Yisong Yue
California Institute of Technology
1200 E. California Blvd.
CMS, 305-16
Pasadena, CA 91125

Office: 303 Annenberg
Email: yyue@caltech.edu
(please read before emailing)

Admin: Diane Goodfellow (diane@cms.caltech.edu)

I am an assistant professor in the Computing and Mathematical Sciences department at the California Institute of Technology. I am the director of the DOLCIT, which is broadly centered around research pertaining statistical decision theory, statistical machine learning, and optimization.

Prospective PhD Students, please consider applying to Caltech's new PhD program in Computing and Mathematical Sciences.

I also recruit from the PhD program in Computation & Neural Systems.

My research interests lie primarily in the theory and application of statistical machine learning. I am more generally interested in artificial intelligence. I am the director of DOLCIT, and I also interact closely with the RSRG and Computational Vision groups at Caltech, as well as the Center for Autonomous Systems and Technology (CAST) and the Center for the Mathematics of Information (CMI).

My research is largely centered around developing integrated learning-based approaches that can characterize complex structured and adaptive decision-making settings. Currently, my focus areas include learning with humans in the loop, interactive learning systems, structured prediction, and spatiotemporal reasoning. In the past, my research has been applied to information retrieval, recommender systems, text classification, learning from rich user interfaces, analyzing implicit human feedback, data-driven animation, behavior analysis, sports analytics, policy learning in robotics, and adaptive routing & allocation problems.

• Multi-dueling Bandits with Dependent Arms
  Yanan Sui, Vincent Zhuang, Joel Burdick, Yisong Yue
  Conference on Uncertainty in Artificial Intelligence (UAI), August 2017.
  [pdf][long][arxiv]
• Coordinated Multi-Agent Imitation Learning
  Hoang M. Le, Yisong Yue, Peter Carr, Patrick Lucey
  International Conference on Machine Learning (ICML), August 2017.
  [pdf][long][arxiv][data]
  
• A Deep Learning Approach for Generalized Speech Animation
  Sarah Taylor, Taehwan Kim, Yisong Yue, Moshe Mahler, James Krahe, Anastasio Garcia Rodriguez, Jessica Hodgins, Iain Matthews
  ACM Conference on Computer Graphics (SIGGRAPH), July 2017.
  [pdf][long][demo video]
  
• Generating Long-term Trajectories Using Deep Hierarchical Networks
  Stephan Zheng, Yisong Yue, Patrick Lucey
  Neural Information Processing Systems (NIPS), December 2016.
  [pdf][long][data]
• Smooth Imitation Learning for Online Sequence Prediction
  Hoang M. Le, Andrew Kang, Yisong Yue, Peter Carr
  International Conference on Machine Learning (ICML), June, 2016.
  [pdf][long][video][press release][Sports Illustrated]
• Personalized Collaborative Clustering
  Yisong Yue, Chong Wang, Khalid El-Arini, Carlos Guestrin
  International World Wide Web Conference (WWW), April, 2014.
  [pdf][slides][data]

• Invited Workshop: I am attending the Data-driven Algorithmics workshop on November 5-10, 2017.
• Invited Talk: Invited talk at Southern California Machine Learning Symposium on October 6th, 2017.
• Invited Talk: Invited talk at Microsoft Research Colloquium at MSR New England on September 6th, 2017.
• Dagstuhl Seminar: I am attending the Machine Learning and Formal Methods Dagstuhl Seminar on August 28th - September 1st, 2017.
• Press Release: Our work on data-driven speech animation is highlighted on Road to VR! [SIGGRAPH paper][demo video (shown below)]
  
• Press Release & Interview: Neural Networks Model Audience Reactions to Movies, also radio interview. [CVPR paper]
• Microsoft Faculty Summit: I am attending the Edge of AI Microsoft Research Faculty Summit on July 17-18, 2017.
• Invited Talk: Invited talk at Machine Learning Methods for Recommender Systems Workshop to be held at SDM 2017.
• Invited talk: Invited talk at Symposium on Machine Learning and Human Behavior to be held at UC Irvine on March 10th, 2017.
• Best Paper Runner Up: Our paper "Data-Driven Ghosting using Deep Imitation Learning" wins Runner Up to Best Research Paper at the MIT Sloan Sports Analytics Conferece! [pdf][project][press release][demo video (shown below)]
  
• Caltech Computes Alumni College: I gave a talk titled Automatically Improving Automation using Big Data at the Caltech Computes Alumni College.
  
• Southern California Machine Learning Symposium: I am co-organizing the next SoCal ML Symposium, to be held at Caltech on November 18th, 2016.
• Innovation in Artificial Intelligence: I will be moderating a discussion panel on Innovation in Artificial Intelligence on September 15th, 2016.
• Coverage on Sports Illustrated: My collaboration with Disney Research on imitation learning for camera control is featured on Sports Illustrated! [ICML paper][CVPR paper]
• Bloomberg Data Science Research Grant: I'm delighted to be awarded a Data Science Research Grant from Bloomberg Labs!
• Personalization Workshop: Please consider participating in our Computational Frameworks for Personalization Workshop being held at ICML 2016!
• Sports Analytics Workshop: Please consider participating in our Large-Scale Sports Analytics Workshop being held at KDD 2016!