My research interests lie primarily in the theory and application of statistical machine learning.
I am currently a visiting researcher Disney Research. In Fall 2014, I will be starting as an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology.
I was previously a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University, where I worked with Carlos Guestrin and Ramayya Krishnan.
I received my Ph.D. from the Computer Science Department at Cornell University, and my advisor was Thorsten Joachims.
Jump to: [publications] - [tutorials] - [other talk materials] - [research word cloud]
Graduate Admissions @ Caltech
Postdoc Openings @ Caltech
Self-Improving Systems that Learn Through Human Interaction - a popular-science blog article I wrote.
Structured Prediction - how can we model complex interdependencies to make more accurate predictions?
Text Classification & Information Extraction - how can we deduce 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?
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.
Data Mining and Knowledge Discovery
Information Processing & Management
Journal of Artificial Intelligence Research
Transactions on Knowledge and Data Engineering
Transactions on the Web
Conference Program Committee / Reviewer
ACML 2011, 2012
EMNLP 2011, 2012
ICML 2007, 2008, 2009, 2010, 2011, 2012, 2013 (Area Chair), 2014
NAACL-HLT 2012, 2013
NIPS 2008, 2009, 2010, 2011, 2012
SIGIR 2008, 2009, 2010, 2013
WSDM 2011, 2012, 2013, 2014
WWW 2011, 2012, 2013, 2014
Siyuan Liu, Yisong Yue, Ramayya Krishnan, Adaptive Collective Routing Using Gaussian Process Dynamic Congestion Models, ACM Conference on Knowledge Discovery and Data Mining (KDD), August, 2013.
Stephane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell, Learning Policies for Contextual Submodular Prediction, International Conference on Machine Learning (ICML), June, 2013.
Yisong Yue, Lavanya Marla, Ramayya Krishnan, An Efficient Simulation-based Approach to Ambulance Fleet Allocation and Dynamic Redeployment, AAAI Conference on Artificial Intelligence (AAAI), Special Track on Computational Sustainability and Artificial Intelligence, July, 2012.
[pdf][spotlight slide][poster][press release]
Yisong Yue, Sue Ann Hong, Carlos Guestrin, Hierarchical Exploration for Accelerating Contextual Bandits, International Conference on Machine Learning (ICML), June, 2012.
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, doi:10.1016/j.jcss.2011.12.028, May, 2012.
Olivier Chapelle, Thorsten Joachims, Filip Radlinski, Yisong Yue, Large Scale Validation and Analysis of Interleaved Search Evaluation, ACM Transactions on Information Systems (TOIS), 30(1), 6:1--6:41, February, 2012.
Yisong Yue, Carlos Guestrin, Linear Submodular Bandits and their Application to Diversified Retrieval, Neural Information Processing Systems (NIPS), December, 2011.
Yisong Yue, Thorsten Joachims, Beat the Mean Bandit, International Conference on Machine Learning (ICML), June, 2011.
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)
Yisong Yue, New Learning Frameworks for Information Retrieval, Ph.D. Dissertation, Cornell University, January, 2011.
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.
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.
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.
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)
Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims, The K-armed Dueling Bandits Problem, Conference on Learning Theory (COLT), June, 2009.
Yisong Yue, Thorsten Joachims, Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem, International Conference on Machine Learning (ICML), June, 2009.
Yisong Yue, Thorsten Joachims, Predicting Diverse Subsets Using Structural SVMs, International Conference on Machine Learning (ICML), June, 2008.
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.
Yisong Yue, Thomas Finley, Filip Radlinski, Thorsten Joachims, A Support Vector Method for Optimizing Average Precision, ACM Conference on Information Retrieval (SIGIR), July, 2007.
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, NIPS 2013 Workshop on Discrete and Combinatorial Problems in Machine Learning, December, 2013. [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]
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]