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

Office: 303 Annenberg

Contact Information >

I am an assistant professor of Computing and Mathematical Sciences 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. I am also on the Scientific Committee for Caltech's new Center for Autonomous Systems and Technology (CAST).

Please consider submitting to or attending the NeurIPS 2019 Workshop on Safety and Robustness in Decision-making!

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).

I am particularly interested in pushing machine learning technologies toward increasingly sophisticated real-world use cases. My foundational research is centered around 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, protein engineering, program synthesis, learning-accelerated optimization, robotics, and adaptive planning & allocation problems.

• Learning Calibratable Policies using Programmatic Style-Consistency
  Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew Hausknecht
  [arxiv]
• Imitation-Projected Programmatic Reinforcement Learning
  Abhinav Verma, Hoang M. Le, Yisong Yue, Swarat Chaudhuri
  Neural Information Processing Systems (NeurIPS), December 2019.
  [pdf][arxiv][code]
• Co-Training for Policy Learning
  Jialin Song, Ravi Lanka, Yisong Yue, Masahiro Ono
  Conference on Uncertainty in Artificial Intelligence (UAI), July 2019.
  (Oral Presentation)
  [pdf][slides][code]
• Batch Policy Learning under Constraints
  Hoang M. Le, Cameron Voloshin, Yisong Yue
  International Conference on Machine Learning (ICML), June 2019.
  (Oral Presentation)
  [pdf][arxiv][project]
• 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
  International Conference on Robotics and Automation (ICRA), May 2019.
  [pdf][arxiv][demo video][press release]
• A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes
  Jialin Song, Yuxin Chen, Yisong Yue
  International Conference on Artificial Intelligence and Statistics (AISTATS), April 2019.
  [pdf][arxiv]
• 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]
• Stagewise Safe Bayesian Optimization with Gaussian Processes
  Yanan Sui, Vincent Zhuang, Joel Burdick, Yisong Yue
  International Conference on Machine Learning (ICML), July 2018.
  [pdf][arxiv]
• Iterative Amortized Inference
  Joseph Marino, Yisong Yue, Stephan Mandt
  International Conference on Machine Learning (ICML), July 2018.
  [pdf][arxiv][code]
• 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]

• Partnered with PyTorch to film a short clip on our robotics research at CAST.
  
• Invited Talk: I am giving a talk at University of Chicago on November 13th, 2019.
• Invited Talk: I am giving a talk at the UIUC Computer Science Colloquium on November 11th, 2019.
• Invited Talk: I am speaking at the PyTorch Developer Conference on October 10th, 2019.
• Invited Workshop: I am giving a talk at the Workshop on Automated Algorithm Design hosted by the TTI-Chicago 2019 Summer Workshop Program.
• Invited Workshop: I am giving a talk at the Workshop on Data Collection, Curation, and Labeling for Mining and Learning co-located with KDD 2019.
• Invited Workshop: I am giving a talk at the Adversarial Machine Learning in Real-World Computer Vision Systems co-located with CVPR 2019.
• Invited Workshop: I am giving a talk at the 2019 Workshop on Human In the Loop Learning (HILL) co-located with ICML 2019. Video below:
• ICML 2019 Workshop: Please consider attending or submitting to the Real-World Sequential Decision Making Workshop co-located with ICML 2019!
• Simons Symposium: I am attending a Simons Symposium on New Directions in Theoretical Machine Learning on May 5-11, 2019.
• Invited Talk: I am speaking at the Machine Learning Seminar at CMU on April 23rd, 2019.
• Neural Lander: Our work on deep learning for provably stable drone landing control is appearing at ICRA 2019! [arxiv] (video below)
  
• 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 Computer Science 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)