Thorsten Joachims

发表

Making large scale SVM learning practical

Thorsten Joachims, T. Joachims, 1998 .

The enigmatics of affect

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

Active exploration for learning rankings from clickthrough data

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

Estimating Position Bias without Intrusive Interventions

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

Learning Student and Content Embeddings for Personalized Lesson Sequence Recommendation

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

Semantic Labeling of 3D Point Clouds for Indoor Scenes

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

Einsatz eines intelligenten, lernenden Agenten für das World Wide Web

Thorsten Joachims, Thorsten Joachims, 2003 .

Predicting structured objects with support vector machines

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

Training structural SVMs when exact inference is intractable

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

Interactively optimizing information retrieval systems as a dueling bandits problem

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

Analysis of nutrition data by means of a matrix factorization method

Thorsten Joachims, Oscar Luaces, Jorge Díez, 2015, Progress in Artificial Intelligence.

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

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

Transductive Support Vector Machines

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

Large-Margin Learning of Submodular Summarization Models

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

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..

Text categorization

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

New Learning Frameworks for Information Retrieval

Thorsten Joachims, Yisong Yue, Yisong Yue, 2011 .

The Self-Normalized Estimator for Counterfactual Learning

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

Unbiased Learning-to-Rank with Biased Feedback

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

Maschinelles Lernen und Data Mining

Katharina Morik, Thorsten Joachims, Stefan Wrobel, 2013, Handbuch der Künstlichen Intelligenz.

Expected Error Analysis for Model Selection

Thorsten Joachims, Tobias Scheffer, 1999, ICML.

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

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

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

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

Predicting Matchups and Preferences in Context

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

The Value of User Feedback

Thorsten Joachims, 2011, ECIR.

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

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

Online Learning with Preference Feedback pdf

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

Query chains: learning to rank from implicit feedback pdf

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

Eye tracking and online search: Lessons learned and challenges ahead

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

Consistent Position Bias Estimation without Online Interventions for Learning-to-Rank pdf

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

Learning more powerful test statistics for click-based retrieval evaluation

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

Training Inductive Support Vector Machines

Thorsten Joachims, 2002 .

Identifying Structural Mapping between XML Fragments

Thorsten Joachims, Aseem Bajaj, 2003 .

Content-based Music Similarity with Triplet Networks pdf

Thorsten Joachims, Michael Zhou, Joseph Cleveland, 2020, ArXiv.

Learning Representations of Student Knowledge and Educational Content

Thorsten Joachims, Igor Labutov, Siddharth Reddy, 2015 .

A General Framework for Counterfactual Learning-to-Rank

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

Unbiased Ranking Evaluation on a Budget

Thorsten Joachims, Adith Swaminathan, Tobias Schnabel, 2015, WWW.

Playlist prediction via metric embedding

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

Predicting diverse subsets using structural SVMs

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

Text categorization with support vector machines

Thorsten Joachims, T. Joachims, 1999 .

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.

WebWatcher : A Learning Apprentice for the World Wide Web

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

A preference elicitation interface for collecting rich recommender datasets

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

Structured Output Prediction with Support Vector Machines

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

Structured learning of two-level dynamic rankings pdf

Thorsten Joachims, Karthik Raman, Pannagadatta K. Shivaswamy, 2011, CIKM '11.

Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers

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

Large Margin Methods for Structured and Interdependent Output Variables

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

Evaluating Search Engine Relevance with Click-Based Metrics

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

Evaluating Retrieval Performance Using Clickthrough Data

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

Learning to rank for information retrieval (LR4IR 2007)

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

Eye-tracking analysis of user behavior in WWW search

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

Learning to Align Sequences: A Maximum-Margin Approach

Thorsten Joachims, Ron Elber, Tamara Galor, 2006 .

Counterfactual Risk Minimization: Learning from Logged Bandit Feedback

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

Online learning to diversify from implicit feedback

Thorsten Joachims, Karthik Raman, Pannagadatta K. Shivaswamy, 2012, KDD.

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

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

Supervised clustering with support vector machines

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

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

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

The K-armed Dueling Bandits Problem

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

Redundancy, diversity and interdependent document relevance

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

Batch learning from logged bandit feedback through counterfactual risk minimization

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

Beat the Mean Bandit

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

Fairness and Diversity for Rankings in Two-Sided Markets pdf

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

The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation

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

Generating comparative summaries from reviews

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

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

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

Counterfactual Risk Minimization

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

Online Structured Prediction via Coactive Learning pdf

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

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

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

Learning structural SVMs with latent variables

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

Citation Classification And Its Applications

Thorsten Joachims, Carl Lagoze, Selcuk Aya, 2005 .

Search Engines that Learn from Implicit Feedback

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

CAB: Continuous Adaptive Blending for Policy Evaluation and Learning

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

Maschinelles Lernen und Data Mining

Katharina Morik, Thorsten Joachims, Stefan Wrobel, 2003, Handbuch der Künstlichen Intelligenz.

Transductive Inference for Text Classification using Support Vector Machines

Thorsten Joachims, T. Joachims, 1999, ICML.

REVEAL 2019: closing the loop with the real world: reinforcement and robust estimators for recommendation

Thorsten Joachims, Adith Swaminathan, Olivier Koch, 2019, RecSys.

Learning from User Interactions

Thorsten Joachims, 2015, WSDM.

Support vector machine learning for interdependent and structured output spaces

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

Learning Socially Optimal Information Systems from Egoistic Users

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

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

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

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

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

Unbounded Human Learning: Optimal Scheduling for Spaced Repetition

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

Taste Over Time: The Temporal Dynamics of User Preferences

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

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

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

Using Shortlists to Support Decision Making and Improve Recommender System Performance

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

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.

Deep Learning from Logged Interventions

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

WebWatcher: Knowledge Navigation in the World Wide Web

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

Training linear SVMs in linear time

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

Behavior Informatics: A New Perspective

Guandong Xu, Longbing Cao, Reza Zafarani, 2014, IEEE Intelligent Systems.

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.

Estimating the Expected Error of Empirical Minimizers for Model Selection

Thorsten Joachims, Tobias Scheffer, 1998, AAAI/IAAI.

Improving Recommender Systems Beyond the Algorithm pdf

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

Identifying the Original Contribution of a Document via Language Modeling

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

Making large-scale support vector machine learning practical

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

Extreme Classification (Dagstuhl Seminar 18291)

Thorsten Joachims, Samy Bengio, Marius Kloft, 2018, Dagstuhl Reports.

Learning to Embed Songs and Tags for Playlist Prediction

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

A Statistical Learning Model of Text Classification for SVMs

Thorsten Joachims, 2002 .

Filip Radlinski, Thorsten Joachims, Stefan Pohl, 2007, JCDL '07.

A support vector method for multivariate performance measures

Thorsten Joachims, T. Joachims, 2005, ICML.

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

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

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

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

Evaluating Search Engines using Clickthrough Data

Thorsten Joachims, T. Joachims, 2002 .

Identifying the original contribution of a document via language modeling

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

Training structural svms with kernels using sampled cuts

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

Browsing-Assistenten, Tour Guides und adaptive WWW-Server

Dunja Mladenic, Thorsten Joachims, D. Mladenic, 1998, Künstliche Intell..

Evaluating the Robustness of Learning from Implicit Feedback pdf

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

WebWatcher: Machine Learning and Hypertext

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

Support Vector Training of Protein Alignment Models

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

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

Katharina Morik, Thorsten Joachims, Stefan Rüping, 2002 .

Mapping preferences into Euclidean space

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

Improved Learning of Structural Support Vector Machines: Training with Latent Variables and Nonlinear Kernels

Thorsten Joachims, Chun Nam Yu, C. Yu, 2011 .

Composite Kernels for Hypertext Categorisation

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

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

Thorsten Joachims, T. Joachims, 1997, ICML.

Supervised Clustering with Structural SVMS

Thorsten Joachims, Thomas Finley, Thomas Finley, 2008 .

Modeling Intransitivity in Matchup and Comparison Data

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

Knowledge discovery and knowledge validation in intensive care

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

Labeling 3D scenes for Personal Assistant Robots pdf

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

Eye tracking and online search: Lessons learned and challenges ahead

Thorsten Joachims, Bing Pan, Fabio Pellacini, 2008 .

How does clickthrough data reflect retrieval quality?

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

Sparse kernel SVMs via cutting-plane training

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

Using personalized radio to enhance local music discovery

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

Detecting Concept Drift with Support Vector Machines

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

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

Thorsten Joachims, 2001, SIGIR '01.

REVEAL 2020: Bandit and Reinforcement Learning from User Interactions

Thorsten Joachims, Adith Swaminathan, Yves Raimond, 2020, RecSys.

KDD-Cup 2004: results and analysis

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

Dynamic ranked retrieval

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

MOReL : Model-Based Offline Reinforcement Learning pdf

Thorsten Joachims, Praneeth Netrapalli, Aravind Rajeswaran, 2020, NeurIPS.

Multi-space probabilistic sequence modeling

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

Learning diverse rankings with multi-armed bandits

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

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

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

Latent Skill Embedding for Personalized Lesson Sequence Recommendation pdf

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

Policy Learning for Fairness in Ranking

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

Sparse kernel SVMs via cutting-plane training

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

Unbiased Evaluation of Retrieval Quality using Clickthrough Data

Thorsten Joachims, T. Joachims, 2002 .

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

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

Cutting-plane training of structural SVMs

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

Temporal corpus summarization using submodular word coverage

Thorsten Joachims, Adith Swaminathan, Pannagadatta K. Shivaswamy, 2012, CIKM '12.

Beyond myopic inference in big data pipelines

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

Intervention Harvesting for Context-Dependent Examination-Bias Estimation

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

Evaluation methods for unsupervised word embeddings

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

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

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

Training Transductive Support Vector Machines

Thorsten Joachims, 2002 .

Transductive Learning via Spectral Graph Partitioning

Thorsten Joachims, T. Joachims, 2003, ICML.

Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs

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

SVM Light: Support Vector Machine

Thorsten Joachims, T. Joachims, 2002 .

Bayesian Ordinal Peer Grading

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

Deep Learning with Logged Bandit Feedback

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

Fair Learning-to-Rank from Implicit Feedback pdf

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

Inductive Text Classification

Thorsten Joachims, 2002 .

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 Search, Recommendation and Ad Placement

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

Recommendations as Treatments: Debiasing Learning and Evaluation

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

Accurately Interpreting Clickthrough Data as Implicit Feedback

Thorsten Joachims, Bing Pan, Geri Gay, 2017 .

Support Vector Machines (Aktuelles Schlagwort)

Thorsten Joachims, Thorsten Joachims, 1999, Künstliche Intell..

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

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

Invited Talk: Learning from Rational Behavior

Thorsten Joachims, 2014, EMNLP.

Coactive Learning

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

Enriching information retrieval

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

Supervised k-Means Clustering

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

Learning Trajectory Preferences for Manipulators via Iterative Improvement

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

Methods for ordinal peer grading

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

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

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

Error bounds for correlation clustering

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

Accurately interpreting clickthrough data as implicit feedback

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

Web Watcher: A Tour Guide for the World Wide Web

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

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

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

Large-scale validation and analysis of interleaved search evaluation

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

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

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

Proceedings, Twenty-Seventh International Conference on Machine Learning

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

Unbiased Comparative Evaluation of Ranking Functions

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

A support vector method for optimizing average precision

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

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

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

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

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

Support Vector Training of Protein Alignment Models

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

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

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

Fairness of Exposure in Rankings

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

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

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

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

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

Reducing Dueling Bandits to Cardinal Bandits pdf

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

Unstructuring User Preferences: Efficient Non-Parametric Utility Revelation

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

Efficient Performance Estimators for SVMs

Thorsten Joachims, 2002 .

Text-Mining - Serviceteil

Thorsten Joachims, Edda Leopold, Thorsten Joachims, 2002, Künstliche Intell..

Optimizing search engines using clickthrough data

Thorsten Joachims, T. Joachims, 2002, KDD.

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

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

Off-policy Bandits with Deficient Support

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

Stable Coactive Learning via Perturbation

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

Transductive Text Classification

Thorsten Joachims, 2002 .

Multi-armed Bandit Problems with History

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

Large-Margin Learning of Submodular Summarization Methods pdf

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

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

Thorsten Joachims, T. Joachims, 1998, ECML.

REVEAL 2018: offline evaluation for recommender systems

Thorsten Joachims, Adith Swaminathan, Olivier Koch, 2018, RecSys.

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

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

Estimating the Generalization Performance of an SVM Efficiently

Thorsten Joachims, T. Joachims, 2000, ICML.

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

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

Controlling Fairness and Bias in Dynamic Learning-to-Rank

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

Efficient and non-parametric reasoning over user preferences

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

The Maximum-Margin Approach to Learning Text Classifiers

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

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

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

Fairness of Exposure in Stochastic Bandits

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

Controlling Fairness and Bias in Dynamic Learning-to-Rank (Extended Abstract)

Thorsten Joachims, Marco Morik, Ashudeep Singh, 2021, IJCAI.

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

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

Fairness and Control of Exposure in Two-sided Markets

Thorsten Joachims, T. Joachims, 2021, ICTIR.

Recommendations as Treatments

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

Optimizing Rankings for Recommendation in Matching Markets pdf

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

Fairness in Ranking under Uncertainty pdf

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

Policy-Gradient Training of Fair and Unbiased Ranking Functions

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

Fairness and Diversity for Rankings in Two-Sided Markets pdf

Thorsten Joachims, Lequn Wang, 2020, ArXiv.

Parameter Learning for Loopy Markov Random Fields with Structural Support Vector Machines

Thorsten Joachims, Thomas Finley, 2007 .

Batch Learning from Bandit Feedback through Bias Corrected Reward Imputation

Thorsten Joachims, Lequn Wang, Yiwei Bai, 2019 .

Do the right thing ” : machine learning and causal inference for improved decision making

Thorsten Joachims, David Sontag, Nathan Kallus, .

Equality of Opportunity in Rankings

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

Identifying Temporal Patterns and Key Players in Document Collections

Thorsten Joachims, Johannes Gehrke, Rich Caruana, 1995 .