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

Office: 303 Annenberg
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Admin: Diane Goodfellow (

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.
Postdoc Openings
We are soliciting postdoc applications for the DOLCIT Postdoctoral Fellowship Program. Ideal candidates should have broad interests in machine learning. Our research interests span the entire spectrum of theoretical and applied machine learning. We have numerous collaborations with the sciences and engineering.
New PhD Program
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 interests lie primarily in the theory and application of statistical machine learning. I am particularly interested in developing novel methods for interactive machine learning and structured machine learning. 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, experiment design for science, policy learning in robotics, and adaptive planning & allocation problems.
Selected Recent Publications & Preprints:
  • Neural Lander: Stable Drone Landing Control using Learned Dynamics
    Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
    [arxiv][demo video]
  • Detecting Adversarial Examples via Neural Fingerprinting
    Sumanth Dathathri, Stephan Zheng, Yisong Yue, Richard M. Murray
  • Learning to Search via Retrospective Imitation
    Jialin Song, Ravi Lanka, Albert Zhao, Yisong Yue, Masahiro Ono
  • Generative Multi-Agent Behavioral Cloning
    Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey
  • Hierarchical Imitation and Reinforcement Learning
    Hoang M. Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III
    International Conference on Machine Learning (ICML), July 2018.
  • Iterative Amortized Inference
    Joseph Marino, Yisong Yue, Stephan Mandt
    International Conference on Machine Learning (ICML), July 2018.
  • Near-Optimal Machine Teaching via Explanatory Teaching Sets
    Yuxin Chen, Oisin Mac Aodha, Shihan Su, Pietro Perona, Yisong Yue
    International Conference on Artificial Intelligence and Statistics (AISTATS), April 2018.
  • Multi-dueling Bandits with Dependent Arms
    Yanan Sui, Vincent Zhuang, Joel Burdick, Yisong Yue
    Conference on Uncertainty in Artificial Intelligence (UAI), August 2017.
  • 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][demo video]
  • 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][video][press release][Sports Illustrated][]
News & Announcements
[All Content © 2018 Yisong Yue]