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

Office: 303 Annenberg

Contact Information >

I am a 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).

I am committed to promoting diversity, equity, and inclusion in my research group, in my courses, within the CMS department, at Caltech more broadly, and within my research communities.

• Diversity -- I recognize that diversity, in all its shapes and forms, strengthens us both culturally and intellectually.
• Equity -- I will fight for equal treatment of all people, regardless of race, gender, sexual orientation, or any other attributes that are completely irrelevant to academic and research potential.
• Inclusion -- I will work to create an inclusive working environment, so that everyone feels their voices are heard and their contributions are recognized.

My research interests lie primarily in machine learning, and span the entire theory-to-application spectrum from foundational advances all the way to deployment in real systems. My core interest is in developing practical theory of machine learning that pushes principled algorithm design towards real-world applications. I work closely with domain experts to understand the frontier challenges in applied machine learning, distill those challenges into mathematically precise formulations, and develop novel methods to tackle them.

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.

• Online Optimization with Memory and Competitive Control
  Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman
  [arxiv]
• On the distance between two neural networks and the stability of learning
  Jeremy Bernstein, Arash Vahdat, Yisong Yue, Ming-Yu Liu
  [arxiv][code]
• Learning Calibratable Policies using Programmatic Style-Consistency
  Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew Hausknecht
  International Conference on Machine Learning (ICML), July 2020.
  [pdf][arxiv][code][demo]
• Preference-Based Learning for Exoskeleton Gait Optimization
  Maegan Tucker, Ellen Novoseller, Claudia Kann, Yanan Sui, Yisong Yue, Joel Burdick, Aaron D. Ames
  International Conference on Robotics and Automation (ICRA), May 2020.
  (Best Paper Award)
  [pdf][arxiv][demo video][project]
• Imitation-Projected Programmatic Reinforcement Learning
  Abhinav Verma, Hoang M. Le, Yisong Yue, Swarat Chaudhuri
  Neural Information Processing Systems (NeurIPS), December 2019.
  [pdf][arxiv][code][demo video]
• 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][arxiv][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]
• 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]

• NSF Expeditons on Program Learning: we are starting a new research initiative titled Understanding the World Through Code, funded through the NSF Expeditions in Computing Program.
• Controllable Generation of Behaviors: we designed a new method that can generate behaviors calibrated to many different styles. [paper][code][demo]
  
• AI for Swarm Automation: check out this new article titled Machine Learning Helps Robot Swarms Coordinate. [paper 1][paper 2] (videos below)
  
• Invited Talk: I gave a talk on "Learning for Safety-Critical Control in Dynamical Systems" in the Physics ∩ ML seminar. [slides] (video below)
  
• Best Paper Award: Our work on preference learning for exoskeleton gait optimization is appearing at ICRA 2020 with a Best Paper Award! [arxiv][project] (video below).
  
• 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.
• Keynote talk at AI-for-Science: I am giving a keynote talk on Adaptive Experiment Design at the Caltech AI-for-Science workshop.
  
• 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.