Yisong Yue - Research

I am a Ph.D. candidate in the Computer Science Department at Cornell University. My research interests lie primarily in Machine Learning and Information Retrieval. My advisor is Thorsten Joachims.

My work is supported in part by a Microsoft Fellowship and a Yahoo! Key Technical Challenge grant.

My CV can be found here. References are available upon email request (yyue-at-cs-dot-cornell-edu).

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.

Software:

SVM^div - this is a Support Vector Machine method for learning to predict diverse subsets for subtopic retrieval.

SVM^map - this is a Support Vector Machine method for predicting rankings that optimizes for Mean Average Precision.

Professional Activities:
Journal Reviewer

Information Processing & Management

Journal of Artificial Intelligence Research

Neural Networks

Transactions on Knowledge and Data Engineering

Transactions on the Web

Program Committee

ECML/PKDD 2008

SIGIR 2008 Workshop on Beyond Binary Relevance: Preferences, Diversity, and Set-Level Judgments

SIGIR 2008 Workshop on Learning to Rank for Information Retrieval

Publications:

Y. Yue, T. Joachims, Predicting Diverse Subsets Using Structural SVMs, In Proceedings of ICML, 2008. [pdf][ppt][software]

Y. Yue, C.J.C. 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]

Y. Yue, T. Finley, F. Radlinski, T. Joachims, A Support Vector Method for Optimizing Average Precision, In Proceedings of SIGIR, 2007. [pdf][ppt][software]

Other Talks:

Tutorial on Learning to Rank, co-taught with Filip Radlinski, presented at NESCAI '08. [pt1][pt2]

Brief Introduction on Structural SVMs, presented for Microsoft Research Web Learning Group. [ppt]

Manuscripts:

Y. Li, H. Tan, Y. Yue, Finding Influential Blogs Using Link Prediction, Cornell University, Spring 2006.

J.A. Lenfestey, Y. Yue, Loss-Minimizing Voting for Machine Learning Ensembles, Cornell University, Spring 2006.

Y. Yue, Parameter Estimation on MRF-Stereo with Occlusion, Cornell University, Fall 2005.

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