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I am a Ph.D. candidate in the Computer Science Department at Cornell University. My research interests lie primarily in machine learning, information retrieval, and online algorithms. My advisor is Thorsten Joachims.
My work is supported in part by a Microsoft Research Graduate Fellowship and a Yahoo! Key Technical Challenges grant. I am also funded as part of the NSF projects Learning Structure to Structure Mappings and Learning from Implicit Feedback Through Online Experimentation.
My CV can be found here.
Current Work:
My current work focuses developing a learning framework for prediction tasks within information retrieval. Machine learning techniques have proven to be very effective for state-of-the-art search engines (e.g., Google, Yahoo!, Live). As retrieval models become more complex, machine learning will only become more useful. It is important to develop principled techniques with which we can reason about design decisions when considering new retrieval paradigms.
Research Interests:
Support Vector Machines, Structured Prediction
Learning to Rank, Information Retrieval
Online Algorithms, Interactive Learning
Software:
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 predicting rankings that optimizes 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
Program Committee
SIGIR 2009
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
ECML/PKDD 2008
Publications:
Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims, The K-armed Dueling Bandits Problem, Conference on Learning Theory (COLT), 2009.
[pdf][slides]
Yisong Yue, Thorsten Joachims, Interactively Optimizing Information
Retrieval Systems as a Dueling Bandits Problem, International Conference on Machine Learning (ICML),
2009.
[pdf][slides]
Yisong Yue, Thorsten Joachims, Predicting Diverse Subsets Using Structural SVMs, International Conference on Machine Learning (ICML), 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, 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), 2007.
[pdf][slides][software]
Tutorials:
Learning to Rank, co-taught with Filip Radlinski, presented at NESCAI 2008. [pt1][pt2]
Brief Introduction on Structural SVMs, presented for Microsoft Research Web Learning Group, August, 2007. [slides]
Other Talks:
Information Retrieval as Structured Prediction, UMass Amherst Machine Learning Seminar, April, 2009.
Structured Prediction and Active Learning for Information Retrieval, Microsoft Research Asia Invited Talk, August, 2008.
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 [received TA excellence award]
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