Research Interests
  • Spatiotemporal Reasoning - how can we reason about raw spatiotemporal data to build more compact and accurate models?
  • Structured Prediction - how can we model complex interdependencies to make more accurate predictions?
  • Interactive Systems - how can we model the entire interaction experience between users and the system?
  • Online Algorithms - how can we design algorithms that can perform well when they learn "on the fly"?
  • Implicit Feedback - how can we best interpret and leverage observed user behaviors to train smarter systems?
  • Rich User Interactions - how can we best learn from interactions between a user and a rich digital interface?
Software & Data
  • Collaborative Clustering - this dataset was collected using a clustering interface. Each user provides a partial clustering of all the items. [link]
  • Ambulance Allocation - this dataset was built for data-driven simulation-based ambulance allocation. [link]
  • SVMsle - this is a Support Vector Machine method for learning to predict document-level sentiment polarity using latent explanations. [link]
  • SVMdiv - this is a Support Vector Machine method for learning to predict diverse subsets for subtopic retrieval. [link]
  • SVMmap - this is a Support Vector Machine method for learning ranking functions that that optimize for mean average precision. [link]
Professional Activities
Note - Henceforth, I refuse to be a reviewer or editor for any Elsevier journal for reasons described here.

Journal Reviewer
  • Data Mining and Knowledge Discovery
  • Information Processing & Management
  • Information Retrieval
  • Journal of Artificial Intelligence Research
  • Neural Networks
  • Transactions on Knowledge and Data Engineering
  • Transactions on the Web

Conference Program Committee / Reviewer
  • AAAI 2014, 2015
  • ACL 2012
  • ACML 2011, 2012, 2014
  • CIKM 2012
  • COLING 2010, 2014
  • ECML/PKDD 2008
  • EMNLP 2011, 2012
  • ICML 2007, 2008, 2009, 2010, 2011, 2012, 2013 (Area Chair), 2014
  • KDD 2011, 2015 (Senior PC)
  • NAACL-HLT 2012, 2013
  • NIPS 2008, 2009, 2010, 2011, 2012, 2014
  • SIGIR 2008, 2009, 2010, 2013, 2014
  • SoCG 2010
  • UBICOMP 2014
  • WSDM 2011, 2012, 2013, 2014, 2015
  • WWW 2011, 2012, 2013, 2014
Publications
  • Identifying Team Style in Soccer using Formations Learned from Spatiotemporal Tracking Data
    Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue, Sridha Sridharan, Iain Matthews
    ICDM 2014 International Workshop on Spatial and Spatio-temporal Data Mining (SSTDM), December, 2014.
    [pdf]
  • Learning Fine-Grained Spatial Models for Dynamic Sports Play Prediction
    Yisong Yue, Patrick Lucey, Peter Carr, Alina Bialkowski, Iain Matthews
    IEEE International Conference on Data Mining (ICDM), December, 2014.
    (Best Paper Nomination)
    [pdf][demo]
  • Large-Scale Analysis of Soccer Matches using Spatiotemporal Tracking Data
    Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue, Sridha Sridharan, Iain Matthews
    IEEE International Conference on Data Mining (ICDM), December, 2014.
    [pdf]
  • Personalized Collaborative Clustering
    Yisong Yue, Chong Wang, Khalid El-Arini, Carlos Guestrin
    International World Wide Web Conference (WWW), April, 2014.
    [pdf][slides][data]
  • "Win at Home and Draw Away": Automatic Formation Analysis Highlighting the Differences in Home and Away Team Behaviors
    Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue, Iain Matthews
    MIT Sloan Sports Analytics Conference (SSAC), February, 2014.
    [pdf]
  • "How to Get an Open Shot": Analyzing Team Movement in Basketball using Tracking Data
    Patrick Lucey, Alina Bialkowski, Peter Carr, Yisong Yue, Iain Matthews
    MIT Sloan Sports Analytics Conference (SSAC), February, 2014.
    [pdf]
  • Adaptive Collective Routing Using Gaussian Process Dynamic Congestion Models
    Siyuan Liu, Yisong Yue, Ramayya Krishnan
    ACM Conference on Knowledge Discovery and Data Mining (KDD), August, 2013.
    [pdf]
  • Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization
    Jiaji Zhou, Stephane Ross, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell
    ICML Workshop on Inferning: Interactions between Inference and Learning, June, 2013.
    [pdf][software]
  • Learning Policies for Contextual Submodular Prediction
    Stephane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell
    International Conference on Machine Learning (ICML), June, 2013.
    [pdf][long][software][video]
  • An Efficient Simulation-based Approach to Ambulance Fleet Allocation and Dynamic Redeployment
    Yisong Yue, Lavanya Marla, Ramayya Krishnan
    AAAI Conference on Artificial Intelligence (AAAI), Special Track on Computational Sustainability and Artificial Intelligence, July, 2012.
    [pdf][spotlight slide][poster][press release][data]
  • Hierarchical Exploration for Accelerating Contextual Bandits
    Yisong Yue, Sue Ann Hong, Carlos Guestrin
    International Conference on Machine Learning (ICML), June, 2012.
    [pdf][long][slides][poster][video]
  • The K-armed Dueling Bandits Problem
    Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims
    Journal of Computer and System Sciences (JCSS), Special Issue on Learning Theory, doi:10.1016/j.jcss.2011.12.028, May, 2012.
    [pdf][online]
  • Large Scale Validation and Analysis of Interleaved Search Evaluation
    Olivier Chapelle, Thorsten Joachims, Filip Radlinski, Yisong Yue
    ACM Transactions on Information Systems (TOIS), 30(1), 6:1--6:41, February, 2012.
    [pdf]
  • Linear Submodular Bandits and their Application to Diversified Retrieval
    Yisong Yue, Carlos Guestrin
    Neural Information Processing Systems (NIPS), December, 2011.
    [pdf][long][poster]
  • Beat the Mean Bandit
    Yisong Yue, Thorsten Joachims
    International Conference on Machine Learning (ICML), June, 2011.
    [pdf][slides][poster][video]
  • Dynamic Ranked Retrieval
    Christina Brandt, Thorsten Joachims, Yisong Yue, Jacob Bank
    ACM Conference on Web Search and Data Mining (WSDM), February, 2011.
    (Best Paper Nomination)
    [pdf][software][video]
  • New Learning Frameworks for Information Retrieval
    Yisong Yue
    Ph.D. Dissertation, Cornell University, January, 2011.
    [pdf]
  • Multi-level Structured Models for Document-level Sentiment Classification
    Ainur Yessenalina, Yisong Yue, Claire Cardie
    Conference on Empirical Methods in Natural Language Processing (EMNLP), October, 2010.
    [pdf][software][data]
  • Learning More Powerful Test Statistics for Click-Based Retrieval Evaluation
    Yisong Yue, Yue Gao, Olivier Chapelle, Ya Zhang, Thorsten Joachims
    ACM Conference on Information Retrieval (SIGIR), July, 2010.
    [pdf][slides]
  • Beyond Position Bias: Examining Result Attractiveness as a Source of Presentation Bias in Clickthrough Data
    Yisong Yue, Rajan Patel, Hein Roehrig
    International World Wide Web Conference (WWW), April, 2010.
    [pdf][slides]
  • Predicting Structured Objects with Support Vector Machines
    Thorsten Joachims, Thomas Hofmann, Yisong Yue, Chun-Nam Yu
    Communications of the ACM (CACM), Research Highlight, 52(11), 97--104, November, 2009. (with a technical perspective by John Shawe-Taylor)
    [pdf][online]
  • The K-armed Dueling Bandits Problem
    Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims
    Conference on Learning Theory (COLT), June, 2009.
    [pdf][slides]
  • Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem
    Yisong Yue, Thorsten Joachims
    International Conference on Machine Learning (ICML), June, 2009.
    [pdf][slides][video]
  • Predicting Diverse Subsets Using Structural SVMs
    Yisong Yue, Thorsten Joachims
    International Conference on Machine Learning (ICML), June, 2008.
    [pdf][slides][software][video]
  • On Using Simultaneous Perturbation Stochastic Approximation for IR Measures, and the Empirical Optimality of LambdaRank
    Yisong Yue, Christopher Burges
    NIPS Machine Learning for Web Search Workshop, December, 2007.
    [pdf][tech report]
  • A Support Vector Method for Optimizing Average Precision
    Yisong Yue, Thomas Finley, Filip Radlinski, Thorsten Joachims
    ACM Conference on Information Retrieval (SIGIR), July, 2007.
    [pdf][slides][software]
Tutorials
  • An Introduction to Ensemble Methods: Bagging, Boosting, Random Forests and More, presented at Disney Research.
    [slides]
  • Practical Online Retrieval Evaluation, co-taught with Filip Radlinski, presented at SIGIR 2011.
    [slides][demo scripts]
  • Learning to Rank, co-taught with Filip Radlinski, presented at NESCAI 2008.
    [part1][part2]
Other Talk Materials
  • Balancing the Explore/Exploit Tradeoff in Interactive Structured Prediction, California Institute of Technology, October, 2014.
    [slides]
  • Learning with Humans in the Loop, Disney Research, May, 2013.
    [slides]
  • Optimizing Recommender Systems as a Submodular Bandit Problem, University of Toronto, November, 2012.
    [slides]
  • An Introduction to Structural SVMs and its Application to Information Retrieval, University of California Berkeley, October, 2012.
    [slides]
  • Practical and Reliable Retrieval Evaluation Through Online Experimentation, WSDM 2012 Workshop on Web Search Click Data, February, 2012.
    [slides]
  • An Interactive Learning Approach to Optimizing Information Retrieval Systems, Carnegie Mellon University, September, 2010.
    [slides][video]
  • New Learning Frameworks for Information Retrieval, Microsoft Research, March, 2010.
    [video]
  • Diversified Retrieval as Structured Prediction, SIGIR 2009 Workshop on Redundancy, Diversity and Interdependent Document Relevance, July, 2009.
    [slides]
  • Information Retrieval as Structured Prediction, University of Massachusetts Amherst, April, 2009.
    [slides]
[All Content © 2014 Yisong Yue]