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.

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.

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.

• 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
  [arxiv][code]
• Learning to Search via Retrospective Imitation
  Jialin Song, Ravi Lanka, Albert Zhao, Yisong Yue, Masahiro Ono
  [arxiv]
• Generating Multi-Agent Trajectories using Programmatic Weak Supervision
  Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey
  International Conference on Learning Representations (ICLR), May 2019.
  [OpenReview][arxiv (outdated)][demo][code]
• 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.
  [pdf][arxiv][project]
• Iterative Amortized Inference
  Joseph Marino, Yisong Yue, Stephan Mandt
  International Conference on Machine Learning (ICML), July 2018.
  [pdf][arxiv][code]
• 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.
  [pdf]
• Multi-dueling Bandits with Dependent Arms
  Yanan Sui, Vincent Zhuang, Joel Burdick, Yisong Yue
  Conference on Uncertainty in Artificial Intelligence (UAI), August 2017.
  [pdf][arxiv]
• 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][DataScience.com]

• Earthquake Early Detection: State-of-the-art results on earthquake early detection using deep learning! [arxiv]
• Earthquake Localization: State-of-the-art results on earthquake localization using deep learning! [arxiv]
• Invited Workshop: I am presenting at the NeurIPS 2018 workshop on Imitation Learning and its Challenges in Robotics.
• Invited Talk: I am presenting at UCLA on November 20th, 2018.
• Invited Talk: I am presenting at the University of Maryland College Park on October 24th, 2018.
• Invited Talk: I am presenting at Microsoft Research Redmond on October 3rd & 4th, 2018.
• Invited Talk: I am presenting at the University of Washington AI Seminar on October 2nd, 2018.
• Invited Talk: I am presenting at the Rice CS Colloquium on September 27th, 2018.
• Okawa Award: I recently received the Okawa Foundation Research Grant.
• Invited Workshop: I am presenting on Inference+Imitation at the Tractable Probabilistic Models workshop at ICML 2018.
• Invited Workshop: I am presenting on Machine Teaching for Human Learners at the Humanizing AI workshop at IJCAI 2018.
• Tutorial: I am giving a tutorial on imitation learning with Hoang Le at ICML 2018. (video below)
  
• Invited Talk: I am presenting at the Intel AI DevCon. (video below)
  
• Invited Talk: Invited talk at UT Austin AI Seminar on March 30th, 2018.
• 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.
• Blog Article: Our paper on smooth imitation learning was covered in an invited blog article titled Beyond Deep Learning: a Case Study in Sports Analytics. [ICML paper]
• Invited Talk: Invited talk at Microsoft Research Colloquium at MSR New England on September 6th, 2017. [video]
• 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]