ofﬂine Policy Evaluation with New Arms

T. Joachims, Ben London, 2020 .

KDD'15: Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Geoffrey I. Webb, Chengqi Zhang, Longbing Cao, 2015 .

Fairness of Exposure in Stochastic Bandits

Thorsten Joachims, Wen Sun, Lequn Wang, 2021, ICML.

Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking

T. Joachims, Yuta Saito, 2022, ArXiv.

Erratum to "Knowledge discovery and knowledge validation in intensive care"

Katharina Morik, Thorsten Joachims, Ursula Gather, 2000, Artif. Intell. Medicine.

A preference elicitation interface for collecting rich recommender datasets

Thorsten Joachims, Stefan M. Herzog, Daniel Barkoczi, 2017 .

Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent Advances

Thorsten Joachims, Yuta Saito, T. Joachims, 2021, RecSys.

A Statistical Learning Model of Text Classification for Support Vector Machines.

Thorsten Joachims, T. Joachims, 2001, SIGIR 2002.

Proceedings, Twenty-Seventh International Conference on Machine Learning

Johannes Fürnkranz, Thorsten Joachims, T. Joachims, 2010 .

Sparse kernel SVMs via cutting-plane training

Thorsten Joachims, Chun-Nam John Yu, T. Joachims, 2009, Machine-mediated learning.

Evaluating Search Engines using Clickthrough Data

Thorsten Joachims, T. Joachims, 2002 .

Cost-Sensitive Learning via Deep Policy ERM

Christopher De Sa, T. Joachims, Lequn Wang, 2018 .

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

T. Joachims, 2005 .

Off-Policy Evaluation for Large Action Spaces via Embeddings

T. Joachims, Yuta Saito, 2022, ICML.

Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking

T. Joachims, Yuta Saito, 2022, KDD.

Boosted Off-Policy Learning

T. Joachims, Ben London, Levi Lu, 2022, 2208.01148.

Comparison between the regression depth method and the support vector machine to approximate the minimum number of misclassifications

T. Joachims, A. Christmann, P. Fischer, 2000 .

Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes

Thorsten Joachims, Kristian Kersting, Zhao Xu, 2010, ECML/PKDD.

The Self-Normalized Estimator for Counterfactual Learning

Thorsten Joachims, Adith Swaminathan, T. Joachims, 2015, NIPS.

A General Framework for Counterfactual Learning-to-Rank

Thorsten Joachims, Aman Agarwal, Ivan Zaitsev, 2018, SIGIR.

Counterfactual Learning-to-Rank for Additive Metrics and Deep Models pdf

Thorsten Joachims, Aman Agarwal, Ivan Zaitsev, 2018, ArXiv.

Comparison between various regression depth methods and the support vector machine to approximate the minimum number of missclassifications

Thorsten Joachims, Andreas Christmann, Paul Fischer, 2002, Comput. Stat..

Classification Based on the Support Vector Machine, Regression Depth, and Discriminant Analysis

Thorsten Joachims, Andreas Christmann, Paul Fischer, 2002, COMPSTAT.

Evaluation methods for unsupervised word embeddings

Thorsten Joachims, Igor Labutov, David M. Mimno, 2015, EMNLP.

Optimizing search engines using clickthrough data

Thorsten Joachims, T. Joachims, 2002, KDD.

Training structural SVMs when exact inference is intractable

Thorsten Joachims, Thomas Finley, T. Joachims, 2008, ICML '08.

Estimating the Generalization Performance of an SVM Efficiently

Thorsten Joachims, T. Joachims, 2000, ICML.

Support Vector Training of Protein Alignment Models

Thorsten Joachims, Ron Elber, Chun-Nam John Yu, 2007, RECOMB.

Policy-Gradient Training of Fair and Unbiased Ranking Functions

Thorsten Joachims, Himank Yadav, Zhengxiao Du, 2019, SIGIR.

Fair Learning-to-Rank from Implicit Feedback pdf

Thorsten Joachims, Himank Yadav, Zhengxiao Du, 2019, ArXiv.

Predicting Matchups and Preferences in Context

Thorsten Joachims, Shuo Chen, T. Joachims, 2016, KDD.

Modeling Intransitivity in Matchup and Comparison Data

Thorsten Joachims, Shuo Chen, T. Joachims, 2016, WSDM.

Methods for ordinal peer grading

Thorsten Joachims, Karthik Raman, T. Joachims, 2014, KDD.

Supervised k-Means Clustering

Thorsten Joachims, Thomas Finley, T. Joachims, 2008 .

Learning Student and Content Embeddings for Personalized Lesson Sequence Recommendation

Thorsten Joachims, Igor Labutov, Siddharth Reddy, 2016, L@S.

Learning Representations of Student Knowledge and Educational Content

Thorsten Joachims, Igor Labutov, Siddharth Reddy, 2015 .

Efficient and non-parametric reasoning over user preferences

Carmel Domshlak, Thorsten Joachims, T. Joachims, 2007, User Modeling and User-Adapted Interaction.

Active exploration for learning rankings from clickthrough data

Filip Radlinski, Thorsten Joachims, T. Joachims, 2007, KDD '07.

Knowledge discovery and knowledge validation in intensive care

Katharina Morik, Thorsten Joachims, Ursula Gather, 2000, Artif. Intell. Medicine.

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

Thorsten Joachims, Bing Pan, Geri Gay, 2007, J. Comput. Mediat. Commun..

Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement

Thorsten Joachims, Adith Swaminathan, T. Joachims, 2016, SIGIR.

Controlling Fairness and Bias in Dynamic Learning-to-Rank

Thorsten Joachims, Ashudeep Singh, Jessica Hong, 2020, SIGIR.

Intervention Harvesting for Context-Dependent Examination-Bias Estimation

Thorsten Joachims, Aman Agarwal, Zhichong Fang, 2018, SIGIR.

Fairness of Exposure in Rankings

Thorsten Joachims, Ashudeep Singh, T. Joachims, 2018, KDD.

How does clickthrough data reflect retrieval quality?

Filip Radlinski, Thorsten Joachims, Madhu Kurup, 2008, CIKM '08.

User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets

Thorsten Joachims, Lequn Wang, T. Joachims, 2020, ICTIR.

Optimizing Rankings for Recommendation in Matching Markets pdf

Thorsten Joachims, Yi Su, Magd Bayoumi, 2021, WWW.

Contextually Guided Semantic Labeling and Search for 3D Point Clouds pdf

Thorsten Joachims, Hema Swetha Koppula, Ashutosh Saxena, 2011, ArXiv.

Recommendations as Treatments: Debiasing Learning and Evaluation

Thorsten Joachims, Ashudeep Singh, Adith Swaminathan, 2016, ICML.

Using Shortlists to Support Decision Making and Improve Recommender System Performance

Susan T. Dumais, Thorsten Joachims, Paul N. Bennett, 2015, WWW.

Accurately interpreting clickthrough data as implicit feedback

Thorsten Joachims, Bing Pan, Geri Gay, 2005, SIGIR '05.

Exploring Acoustic Similarity for Novel Music Recommendation

T. Joachims, D. Turnbull, D. Cheng, 2020, ISMIR.

Learning preferences for manipulation tasks from online coactive feedback

Thorsten Joachims, Ashutosh Saxena, Shikhar Sharma, 2015, Int. J. Robotics Res..

Counterfactual Evaluation and Learning for Interactive Systems: Foundations, Implementations, and Recent Advances

T. Joachims, Yuta Saito, 2022, KDD.

Unbiased Learning-to-Rank with Biased Feedback

Thorsten Joachims, Adith Swaminathan, Tobias Schnabel, 2016, WSDM.

Large-scale Validation of Counterfactual Learning Methods: A Test-Bed pdf

M. de Rijke, Maarten de Rijke, Thorsten Joachims, 2016, ArXiv.

Large-scale validation and analysis of interleaved search evaluation

Filip Radlinski, Thorsten Joachims, Yisong Yue, 2012, TOIS.

Unbiased Comparative Evaluation of Ranking Functions

Thorsten Joachims, Peter I. Frazier, Adith Swaminathan, 2016, ICTIR.

Evaluating Retrieval Performance Using Clickthrough Data

Thorsten Joachims, T. Joachims, 2003, Text Mining.

Unbiased Evaluation of Retrieval Quality using Clickthrough Data

Thorsten Joachims, T. Joachims, 2002 .

Batch learning from logged bandit feedback through counterfactual risk minimization

Thorsten Joachims, Adith Swaminathan, T. Joachims, 2015, J. Mach. Learn. Res..

Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling

T. Joachims, Yuta Saito, Qingyang Ren, 2023, 2305.08062.

Improving Recommender Systems Beyond the Algorithm pdf

Thorsten Joachims, Paul N. Bennett, Tobias Schnabel, 2018, ArXiv.

Bayesian Ordinal Aggregation of Peer Assessments: A Case Study on KDD 2015

Thorsten Joachims, Karthik Raman, T. Joachims, 2016, Solving Large Scale Learning Tasks.

Reducing Dueling Bandits to Cardinal Bandits pdf

Thorsten Joachims, Nir Ailon, Zohar S. Karnin, 2014, ICML.

Enriching information retrieval

Thorsten Joachims, Paul N. Bennett, Khalid El-Arini, 2012, SIGF.

Integrating Kernel Methods into a Knowledge-Based Approach to Evidence-Based Medicine

Katharina Morik, Thorsten Joachims, Stefan Rüping, 2002 .

Estimating Position Bias without Intrusive Interventions

Thorsten Joachims, Marc Najork, Aman Agarwal, 2018, WSDM.

Learning Socially Optimal Information Systems from Egoistic Users

Thorsten Joachims, Karthik Raman, T. Joachims, 2013, ECML/PKDD.

Beat the Mean Bandit

Thorsten Joachims, Yisong Yue, Yisong Yue, 2011, ICML.

Dynamic ranked retrieval

Thorsten Joachims, Yisong Yue, Christina Brandt, 2011, WSDM '11.

Learning more powerful test statistics for click-based retrieval evaluation

Yue Gao, Thorsten Joachims, Ya Zhang, 2010, SIGIR.

Predicting diverse subsets using structural SVMs

Thorsten Joachims, Yisong Yue, Yisong Yue, 2008, ICML '08.

Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search

Filip Radlinski, Thorsten Joachims, Bing Pan, 2007, TOIS.

Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling

T. Joachims, Yuta Saito, Qingyang Ren, 2023, ICML.

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

Chengqi Zhang, Longbing Cao, Thorsten Joachims, 2015, KDD.

Bayesian Ordinal Peer Grading

Thorsten Joachims, Karthik Raman, T. Joachims, 2015, L@S.

Multi-armed Bandit Problems with History

Thorsten Joachims, Pannagadatta K. Shivaswamy, T. Joachims, 2012, AISTATS.

Query chains: learning to rank from implicit feedback pdf

Filip Radlinski, Thorsten Joachims, T. Joachims, 2005, KDD '05.

Contextually guided semantic labeling and search for three-dimensional point clouds

Thorsten Joachims, Hema Swetha Koppula, Ashutosh Saxena, 2013, Int. J. Robotics Res..

Mapping preferences into Euclidean space

Thorsten Joachims, Oscar Luaces, Jorge Díez, 2015, Expert Syst. Appl..

Uncertainty Quantification for Fairness in Two-Stage Recommender Systems

T. Joachims, Lequn Wang, 2022, WSDM.

Improving Screening Processes via Calibrated Subset Selection

T. Joachims, Lequn Wang, Manuel Gomez Rodriguez, 2022, ICML.

CAB: Continuous Adaptive Blending for Policy Evaluation and Learning

Yi Su, Thorsten Joachims, Lequn Wang, 2019, ICML.

CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning pdf

Yi Su, Thorsten Joachims, Lequn Wang, 2018, ArXiv.

Beyond myopic inference in big data pipelines

Thorsten Joachims, Johannes Gehrke, Adith Swaminathan, 2013, KDD.

Fairness in the First Stage of Two-Stage Recommender Systems

T. Joachims, Lequn Wang, 2022, ArXiv.

Using personalized radio to enhance local music discovery

Thorsten Joachims, Douglas Turnbull, Alexander J. Wolf, 2014, CHI Extended Abstracts.

Multi-space probabilistic sequence modeling

Thorsten Joachims, Shuo Chen, Jiexun Xu, 2013, KDD.

Playlist prediction via metric embedding

Thorsten Joachims, Shuo Chen, Douglas Turnbull, 2012, KDD.

Taste Space Versus the World: an Embedding Analysis of Listening Habits and Geography

Thorsten Joachims, Douglas Turnbull, Joshua L. Moore, 2014, ISMIR.

Learning to Embed Songs and Tags for Playlist Prediction

Thorsten Joachims, Shuo Chen, Douglas Turnbull, 2012, ISMIR.

Search Engines that Learn from Implicit Feedback

Filip Radlinski, Thorsten Joachims, T. Joachims, 2007, Computer.

Transductive Support Vector Machines

Thorsten Joachims, T. Joachims, 2006, Semi-Supervised Learning.

Structured Learning of Sum-of-Submodular Higher Order Energy Functions

Thorsten Joachims, Sam Park, Ramin Zabih, 2013, 2013 IEEE International Conference on Computer Vision.

Large-Margin Learning of Submodular Summarization Models

Thorsten Joachims, Pannagadatta K. Shivaswamy, Ruben Sipos, 2012, EACL.

Off-policy Bandits with Deficient Support

Thorsten Joachims, Noveen Sachdeva, Yi Su, 2020, KDD.

Counterfactual Risk Minimization

Thorsten Joachims, Adith Swaminathan, T. Joachims, 2015, ICML.

Learning Trajectory Preferences for Manipulators via Iterative Improvement

Thorsten Joachims, Ashutosh Saxena, Ashesh Jain, 2013, NIPS.

How to Explain and Justify Almost Any Decision: Potential Pitfalls for Accountability in AI Decision-Making

T. Joachims, Joyce Zhou, 2023, FAccT.

Deep Learning with Logged Bandit Feedback

M. de Rijke, Maarten de Rijke, Thorsten Joachims, 2018, ICLR.

Boosted Off-Policy Learning

T. Joachims, Ben London, Levi Lu, 2022, AISTATS.

Recommendations as Treatments

Thorsten Joachims, Ben London, Adith Swaminathan, 2021, AI Mag..

Batch Learning from Bandit Feedback through Bias Corrected Reward Imputation

Thorsten Joachims, Lequn Wang, Yiwei Bai, 2019 .

Query chains: learning to rank from implicit feedback

T. Joachims, Filip Radlinski, 2005, KDD '05.

New Learning Frameworks for Information Retrieval

Thorsten Joachims, Yisong Yue, Yisong Yue, 2011 .

Evaluating Search Engine Relevance with Click-Based Metrics

Filip Radlinski, Thorsten Joachims, Madhu Kurup, 2010, Preference Learning.

Eye-tracking analysis of user behavior in WWW search

Thorsten Joachims, Geri Gay, Laura A. Granka, 2004, SIGIR '04.

Guest Editors' Introduction to the Special Issue on Automated Text Categorization

Thorsten Joachims, Fabrizio Sebastiani, T. Joachims, 2002, Journal of Intelligent Information Systems.

Accurately Interpreting Clickthrough Data as Implicit Feedback

Thorsten Joachims, Bing Pan, Geri Gay, 2017 .

CONSEQUENCES — Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems

T. Joachims, Yuta Saito, Flavian Vasile, 2022, RecSys.

Online Structured Prediction via Coactive Learning pdf

Thorsten Joachims, Pannagadatta K. Shivaswamy, T. Joachims, 2012, ICML.

The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation

Peter Bailey, Thorsten Joachims, Paul N. Bennett, 2020, SIGIR.

Structured Output Prediction with Support Vector Machines

Thorsten Joachims, T. Joachims, 2006, SSPR/SPR.

Fairness in Ranking under Uncertainty pdf

Thorsten Joachims, David Kempe, Ashudeep Singh, 2021, NeurIPS.

Fairness and Diversity for Rankings in Two-Sided Markets pdf

Thorsten Joachims, Lequn Wang, T. Joachims, 2020, ArXiv.

Information genealogy: uncovering the flow of ideas in non-hyperlinked document databases

Thorsten Joachims, Benyah Shaparenko, T. Joachims, 2007, KDD '07.

Content-based Music Similarity with Triplet Networks

Michael X. Zhou, T. Joachims, D. Turnbull, 2020, ArXiv.

Stable Coactive Learning via Perturbation

Thorsten Joachims, Karthik Raman, Pannagadatta K. Shivaswamy, 2013, ICML.

Identifying the original contribution of a document via language modeling

Thorsten Joachims, Benyah Shaparenko, T. Joachims, 2009, ECML/PKDD.

SIGIR 2003 workshop report: implicit measures of user interests and preferences

Susan T. Dumais, Thorsten Joachims, Krishna Bharat, 2003, SIGF.

Deep Learning from Logged Interventions

Thorsten Joachims, T. Joachims, 2018, DLRS@RecSys.

Fairness and Control of Exposure in Two-sided Markets

Thorsten Joachims, T. Joachims, 2021, ICTIR.

Equality of Opportunity in Rankings

Thorsten Joachims, Ashudeep Singh, T. Joachims, 2017 .

The Maximum-Margin Approach to Learning Text Classifiers

Thorsten Joachims, T. Joachims, 2001, Künstliche Intell..

Ranking with Long-Term Constraints

T. Joachims, Kianté Brantley, Sarah Dean, 2023, ArXiv.

Learning to classify text using support vector machines - methods, theory and algorithms

T. Joachims, 2002, The Kluwer international series in engineering and computer science.

Generating comparative summaries from reviews

Thorsten Joachims, Ruben Sipos, T. Joachims, 2013, CIKM.

Predicting structured objects with support vector machines

Thomas Hofmann, Thorsten Joachims, Yisong Yue, 2009, Commun. ACM.

Mapping subsets of scholarly information

Thorsten Joachims, Paul Ginsparg, Paul Houle, 2003, Proceedings of the National Academy of Sciences of the United States of America.

Introduction to special issue on learning to rank for information retrieval

Tie-Yan Liu, Thorsten Joachims, Hang Li, 2010, Information Retrieval.

Learning a Distance Metric from Relative Comparisons

T. Joachims, Matthew Schultz, 2003, NIPS.

The enigmatics of affect

Kristina Höök, Thorsten Joachims, Phoebe Sengers, 2002, DIS '02.

Ranking with Social Cues: Integrating Online Review Scores and Popularity Information pdf

Thorsten Joachims, Pantelis P. Analytis, Mehdi Moussaïd, 2017, ICWSM.

Recommendations as Treatments: Debiasing Learning and Evaluation

T. Joachims, Adith Swaminathan, Tobias Schnabel, 2016, ICML.

A preference elicitation interface for collecting dense recommender datasets with rich user information pdf

Thorsten Joachims, Stefan M. Herzog, Stefan Herzog, 2017, ArXiv.

Variance-Minimizing Augmentation Logging for Counterfactual Evaluation in Contextual Bandits

T. Joachims, Aaron David Tucker, 2023, WSDM.

Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits

T. Joachims, Aaron David Tucker, 2022, ArXiv.

Online Learning with Preference Feedback pdf

Thorsten Joachims, Pannagadatta K. Shivaswamy, T. Joachims, 2011, ArXiv.

Coactive Learning

Thorsten Joachims, Pannagadatta K. Shivaswamy, T. Joachims, 2015, J. Artif. Intell. Res..

Was this review helpful to you?: it depends! context and voting patterns in online content

Thorsten Joachims, Arpita Ghosh, Ruben Sipos, 2014, WWW.

Beyond binary relevance: preferences, diversity, and set-level judgments

Ben Carterette, Thorsten Joachims, Paul N. Bennett, 2008, SIGF.

Ranking with Long-Term Constraints

T. Joachims, Kianté Brantley, Sarah Dean, 2023, WSDM.

Knowledge discovery and knowledge validation in intensive care

T. Joachims, K. Morik, U. Gather, 2000, Artif. Intell. Medicine.

Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration

Susan T. Dumais, Thorsten Joachims, Paul N. Bennett, 2018, WSDM.

Variance-Minimizing Augmentation Logging for Counterfactual Evaluation in Contextual Bandits

T. Joachims, Aaron David Tucker, 2023, WSDM.

Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers

T. Joachims, S. Basu, Tobias Schnabel, 2017, KDD.

Batch learning from logged bandit feedback through counterfactual risk minimization

T. Joachims, Adith Swaminathan, 2015, J. Mach. Learn. Res..

Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions

René F. Kizilcec, T. Joachims, Hansol Lee, 2023, L@S.

A statistical learning learning model of text classification for support vector machines

Thorsten Joachims, T. Joachims, 2001, SIGIR '01.

KDD-Cup 2004: results and analysis

T. Joachims, R. Caruana, L. Backstrom, 2004, SKDD.