Yisong's World of Research
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I am currently a postdoctoral researcher in the iLab and the Machine Learning Department at Carnegie Mellon University, working with Carlos Guestrin and others there. My research interests lie in machine learning, information retrieval, and online algorithms. I previously received my Ph.D. from the Computer Science Department at Cornell University, and my advisor was Thorsten Joachims.

  • Research Biography

  • Curriculum Vitae

  • Self-Improving Systems that Learn Through Human Interaction - a popular-science blog article I wrote.


    Research Interests:
  • Structured Prediction - how can we model complex interdependencies to make more accurate predictions?
  • Text Classification & Information Extraction - how can we deduce or uncover the salient information embedded in textual data?
  • Diversified Retrieval - how can we make diverse and novel recommendations to improve utility to users?
  • 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?


    Software:
  • SVMsle - this is a Support Vector Machine method for learning to predict document-level sentiment polarity using latent explanations.
  • SVMdiv - this is a Support Vector Machine method for learning to predict diverse subsets for subtopic retrieval.
  • SVMmap - this is a Support Vector Machine method for learning ranking functions that that optimize for mean average precision.


    Professional Activities:
    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
  • ACL 2012
  • ACML 2011
  • COLING 2010
  • ECML/PKDD 2008
  • EMNLP 2011
  • ICML 2007, 2008, 2009, 2010, 2011, 2012
  • KDD 2011
  • NAACL-HLT 2012
  • NIPS 2008, 2009, 2010, 2011
  • SIGIR 2008, 2009, 2010
  • SoCG 2010
  • WSDM 2011, 2012
  • WWW 2011, 2012
  • Workshop on Enriching Information Retrieval, SIGIR 2011
  • Workshop on Redundancy, Diversity, and Interdependent Document Relevance, SIGIR 2009
  • Workshop on Beyond Binary Relevance: Preferences, Diversity, and Set-Level Judgments, SIGIR 2008
  • Workshop on Learning to Rank for Information Retrieval, SIGIR 2008, 2009


    Publications:
  • Olivier Chapelle, Thorsten Joachims, Filip Radlinski, Yisong Yue, Large Scale Validation and Analysis of Interleaved Search Evaluation, ACM Transactions on Information Systems (TOIS), (to appear)
    [preprint]

  • Yisong Yue, Carlos Guestrin, Linear Submodular Bandits and their Application to Diversified Retrieval, Neural Information Processing Systems (NIPS), December, 2011.
    [pdf][long][poster]

  • Yisong Yue, Thorsten Joachims, Beat the Mean Bandit, International Conference on Machine Learning (ICML), June, 2011.
    [pdf][slides][poster][video]

  • Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims, The K-armed Dueling Bandits Problem, Journal of Computer and System Sciences (JCSS), Special Issue on Learning Theory, (to appear)
    [preprint]

  • Christina Brandt, Thorsten Joachims, Yisong Yue, Jacob Bank, Dynamic Ranked Retrieval, ACM Conference on Web Search and Data Mining (WSDM), February, 2011. (Best Paper Nomination)
    [pdf][software][video]

  • Yisong Yue, New Learning Frameworks for Information Retrieval, Ph.D. Dissertation, Cornell University, January, 2011.
    [pdf]

  • Ainur Yessenalina, Yisong Yue, Claire Cardie, Multi-level Structured Models for Document-level Sentiment Classification, Conference on Empirical Methods in Natural Language Processing (EMNLP), October, 2010.
    [pdf][software][data]

  • Yisong Yue, Yue Gao, Olivier Chapelle, Ya Zhang, Thorsten Joachims, Learning More Powerful Test Statistics for Click-Based Retrieval Evaluation, ACM Conference on Information Retrieval (SIGIR), July, 2010.
    [pdf][slides]

  • Yisong Yue, Rajan Patel, Hein Roehrig, Beyond Position Bias: Examining Result Attractiveness as a Source of Presentation Bias in Clickthrough Data, World Wide Web Conference (WWW), April, 2010.
    [pdf][slides]

  • Thorsten Joachims, Thomas Hofmann, Yisong Yue, Chun-Nam Yu, Predicting Structured Objects with Support Vector Machines, Communications of the ACM (CACM), Research Highlight, 52(11), 97--104, November, 2009. (with a technical perspective by John Shawe-Taylor)
    [pdf][online]

  • Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims, The K-armed Dueling Bandits Problem, Conference on Learning Theory (COLT), June, 2009.
    [pdf][slides]

  • Yisong Yue, Thorsten Joachims, Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem, International Conference on Machine Learning (ICML), June, 2009.
    [pdf][slides][video]

  • Yisong Yue, Thorsten Joachims, Predicting Diverse Subsets Using Structural SVMs, International Conference on Machine Learning (ICML), June, 2008.
    [pdf][slides][software][video]

  • Yisong Yue, Christopher Burges, On Using Simultaneous Perturbation Stochastic Approximation for IR Measures, and the Empirical Optimality of LambdaRank, NIPS Machine Learning for Web Search Workshop, December, 2007.
    [pdf][tech report]

  • Yisong Yue, Thomas Finley, Filip Radlinski, Thorsten Joachims, A Support Vector Method for Optimizing Average Precision, ACM Conference on Information Retrieval (SIGIR), July, 2007.
    [pdf][slides][software]


    Tutorials:
  • 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]
  • Brief Introduction on Structural SVMs, presented for Microsoft Research Web Learning Group, August, 2007. [slides]


    Other Talk Materials:
  • 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, UMass Amherst, April, 2009. [slides]


    Teaching Assistant Experience:
  • Fall 2007, CS 473 - AI Practicum / Robotics and Embodied AI
  • Spring 2007, INFO 204 - Networks [received TA excellence award]
  • Fall 2006, CS 578 - Empirical Methods in Machine Learning and Data Mining
  • Fall 2005 - Spring 2006, CS 100M - Introduction to Computer Programming [Teaching Evaluation] [received TA excellence award]

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