Yisong Yue (he/him)
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 do not define a person's 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.

Read more about diversity, equity, and inclusion at the CMS Department and EAS Division at Caltech.

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. 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.

• Learning Unstable Dynamics with One Minute of Data: A Differentiation-based Gaussian Process Approach
  Ivan D. Jimenez Rodriguez, Ugo Rosolia, Aaron D. Ames, Yisong Yue
  [arxiv]
• Iterative Amortized Policy Optimization
  Joseph Marino, Alexandre Piché, Alessandro Davide Ialongo, Yisong Yue
  [arxiv][code]
• Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactions
  Guanya Shi, Wolfgang Hönig, Xichen Shi, Yisong Yue, Soon-Jo Chung
  IEEE Transactions on Robotics (T-RO), 2021.
  [arxiv][video][press release]
• Task Programming: Learning Data Efficient Behavior Representations
  Jennifer J. Sun, Ann Kennedy, Eric Zhan, David J. Anderson, Yisong Yue, Pietro Perona
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2021.
  (Best Student Paper Award)
  [arxiv][code][project]
• Learning to Make Decisions via Submodular Regularization
  Ayya Alieva, Aiden Aceves, Jialin Song, Stephen Mayo, Yisong Yue, Yuxin Chen
  International Conference on Learning Representations (ICLR), May 2021.
  [pdf][poster]
• Online Robust Control of Nonlinear Systems with Large Uncertainty
  Dimitar Ho, Hoang M. Le, John Doyle, Yisong Yue
  International Conference on Artificial Intelligence and Statistics (AISTATS), April 2021.
  [pdf][arxiv]
• Learning Differentiable Programs with Admissible Neural Heuristics
  Ameesh Shah*, Eric Zhan*, Jennifer J. Sun, Abhinav Verma, Yisong Yue, Swarat Chaudhuri
  Neural Information Processing Systems (NeurIPS), December 2020.
  [arxiv][code]
• A General Large Neighborhood Search Framework for Solving Integer Programs
  Jialin Song, Ravi Lanka, Yisong Yue, Bistra Dilkina
  Neural Information Processing Systems (NeurIPS), December 2020.
  [pdf][arxiv][code]
• On the distance between two neural networks and the stability of learning
  Jeremy Bernstein, Arash Vahdat, Yisong Yue, Ming-Yu Liu
  Neural Information Processing Systems (NeurIPS), December 2020.
  [arxiv][code]
• 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]
• 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]
• 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]

• AI for Science Challenge: We have released the Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions. [arxiv][dataset][code]
  
• Invited Talk: Video now available of my talk on "Learning to Optimize as Policy Learning" presented at the Princeton Optimization Seminar. [slides]
  
• Invited Talk: I presented on "Learning for Safety-Critical Control in Dynamical Systems" at the Control Meets Learning seminar series.
  
• Invited Talk: I gave the Earnest C. Watson Lecture on January 13th, 2021.
  
• Invited Talk: I gave a presentation on AI for Adaptive Experiment Design at the Directions in ML: AutoML and Automating Algorithms hosted by Microsoft Research. [slides]
  
• Neural-Swarm2: we have released the details of our heterogeneous neural swarm approach! [paper]
  
• Invited Talk: I am presenting on "Competitive Algorithms for Online Control" at the Simons Institute Workshop on Mathematics of Online Decision Making. [slides]
  
• Invited Talk: I am presenting at the Workshop on Imitation Learning: Single & Multi-Agent hosted by DAI 2020. [slides]
• Invited Talk: I am presenting on "Learning to Optimize as Policy Learning" at the Princeton Optimization Seminar. [slides]
• ML for Rover Path Planning: check out this video of our MLNav extension of ENav (the path planner currently on the Mars Perseverance Rover). [arxiv]
  
• NSF Expeditions 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.