AboutI 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 centered around research pertaining statistical decision theory, statistical machine learning, and optimization, broadly construed.
New PhD ProgramProspective 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.
ResearchMy 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.
Currently, I am particularly interested in 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.
Some Recent Publications:
- Generating Long-term Trajectories Using Deep Hierarchical Networks
Stephan Zheng, Yisong Yue, Patrick Lucey
Neural Information Processing Systems (NIPS), December 2016.
- 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][press release][Sports Illustrated]
- Chalkboarding: A New Spatiotemporal Query Paradigm for Sports Play Retrieval
Long Sha, Patrick Lucey, Yisong Yue, Peter Carr, Charlie Rohlf, Iain Matthews
ACM Conference on Intelligent User Interfaces (IUI), March, 2016.
[pdf][demo video][press release]
- A Decision Tree Framework for Spatiotemporal Sequence Prediction
Taehwan Kim, Yisong Yue, Sarah Taylor, Iain Matthews
ACM Conference on Knowledge Discovery and Data Mining (KDD), August, 2015.
- Personalized Collaborative Clustering
Yisong Yue, Chong Wang, Khalid El-Arini, Carlos Guestrin
International World Wide Web Conference (WWW), April, 2014.
News & Announcements
- 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!
- Blog Post: Thoughts on NIPS 2015 and OpenAI.
- Invited Workshop: Algorithms for Human Robot Interaction Workshop.
- Blog Post: Thoughts on KDD 2015.
- Fundraising Chair of AISTATS 2016.
- Invited Talk at Reflections | Projections 2015 organized by ACM@UIUC.
- Invited Workshop: Data-driven Algorithmics Workshop. [slides]
- A Decision Tree Framework for Spatiotemporal Sequence Prediction accepted for publication at KDD 2015. [pdf][demo]
- Sports Analytics Workshop: Please consider participating in our Large-Scale Sports Analytics Workshop being held at KDD 2015!
- Interview by Jessica Stoller-Conrad @Caltech. [link]
- Invited Talk at Human Propelled Machine Learning Workshop at NIPS 2014.
- Learning Fine-Grained Spatial Models for Dynamic Sports Play Prediction accepted for publication at ICDM 2014. [pdf][demo][press release]
- Personalization Workshop: Please consider participating in our Personalization Workshop being held at NIPS 2014!
- Sports Analytics Workshop: Please consider participating in our Large-Scale Sports Analytics Workshop being held at KDD 2014!