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.

My fundamental research is largely focused on the following areas:

• Interactive Machine Learning, which pertains to settings where the system must repeatedly interact with the environment (which might be a human), and possibly learn from collecting data through interactions. Includes settings such as active learning, Bayesian optimization, bandits, reinforcement learning, and experiment design.
• Structured Machine Learning, which pertains to settings where we are modeling complex phenomena whose ambient representation is exponentially or infinitely large. Structure refers to mathematical abstractions that constrain the space to be more tractable both statistically and computationally. Common research directions include integrating learning with other domains such as physics, control, and logic, as well as unifying the respective theories.

[curriculum vitae]

Note that focus areas are typically overlapping, and many papers span multiple focus areas.

• AI for Science -- we aim to leverage learning approaches to improve workflows in science and accelerate knowledge discovery.
  More Info

  • BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos
    Jennifer J. Sun, Pierre Karashchuk, Amil Dravid, Serim Ryou, Sonia Fereidooni, John Tuthill, Aggelos Katsaggelos, Bingni W. Brunton, Georgia Gkioxari, Ann Kennedy, Yisong Yue, Pietro Perona
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
    [arxiv]
  • Neurosymbolic Programming for Science
    Jennifer J. Sun, Megan Tjandrasuwita, Atharva Sehgal, Armando Solar-Lezama, Swarat Chaudhuri, Yisong Yue, Omar Costilla-Reyes
    [arxiv]
  • The MABe22 Benchmarks for Representation Learning of Multi-Agent Behavior
    Jennifer J. Sun, Andrew Ulmer, Dipam Chakraborty, Brian Geuther, Edward Hayes, Heng Jia, Vivek Kumar, Zachary Partridge, Alice Robie, Catherine E. Schretter, Chao Sun, Keith Sheppard, Param Uttarwar, Pietro Perona, Yisong Yue, Kristin Branson, Ann Kennedy
    [arxiv]
  • Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings
    Sabera Talukder, Jennifer J. Sun, Matthew Leonard, Bingni W. Brunton, Yisong Yue
    [arxiv]
  • Self-Supervised Keypoint Discovery in Behavioral Videos
    Jennifer J. Sun*, Serim Ryou*, Roni Goldshmid, Brandon Weissbourd, John Dabiri, David J. Anderson, Ann Kennedy, Yisong Yue, Pietro Perona
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2022.
    [arxiv]
  • Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis
    Albert Tseng, Jennifer J. Sun, Yisong Yue
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2022.
    [arxiv]
  • Interpreting Expert Annotation Differences in Animal Behavior
    Megan Tjandrasuwita, Jennifer J. Sun, Ann Kennedy, Swarat Chaudhuri, Yisong Yue
    CVPR Workshop on CV4Animals, June 2022.
    [arxiv]
  • DeepGEM: Generalized Expectation-Maximization for Blind Inversion
    Angela Gao, Jorge Castellanos, Yisong Yue, Zachary Ross, Katherine Bouman
    Neural Information Processing Systems (NeurIPS), December 2021.
    [pdf][code][project]
  • The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions
    Jennifer J. Sun, Tomomi Karigo, Dipam Chakraborty, Sharada P. Mohanty, Benjamin Wild, Quan Sun, Chen Chen, David J. Anderson, Pietro Perona, Yisong Yue, Ann Kennedy
    Neural Information Processing Systems (NeurIPS), December 2021.
    [arxiv][dataset][code][challenge]
  • Informed training set design enables efficient machine learning-assisted directed protein evolution
    Bruce Wittmann, Yisong Yue, Frances Arnold
    Cell Systems, August 2021.
    [online][bioRxiv][code]
  • Fine-Grained System Identification of Nonlinear Neural Circuits
    Dawna Bagherian, James Gornet, Jeremy Bernstein, Yu-Li Ni, Yisong Yue, Markus Meister
    ACM Conference on Knowledge Discovery and Data Mining (KDD), August 2021.
    [pdf][arxiv][code]
  • Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments
    Logan Cross, Jeff Cockburn, Yisong Yue, John O’Doherty
    Neuron, February 2021.
    [online][press release]
  • Multi-Label Classification Models for the Prediction of Cross-Coupling Reaction Conditions
    Michael Maser, Alexander Cui, Serim Ryou, Travis DeLano, Yisong Yue, Sarah Reisman
    Journal of Chemical Information and Modeling (JCIM), January 2021.
    [online][ChemRxiv]
  • Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions
    Serim Ryou, Michael R. Maser, Alexander Y. Cui, Travis J. DeLano, Yisong Yue, Sarah E. Reisman
    ICML 2020 Workshop on Graph Representation Learning and Beyond, July 2020.
    [arxiv]
  • PhaseLink: A Deep Learning Approach to Seismic Phase Association
    Zachary Ross, Yisong Yue, Men-Andrin Meier, Egill Hauksson, Thomas Heaton
    Journal of Geophysical Research - Solid Earth, DOI:0.1029/2018JB016674R, January 2019.
    [online][arxiv]
  • Reliable Real-time Seismic Signal/Noise Discrimination with Machine Learning
    Men-Andrin Meier, Zachary Ross, Anshul Ramachandran, Ashwin Balakrishna, Suraj Nair, Peter Kundzicz, Zefeng Li, Jennifer Andrews, Egill Hauksson, Yisong Yue
    Journal of Geophysical Research - Solid Earth, DOI:0.1029/2018JB016661, January 2019.
    [online][arxiv]
  • Factorized Variational Autoencoders for Modeling Audience Reactions to Movies
    Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain Matthews, Greg Mori
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July, 2017.
    [pdf][press release][radio interview]
  • Data-Driven Ghosting using Deep Imitation Learning
    Hoang M. Le, Peter Carr, Yisong Yue, Patrick Lucey
    MIT Sloan Sports Analytics Conference (SSAC), March, 2017.
    (Best Paper Nomination)
    [pdf][project][demo video][press release]
  • Identifying Team Style in Soccer using Formations Learned from Spatiotemporal Tracking Data
    Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue, Sridha Sridharan, Iain Matthews
    ICDM 2014 International Workshop on Spatial and Spatio-temporal Data Mining (SSTDM), December, 2014.
    [pdf]
  • Large-Scale Analysis of Soccer Matches using Spatiotemporal Tracking Data
    Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue, Sridha Sridharan, Iain Matthews
    IEEE International Conference on Data Mining (ICDM), December, 2014.
    [pdf]
  • "Win at Home and Draw Away": Automatic Formation Analysis Highlighting the Differences in Home and Away Team Behaviors
    Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue, Iain Matthews
    MIT Sloan Sports Analytics Conference (SSAC), February, 2014.
    [pdf]
  • "How to Get an Open Shot": Analyzing Team Movement in Basketball using Tracking Data
    Patrick Lucey, Alina Bialkowski, Peter Carr, Yisong Yue, Iain Matthews
    MIT Sloan Sports Analytics Conference (SSAC), February, 2014.
    [pdf]
• Adaptive Experiment Design -- we aim to develop adaptive algorithms for experiment design inspired by applications to science and engineering. Research questions include balancing the exploration/exploitation trade-off subject to constraints (e.g., safety), and between different types of experiments.
  More Info

  • End-to-End Sequential Sampling and Reconstruction for MR Imaging
    Tianwei Yin*, Zihui Wu*, He Sun, Adrian V. Dalca, Yisong Yue, Katherine L. Bouman
    Machine Learning for Health (ML4H), December 2021.
    (Best Paper Award)
    [arxiv][project]
  • Informed training set design enables efficient machine learning-assisted directed protein evolution
    Bruce Wittmann, Yisong Yue, Frances Arnold
    Cell Systems, August 2021.
    [online][bioRxiv][code]
  • ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
    Kejun Li, Maegan Tucker, Erdem Bıyık, Ellen Novoseller, Joel W. Burdick, Yanan Sui, Dorsa Sadigh, Yisong Yue, Aaron D. Ames
    International Conference on Robotics and Automation (ICRA), May 2021.
    [arxiv][video]
  • 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]
  • Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
    Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
    IEEE Robotics and Automation Letters (RA-L), April 2021.
    [arxiv]
  • Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits
    Maegan Tucker, Myra Cheng, Ellen Novoseller, Richard Cheng, Yisong Yue, Joel W. Burdick, Aaron D. Ames
    International Conference on Intelligent Robots and Systems (IROS), October 2020.
    [arxiv]
  • Robust Regression for Safe Exploration in Control
    Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue
    Conference on Learning for Dynamics and Control (L4DC), June 2020.
    [arxiv]
  • 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]
  • Mirrored Plasmonic Filter Design via Active Learning of Multi-Fidelity Physical Models
    Jialin Song, Yury S Tokpanov, Yuxin Chen, Dagny Fleischman, Katherine T Fountaine, Yisong Yue, Harry A Atwater
    IEEE Conference on Lasers and Electro-Optics (CLEO), May 2020.
    [online]
  • 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]
  • Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design
    Kevin Yang, Yuxin Chen, Alycia Lee, Yisong Yue
    International Conference on Artificial Intelligence and Statistics (AISTATS), April 2019.
    [pdf][arxiv]
  • Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes
    Jialin Song, Yury S. Tokpanov, Yuxin Chen, Dagny Fleischman, Kate T. Fountaine, Harry A. Atwater, Yisong Yue
    NeurIPS 2018 Workshop on Machine Learning for Molecules and Materials, December 2018.
    [arxiv]
  • 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]
  • Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces
    Yanan Sui, Yisong Yue, Joel Burdick
    International Joint Conference on Artificial Intelligence (IJCAI), August 2017.
    [pdf][arxiv]
  • 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]
• Learning to Optimize -- we aim to learn customized solvers to tackle focused distributions of optimization problems. Our interests span both continuous (e.g., amortized optimization) and discrete optimization (e.g., learning to branch-and-bound), with applications to planning and inverse problems.
  More Info

  • Learning Pseudo-Backdoors for Mixed Integer Programs
    Aaron Ferber, Jialin Song, Bistra Dilkina, Yisong Yue
    International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), June 2022.
    [arxiv]
  • MLNav: Learning to Safely Navigate on Martian Terrains
    Shreyansh Daftry, Neil Abcouwer, Tyler Del Sesto, Siddarth Venkatraman, Jialin Song, Lucas Igel, Amos Byon, Ugo Rosolia, Yisong Yue, Masahiro Ono
    IEEE Robotics and Automation Letters (RA-L), May 2022
    [conference][journal][video]
  • End-to-End Sequential Sampling and Reconstruction for MR Imaging
    Tianwei Yin*, Zihui Wu*, He Sun, Adrian V. Dalca, Yisong Yue, Katherine L. Bouman
    Machine Learning for Health (ML4H), December 2021.
    (Best Paper Award)
    [arxiv][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]
  • Machine Learning Based Path Planning for Improved Rover Navigation
    Neil Abcouwer, Shreyansh Daftry, Siddarth Venkatraman, Tyler del Sesto, Olivier Toupet, Ravi Lanka, Jialin Song, Yisong Yue, Masahiro Ono
    IEEE Aerospace Conference (AeroConf), March 2021.
    [arxiv][video]
  • Iterative Amortized Policy Optimization
    Joseph Marino, Alexandre Piché, Alessandro Davide Ialongo, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December 2021.
    [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]
  • Learning to Search via Retrospective Imitation
    Jialin Song, Ravi Lanka, Albert Zhao, Aadyot Bhatnagar, Yisong Yue, Masahiro Ono
    [arxiv]
  • GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning
    Benjamin Rivière, Wolfgang Hoenig, Yisong Yue, Soon-Jo Chung
    IEEE Robotics and Automation Letters (RA-L), June 2020.
    (Best Paper Nomination)
    [pdf][arxiv][demo video][press release]
  • 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]
  • A General Method for Amortizing Variational Filtering
    Joseph Marino, Milan Cvitkovic, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December 2018.
    [pdf][arxiv][code]
  • Iterative Amortized Inference
    Joseph Marino, Yisong Yue, Stephan Mandt
    International Conference on Machine Learning (ICML), July 2018.
    [pdf][arxiv][code]
  • Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization
    Jiaji Zhou, Stephane Ross, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell
    ICML Workshop on Inferning: Interactions between Inference and Learning, June, 2013.
    [pdf][software]
  • Learning Policies for Contextual Submodular Prediction
    Stephane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell
    International Conference on Machine Learning (ICML), June, 2013.
    [pdf][software][video]
• Learning & Control -- we aim to develop integrated algorithmic frameworks that unify learning theory and control theory.
  More Info

  • Online Adaptive Controller Selection in Time-Varying Systems: No-Regret via Contractive Perturbations
    Yiheng Lin, James Preiss, Emile Anand, Yingying Li, Yisong Yue, Adam Wierman
    [arxiv]
  • End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functions
    Ryan Cosner, Yisong Yue, Aaron D. Ames
    IEEE Conference on Decision and Control (CDC), December, 2022.
    [pdf]
  • Safety of Sampled-Data Systems with Control Barrier Functions via Approximate Discrete Time Models
    Andrew J. Taylor, Victor D. Dorobantu, Ryan K. Cosner, Yisong Yue, Aaron D. Ames
    IEEE Conference on Decision and Control (CDC), December, 2022.
    [arxiv]
  • Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies
    Ivan Dario Jimenez Rodriguez*, Noel Csomay-Shanklin*, Yisong Yue, Aaron D. Ames
    Conference on Learning for Dynamics and Control (L4DC), June 2022.
    [arxiv][video]
  • LyaNet: A Lyapunov Framework for Training Neural ODEs
    Ivan Dario Jimenez Rodriguez, Aaron D. Ames, Yisong Yue
    International Conference on Machine Learning (ICML), July 2022.
    [arxiv][code]
  • The Mixed-Observable Constrained Linear Quadratic Regulator Problem: the Exact Solution and Practical Algorithms
    Ugo Rosolia, Yuxiao Chen, Shreyansh Daftry, Masahiro Ono, Yisong Yue, Aaron D. Ames
    IEEE Transactions on Automation Control (TAC), Technical Note, 2022.
    [arxiv][online]
  • Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds
    Michael O’Connell*, Guanya Shi*, Xichen Shi, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
    Science Robotics, May 2022.
    [arxiv][online][code][video][press release]
  • Meta-Adaptive Nonlinear Control: Theory and Algorithms
    Guanya Shi, Kamyar Azizzadenesheli, Michael O'Connell, Soon-Jo Chung, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December 2021.
    [arxiv][code]
  • Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty
    Andrew J. Taylor*, Victor D. Dorobantu*, Sarah Dean*, Benjamin Recht, Yisong Yue, Aaron D. Ames
    IEEE Conference on Decision and Control (CDC), December 2021.
    [arxiv]
  • Competitive Control with Delayed Imperfect Information
    Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman
    American Control Conference (ACC), June 2022.
    [arxiv]
  • Sampled-Data Stabilization with Control Lyapunov Functions via Quadratically Constrained Quadratic Programs
    Andrew J. Taylor*, Victor D. Dorobantu*, Yisong Yue, Paulo Tabuada, Aaron D. Ames
    IEEE Control Systems Letters (L-CSS), June 2021.
    [arxiv]
  • Online Robust Control of Nonlinear Systems with Large Uncertinaty
    Dimitar Ho, Hoang M. Le, John Doyle, Yisong Yue
    International Conference on Artificial Intelligence and Statistics (AISTATS), April 2021.
    [pdf][arxiv]
  • Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
    Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
    IEEE Robotics and Automation Letters (RA-L), April 2021.
    [arxiv]
  • The Power of Predictions in Online Control
    Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman
    Neural Information Processing Systems (NeurIPS), December 2020.
    [arxiv]
  • Online Optimization with Memory and Competitive Control
    Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman
    Neural Information Processing Systems (NeurIPS), December 2020.
    [arxiv]
  • A Control Barrier Perspective on Episodic Learning via Projection-to-State Safety
    Andrew J. Taylor, Andrew Singletary, Yisong Yue, Aaron D. Ames
    IEEE Control Systems Letters (L-CSS), July 2020.
    [arxiv]
  • Robust Regression for Safe Exploration in Control
    Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue
    Conference on Learning for Dynamics and Control (L4DC), June 2020.
    [arxiv]
  • Learning for Safety-Critical Control with Control Barrier Functions
    Andrew Taylor, Andrew Singletary, Yisong Yue, Aaron Ames
    Conference on Learning for Dynamics and Control (L4DC), June 2020.
    [arxiv]
  • A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability
    Andrew J. Taylor*, Victor D. Dorobantu*, Meera Krishnamoorthy, Hoang M. Le, Yisong Yue, Aaron D. Ames
    IEEE Conference on Decision and Control (CDC), December 2019.
    [pdf][arxiv]
  • Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems
    Andrew J. Taylor*, Victor D. Dorobantu*, Hoang M. Le, Yisong Yue, Aaron D. Ames
    International Conference on Intelligent Robots and Systems (IROS), November 2019.
    [pdf][arxiv][code]
  • Control Regularization for Reduced Variance Reinforcement Learning
    Richard Cheng, Abhinav Verma, Gabor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdick
    International Conference on Machine Learning (ICML), June 2019.
    [pdf][arxiv][code]
  • 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][code]
• Certifiable Learning -- we aim to develop learning approaches with certifiable guarantees such as safety, robustness, or stability.
  More Info

  • Policy Optimization with Linear Temporal Logic Constraints
    Cameron Voloshin, Hoang M. Le, Swarat Chaudhuri, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December, 2022.
    [arxiv]
  • Meta-Adaptive Nonlinear Control: Theory and Algorithms
    Guanya Shi, Kamyar Azizzadenesheli, Michael O'Connell, Soon-Jo Chung, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December 2021.
    [arxiv][code]
  • Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty
    Andrew J. Taylor*, Victor D. Dorobantu*, Sarah Dean*, Benjamin Recht, Yisong Yue, Aaron D. Ames
    IEEE Conference on Decision and Control (CDC), December 2021.
    [arxiv]
  • 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]
  • Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
    Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
    IEEE Robotics and Automation Letters (RA-L), April 2021.
    [arxiv]
  • A Control Barrier Perspective on Episodic Learning via Projection-to-State Safety
    Andrew J. Taylor, Andrew Singletary, Yisong Yue, Aaron D. Ames
    IEEE Control Systems Letters (L-CSS), July 2020.
    [arxiv]
  • GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning
    Benjamin Rivière, Wolfgang Hoenig, Yisong Yue, Soon-Jo Chung
    IEEE Robotics and Automation Letters (RA-L), June 2020.
    (Best Paper Nomination)
    [pdf][arxiv][demo video]
  • Robust Regression for Safe Exploration in Control
    Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue
    Conference on Learning for Dynamics and Control (L4DC), June 2020.
    [arxiv]
  • Learning for Safety-Critical Control with Control Barrier Functions
    Andrew Taylor, Andrew Singletary, Yisong Yue, Aaron Ames
    Conference on Learning for Dynamics and Control (L4DC), June 2020.
    [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][demo video]
  • A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability
    Andrew J. Taylor*, Victor D. Dorobantu*, Meera Krishnamoorthy, Hoang M. Le, Yisong Yue, Aaron D. Ames
    IEEE Conference on Decision and Control (CDC), December 2019.
    [pdf][arxiv]
  • Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems
    Andrew J. Taylor*, Victor D. Dorobantu*, Hoang M. Le, Yisong Yue, Aaron D. Ames
    International Conference on Intelligent Robots and Systems (IROS), November 2019.
    [pdf][arxiv][code]
  • Barrier Certificates for Assured Machine Teaching
    Mohamadreza Ahmadi, Bo Wu, Yuxin Chen, Yisong Yue, Ufuk Topcu
    American Control Conference (ACC), July 2019.
    [pdf][arxiv]
    
  • Detecting Adversarial Examples via Neural Fingerprinting
    Sumanth Dathathri, Stephan Zheng, Tianwei Yin, Richard M. Murray, Yisong Yue
    [arxiv][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]
  • Control Regularization for Reduced Variance Reinforcement Learning
    Richard Cheng, Abhinav Verma, Gabor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdick
    International Conference on Machine Learning (ICML), June 2019.
    [pdf][arxiv][code]
  • 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]
  • 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]
  • Safe Exploration and Optimization of Constrained MDPs using Gaussian Processes
    Akifumi Wachi, Yanan Sui, Yisong Yue, Masahiro Ono
    AAAI Conference on Artificial Intelligence (AAAI), February 2018.
    [pdf]
  • 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][code]
• Neurosymbolic & Program Learning -- we aim to develop learning approaches that leverage symbolic primitives, i.e., programming languages. Such functions can be more interpretable, convenient for specifying domain knowledge, data efficient, and amenable to formal verification. I am also involved with this project.
  More Info

  • Eventual Discounting Temporal Logic Counterfactual Experience Replay
    Cameron Voloshin, Abhinav Verma, Yisong Yue
    [arxiv]
  • Neurosymbolic Programming for Science
    Jennifer J. Sun, Megan Tjandrasuwita, Atharva Sehgal, Armando Solar-Lezama, Swarat Chaudhuri, Yisong Yue, Omar Costilla-Reyes
    [arxiv]
  • Policy Optimization with Linear Temporal Logic Constraints
    Cameron Voloshin, Hoang M. Le, Swarat Chaudhuri, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December, 2022.
    [arxiv]
  • Neurosymbolic Programming
    Swarat Chaudhuri, Kevin Ellis, Oleksandr Polozov, Rishabh Singh, Armando Solar-Lezama, Yisong Yue
    Foundations and Trends in Programming Languages, Volume 7: No. 3, pages 158-243, December 2021.
    [preprint][online]
  • Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis
    Albert Tseng, Jennifer J. Sun, Yisong Yue
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2022.
    [arxiv]
  • Interpreting Expert Annotation Differences in Animal Behavior
    Megan Tjandrasuwita, Jennifer J. Sun, Ann Kennedy, Swarat Chaudhuri, Yisong Yue
    CVPR Workshop on CV4Animals, June 2022.
    [arxiv]
  • Unsupervised Learning of Neurosymbolic Encoders
    Eric Zhan*, Jennifer J. Sun*, Ann Kennedy, Yisong Yue, Swarat Chaudhuri
    Transactions of Machine Learning Research (TMLR), 2022.
    [arxiv][code]
  • 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 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]
  • 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]
  • 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]
  • 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.
    [pdf][arxiv][demo video][code]
• Robotics & Robot Learning -- we aim to develop learning methods for real robotics applications. Domains of interest include low-level control, high-level planning, robotic swarms, perception, and human-robot interaction.
  More Info

  • POLAR: Preference Optimization and Learning Algorithms for Robotics
    Maegan Tucker, Kejun Li, Yisong Yue, Aaron D. Ames
    [arxi]
  • Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies
    Ivan Dario Jimenez Rodriguez*, Noel Csomay-Shanklin*, Yisong Yue, Aaron D. Ames
    Conference on Learning for Dynamics and Control (L4DC), June 2022.
    [arxiv][video]
  • Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds
    Michael O’Connell*, Guanya Shi*, Xichen Shi, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
    Science Robotics, May 2022.
    [arxiv][online][code][video][press release]
  • MLNav: Learning to Safely Navigate on Martian Terrains
    Shreyansh Daftry, Neil Abcouwer, Tyler Del Sesto, Siddarth Venkatraman, Jialin Song, Lucas Igel, Amos Byon, Ugo Rosolia, Yisong Yue, Masahiro Ono
    IEEE Robotics and Automation Letters (RA-L), May 2022
    [conference][journal][video]
  • Self-Supervised Online Learning for Safety-Critical Control using Stereo Vision
    Ryan Cosner*, Ivan Dario Jimenez Rodriguez*, Tamas G. Molnar, Wyatt Ubellacker, Yisong Yue, Katherine Bouman, Aaron Ames
    International Conference on Robotics and Automation (ICRA), May 2022
    [arxiv]
  • Safety-Aware Preference-Based Learning for Safety-Critical Control
    Ryan K. Cosner, Maegan Tucker, Andrew J. Taylor, Kejun Li, Tamás G. Molnár, Wyatt Ubellacker, Anil Alan, Gábor Orosz, Yisong Yue, Aaron D. Ames
    Conference on Learning for Dynamics and Control (L4DC), June 2022.
    [arxiv]
  • Natural Multicontact Walking for Robotic Assistive Devices via Musculoskeletal Models and Hybrid Zero Dynamics
    Kejun Li, Maegan Tucker, Rachel Gehlhar, Yisong Yue, Aaron D. Ames
    IEEE Robotics and Automation Letters (RA-L), May 2022
    [arxiv][demo video]
  • Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control
    Ivan D. Jimenez Rodriguez, Ugo Rosolia, Aaron D. Ames, Yisong Yue
    International Conference on Intelligent Robots and Systems (IROS), September 2021.
    [arxiv][demo video]
  • 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]
  • Machine Learning Based Path Planning for Improved Rover Navigation
    Neil Abcouwer, Shreyansh Daftry, Siddarth Venkatraman, Tyler del Sesto, Olivier Toupet, Ravi Lanka, Jialin Song, Yisong Yue, Masahiro Ono
    IEEE Aerospace Conference (AeroConf), March 2021.
    [arxiv][video]
  • ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
    Kejun Li, Maegan Tucker, Erdem Bıyık, Ellen Novoseller, Joel W. Burdick, Yanan Sui, Dorsa Sadigh, Yisong Yue, Aaron D. Ames
    International Conference on Robotics and Automation (ICRA), May 2021.
    [arxiv][video]
  • Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
    Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
    IEEE Robotics and Automation Letters (RA-L), April 2021.
    [arxiv]
  • Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits
    Maegan Tucker, Myra Cheng, Ellen Novoseller, Richard Cheng, Yisong Yue, Joel W. Burdick, Aaron D. Ames
    International Conference on Intelligent Robots and Systems (IROS), October 2020.
    [arxiv]
  • GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning
    Benjamin Rivière, Wolfgang Hoenig, Yisong Yue, Soon-Jo Chung
    IEEE Robotics and Automation Letters (RA-L), June 2020.
    (Best Paper Nomination)
    [pdf][arxiv][demo video][press release]
  • Learning for Safety-Critical Control with Control Barrier Functions
    Andrew Taylor, Andrew Singletary, Yisong Yue, Aaron Ames
    Conference on Learning for Dynamics and Control (L4DC), June 2020.
    [arxiv]
  • Robust Regression for Safe Exploration in Control
    Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue
    Conference on Learning for Dynamics and Control (L4DC), June 2020.
    [arxiv]
  • Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions
    Guanya Shi, Wolfgang Hoenig, Yisong Yue, Soon-Jo Chung
    International Conference on Robotics and Automation (ICRA), May 2020.
    [pdf][arxiv][demo video][press release]
  • 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]
  • Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems
    Andrew J. Taylor*, Victor D. Dorobantu*, Hoang M. Le, Yisong Yue, Aaron D. Ames
    International Conference on Intelligent Robots and Systems (IROS), November 2019.
    [pdf][arxiv][code]
  • 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]
• Distribution Shift -- we aim to develop methods for training and evaluating models under distribution shift. This setting often arises in real-world systems with large amounts of pre-collected historical data. Research directions include counterfactual analysis and distributionally robust learning.
  More Info

  • Distributionally Robust Learning for Unsupervised Domain Adaptation
    Haoxuan Wang, Anqi Liu, Zhiding Yu, Yisong Yue, Anima Anandkumar
    [arxiv]
  • Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning
    Cameron Voloshin, Hoang M. Le, Nan Jiang, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December 2021.
    [arxiv][code]
  • Minimax Model Learning
    Cameron Voloshin, Nan Jiang, Yisong Yue
    International Conference on Artificial Intelligence and Statistics (AISTATS), April 2021.
    [pdf][arxiv]
  • Active Learning under Label Shift
    Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue
    International Conference on Artificial Intelligence and Statistics (AISTATS), April 2021.
    [arxiv]
  • Triply Robust Off-Policy Evaluation
    Anqi Liu, Hao Liu, Anima Anandkumar, Yisong Yue
    [arxiv]
  • Robust Regression for Safe Exploration in Control
    Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue
    Conference on Learning for Dynamics and Control (L4DC), June 2020.
    [arxiv]
  • 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]
• Core Learning Algorithms & Analysis -- we aim to develop fundamental understanding of the structure of optimization and generalization in learning settings, and develop principled algorithms that leverage this structure. Some structures are extracted from the data domain, and others are imposed through inspiration from biological learning.
  More Info

  • Conformal Generative Modeling on Triangulated Surfaces
    Victor Dorobantu, Charlotte Borcherds, Yisong Yue
    [arxiv][project][code]
  • FI-ODE: Certified and Robust Forward Invariance in Neural ODEs
    Yujia Huang, Ivan Dario Jimenez Rodriguez, Huan Zhang, Yuanyuan Shi, Yisong Yue
    [arxiv][code]
  • Online Adaptive Controller Selection in Time-Varying Systems: No-Regret via Contractive Perturbations
    Yiheng Lin, James Preiss, Emile Anand, Yingying Li, Yisong Yue, Adam Wierman
    [arxiv]
  • Compactly Restrictable Metric Policy Optimization Problems
    Victor D. Dorobantu, Kamyar Azizzadenesheli, Yisong Yue
    IEEE Transactions on Automation Control (TAC), Technical Note, 2022.
    [arxiv]
  • Investigating Generalization by Controlling Normalized Margin
    Alexander Farhang, Jeremy Bernstein, Kushal Tirumala, Yang Liu, Yisong Yue
    International Conference on Machine Learning (ICML), July 2022.
    [arxiv][code]
  • Kernel Interpolation as a Bayes Point Machine
    Jeremy Bernstein, Alex Farhang, Yisong Yue
    [arxiv]
  • Computing the Information Content of Trained Neural Networks
    Jeremy Bernstein, Yisong Yue
    [arxiv][code]
  • Architecture Agnostic Neural Networks
    Sabera Talukder, Guruprasad Raghavan, Yisong Yue
    [arxiv]
  • Fine-Grained System Identification of Nonlinear Neural Circuits
    Dawna Bagherian, James Gornet, Jeremy Bernstein, Yu-Li Ni, Yisong Yue, Markus Meister
    ACM Conference on Knowledge Discovery and Data Mining (KDD), August 2021.
    [pdf][arxiv][code]
  • Competitive Policy Optimization
    Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar
    Conference on Uncertainty in Artificial Intelligence (UAI), July 2021.
    [arxiv]
  • Learning by Turning: Neural Architecture Aware Optimisation
    Yang Liu*, Jeremy Bernstein*, Markus Meister, Yisong Yue
    International Conference on Machine Learning (ICML), July 2021.
    [arxiv][code]
  • Deep Bayesian Quadrature Policy Optimization
    Akella Ravi Tej, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Anima Anandkumar, Yisong Yue
    [arxiv][code] AAAI Conference on Artificial Intelligence (AAAI), February 2021.
    
  • Learning compositional functions via multiplicative weight updates
    Jeremy Bernstein, Jiawei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December 2020.
    [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]
  • Online Optimization with Memory and Competitive Control
    Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman
    Neural Information Processing Systems (NeurIPS), December, 2020.
    [arxiv]
  • Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
    Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, Rose Yu
    International Conference on Machine Learning (ICML), July 2020.
    [pdf][arxiv][code]
  • 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]
• Structured Policy Learning -- we aim to develop policy learning algorithms (both imitation and reinforcement) that can incorporate structural assumptions to improve the effectiveness of learning. Examples include blending with graphical models to encode conditional independence assumptions, blending with model-based controllers to ensure various stability properties, and hierarchical learning to plan over long time horizons.
  More Info

  • Eventual Discounting Temporal Logic Counterfactual Experience Replay
    Cameron Voloshin, Abhinav Verma, Yisong Yue
    [arxiv]
  • Policy Optimization with Linear Temporal Logic Constraints
    Cameron Voloshin, Hoang M. Le, Swarat Chaudhuri, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December, 2022.
    [arxiv]
  • Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies
    Ivan Dario Jimenez Rodriguez*, Noel Csomay-Shanklin*, Yisong Yue, Aaron D. Ames
    Conference on Learning for Dynamics and Control (L4DC), June 2022.
    [arxiv][video]
  • End-to-End Sequential Sampling and Reconstruction for MR Imaging
    Tianwei Yin*, Zihui Wu*, He Sun, Adrian V. Dalca, Yisong Yue, Katherine L. Bouman
    Machine Learning for Health (ML4H), December 2021.
    (Best Paper Award)
    [arxiv][project]
  • Competitive Policy Optimization
    Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar
    Conference on Uncertainty in Artificial Intelligence (UAI), July 2021.
    [arxiv]
  • 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 Optimization with Memory and Competitive Control
    Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman
    Neural Information Processing Systems (NeurIPS), December, 2020.
    [arxiv]
  • Learning to Search via Retrospective Imitation
    Jialin Song, Ravi Lanka, Albert Zhao, Aadyot Bhatnagar, Yisong Yue, Masahiro Ono
    [arxiv]
  • 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]
  • GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning
    Benjamin Rivière, Wolfgang Hoenig, Yisong Yue, Soon-Jo Chung
    IEEE Robotics and Automation Letters (RA-L), June 2020.
    (Best Paper Nomination)
    [pdf][arxiv][demo video][press release]
  • 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]
  • Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems
    Andrew J. Taylor*, Victor D. Dorobantu*, Hoang M. Le, Yisong Yue, Aaron D. Ames
    International Conference on Intelligent Robots and Systems (IROS), November 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][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]
  • Control Regularization for Reduced Variance Reinforcement Learning
    Richard Cheng, Abhinav Verma, Gabor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdick
    International Conference on Machine Learning (ICML), June 2019.
    [pdf][arxiv][code]
  • 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.
    [pdf][arxiv][demo video][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]
  • Coordinated Multi-Agent Imitation Learning
    Hoang M. Le, Yisong Yue, Peter Carr, Patrick Lucey
    International Conference on Machine Learning (ICML), August 2017.
    [pdf][arxiv][data][press release]
    
  • Learning recurrent representations for hierarchical behavior modeling
    Eyrun Eyjolfsdottir, Kristin Branson, Yisong Yue, Pietro Perona
    International Conference on Learning Representations (ICLR), April, 2017.
    [pdf][arxiv][supplementary]
  • Data-Driven Ghosting using Deep Imitation Learning
    Hoang M. Le, Peter Carr, Yisong Yue, Patrick Lucey
    MIT Sloan Sports Analytics Conference (SSAC), March, 2017.
    (Best Paper Nomination)
    [pdf][project][demo video][press release]
  • Generating Long-term Trajectories Using Deep Hierarchical Networks
    Stephan Zheng, Yisong Yue, Patrick Lucey
    Neural Information Processing Systems (NeurIPS), December, 2016.
    [pdf][data]
  • Learning Online Smooth Predictors for Real-time Camera Planning using Recurrent Decision Trees
    Jianhui Chen, Hoang M. Le, Peter Carr, Yisong Yue, James J. Little
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
    (Oral Presentation)
    [pdf][press release][Sports Illustrated][DataScience.com][code]
  • 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][code]
  • Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization
    Jiaji Zhou, Stephane Ross, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell
    ICML Workshop on Inferning: Interactions between Inference and Learning, June, 2013.
    [pdf][software]
  • Learning Policies for Contextual Submodular Prediction
    Stephane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell
    International Conference on Machine Learning (ICML), June, 2013.
    [pdf][software][video]
• Latent Variable & Representation Learning -- we aim to develop learning approaches that extract latent structure from raw input data. Problems of interest include learning from indirect measurements and side information, as well as improving learning and optimization algorithms.
  More Info

  • BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos
    Jennifer J. Sun, Pierre Karashchuk, Amil Dravid, Serim Ryou, Sonia Fereidooni, John Tuthill, Aggelos Katsaggelos, Bingni W. Brunton, Georgia Gkioxari, Ann Kennedy, Yisong Yue, Pietro Perona
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
    [arxiv]
  • Self-Supervised Keypoint Discovery in Behavioral Videos
    Jennifer J. Sun*, Serim Ryou*, Roni Goldshmid, Brandon Weissbourd, John Dabiri, David J. Anderson, Ann Kennedy, Yisong Yue, Pietro Perona
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2022.
    [arxiv]
  • 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]
  • On the Benefits of Early Fusion in Multimodal Representation Learning
    George Barnum, Sabera Talukder, Yisong Yue
    [arxiv]
  • Learning Invariant Representation of Tasks for Robust Surgical State Estimation
    Yidan Qin, Max Allan, Yisong Yue, Joel Burdick, Mahdi Azizian
    IEEE Robotics and Automation Letters (RA-L), 2021.
    [arxiv]
  • Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions
    Serim Ryou, Michael R. Maser, Alexander Y. Cui, Travis J. DeLano, Yisong Yue, Sarah E. Reisman
    ICML 2020 Workshop on Graph Representation Learning and Beyond, July 2020.
    [arxiv]
  • Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
    Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, Rose Yu
    International Conference on Machine Learning (ICML), July 2020.
    [pdf][arxiv][code]
  • An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing
    Baihong Jin, Yingshui Tan, Alexander Nettekoven, Yuxin Chen, Ufuk Topcu, Yisong Yue, Alberto Sangiovanni Vincentelli
    IEEE International Conference on Machine Learning and Applications (ICMLA), December 2019.
    [arxiv]
  • Landmark Ordinal Embedding
    Nikhil Ghosh, Yuxin Chen, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December 2019.
    [pdf][arxiv]
  • A General Method for Amortizing Variational Filtering
    Joseph Marino, Milan Cvitkovic, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December 2018.
    [pdf][arxiv][code]
  • Iterative Amortized Inference
    Joseph Marino, Yisong Yue, Stephan Mandt
    International Conference on Machine Learning (ICML), July 2018.
    [pdf][arxiv][code]
  • Learning recurrent representations for hierarchical behavior modeling
    Eyrun Eyjolfsdottir, Kristin Branson, Yisong Yue, Pietro Perona
    International Conference on Learning Representations (ICLR), April, 2017.
    [pdf][arxiv][supplementary]
  • A Rotation Invariant Latent Factor Model for Moveme Discovery from Static Poses
    Matteo Ronchi, Joon Sik Kim, Yisong Yue
    IEEE International Conference on Data Mining (ICDM), December, 2016.
    [pdf][project]
  • Factorized Variational Autoencoders for Modeling Audience Reactions to Movies
    Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain Matthews, Greg Mori
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July, 2017.
    [pdf][press release][radio interview]
  • Learning Fine-Grained Spatial Models for Dynamic Sports Play Prediction
    Yisong Yue, Patrick Lucey, Peter Carr, Alina Bialkowski, Iain Matthews
    IEEE International Conference on Data Mining (ICDM), December, 2014.
    (Best Paper Nomination)
    [pdf][demo][press release]
  • Personalized Collaborative Clustering
    Yisong Yue, Chong Wang, Khalid El-Arini, Carlos Guestrin
    International World Wide Web Conference (WWW), April, 2014.
    [pdf][slides][data]
• Spatiotemporal Sequence Modeling -- we aim to learn models that can reason over complex spatiotemporal sequences. Problems of interest include forecasting, generation, and conditioning on complex contextual inputs.
  More Info

  • Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings
    Sabera Talukder, Jennifer J. Sun, Matthew Leonard, Bingni W. Brunton, Yisong Yue
    [arxiv]
  • An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing
    Baihong Jin, Yingshui Tan, Alexander Nettekoven, Yuxin Chen, Ufuk Topcu, Yisong Yue, Alberto Sangiovanni Vincentelli
    IEEE International Conference on Machine Learning and Applications (ICMLA), December 2019.
    [arxiv]
  • NAOMI: Non-Autoregressive Multiresolution Sequence Imputation
    Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December 2019.
    [pdf][arxiv][code][demo video]
  • PhaseLink: A Deep Learning Approach to Seismic Phase Association
    Zachary Ross, Yisong Yue, Men-Andrin Meier, Egill Hauksson, Thomas Heaton
    Journal of Geophysical Research - Solid Earth, DOI:0.1029/2018JB016674R, January 2019.
    [online][arxiv]
  • Reliable Real-time Seismic Signal/Noise Discrimination with Machine Learning
    Men-Andrin Meier, Zachary Ross, Anshul Ramachandran, Ashwin Balakrishna, Suraj Nair, Peter Kundzicz, Zefeng Li, Jennifer Andrews, Egill Hauksson, Yisong Yue
    Journal of Geophysical Research - Solid Earth, DOI:0.1029/2018JB016661, January 2019.
    [online][arxiv]
  • A General Method for Amortizing Variational Filtering
    Joseph Marino, Milan Cvitkovic, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December 2018.
    [pdf][arxiv][code]
  • Long-term Forecasting using Higher Order Tensor-Train RNNs
    Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue
    [arxiv][code]
  • 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.
    [pdf][arxiv][demo video][code]
  • 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]
    
  • Generating Long-term Trajectories Using Deep Hierarchical Networks
    Stephan Zheng, Yisong Yue, Patrick Lucey
    Neural Information Processing Systems (NeurIPS), December, 2016.
    [pdf][data]
  • A Decision Tree Framework for Spatiotemporal Sequence Prediction
    Taehwan Kim, Yisong Yue, Sarah Taylor, Iain Matthews
    ACM Conference on Knowledge Discovery and Data Mining (KDD), August, 2015.
    [pdf][demo]
• Preference Learning -- we aim to design learning algorithms that learn from preference feedback (e.g., is A better than B?) rather than typical cardinal feedback (e.g., how good is A?). Preference feedback is often more reliable than cardinal feedback when eliciting subjective feedback from humans.
  More Info

  • POLAR: Preference Optimization and Learning Algorithms for Robotics
    Maegan Tucker, Kejun Li, Yisong Yue, Aaron D. Ames
    [arxi]
  • Safety-Aware Preference-Based Learning for Safety-Critical Control
    Ryan K. Cosner, Maegan Tucker, Andrew J. Taylor, Kejun Li, Tamás G. Molnár, Wyatt Ubellacker, Anil Alan, Gábor Orosz, Yisong Yue, Aaron D. Ames
    Conference on Learning for Dynamics and Control (L4DC), June 2022.
    [arxiv]
  • ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
    Kejun Li, Maegan Tucker, Erdem Bıyık, Ellen Novoseller, Joel W. Burdick, Yanan Sui, Dorsa Sadigh, Yisong Yue, Aaron D. Ames
    International Conference on Robotics and Automation (ICRA), May 2021.
    [arxiv][video]
  • Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits
    Maegan Tucker, Myra Cheng, Ellen Novoseller, Richard Cheng, Yisong Yue, Joel W. Burdick, Aaron D. Ames
    International Conference on Intelligent Robots and Systems (IROS), October 2020.
    [arxiv]
  • Dueling Posterior Sampling for Preference-Based Reinforcement Learning
    Ellen Novoseller, Yibing Wei, Yanan Sui, Yisong Yue, Joel Burdick
    Conference on Uncertainty in Artificial Intelligence (UAI), August 2020.
    [arxiv]
  • 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]
  • Landmark Ordinal Embedding
    Nikhil Ghosh, Yuxin Chen, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December 2019.
    [pdf][arxiv]
  • Advancements in Dueling Bandits
    Yanan Sui, Masrour Zoghi, Katja Hofmann, Yisong Yue
    International Joint Conference on Artificial Intelligence (IJCAI), Survey Track, July 2018.
    [pdf]
  • 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]
  • Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces
    Yanan Sui, Yisong Yue, Joel Burdick
    International Joint Conference on Artificial Intelligence (IJCAI), August 2017.
    [pdf][arxiv]
  • 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]
  • Large-Scale Validation and Analysis of Interleaved Search Evaluation
    Olivier Chapelle, Thorsten Joachims, Filip Radlinski, Yisong Yue
    ACM Transactions on Information Systems (TOIS), 30(1), 6:1--6:41, February, 2012.
    (Selected for ACM Notable Computing Books and Articles of 2012)
    [pdf]
  • The K-armed Dueling Bandits Problem
    Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims
    Journal of Computer and System Sciences (JCSS), Special Issue on Learning Theory, DOI:10.1016/j.jcss.2011.12.028, May, 2012.
    [pdf][online]
  • Beat the Mean Bandit
    Yisong Yue, Thorsten Joachims
    International Conference on Machine Learning (ICML), June, 2011.
    [pdf][slides][poster][video]
  • Learning More Powerful Test Statistics for Click-Based Retrieval Evaluation
    Yisong Yue, Yue Gao, Olivier Chapelle, Ya Zhang, Thorsten Joachims
    ACM Conference on Information Retrieval (SIGIR), July, 2010.
    [pdf][slides]
  • The K-armed Dueling Bandits Problem
    Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims
    Conference on Learning Theory (COLT), June, 2009.
    [pdf][slides]
  • Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem
    Yisong Yue, Thorsten Joachims
    International Conference on Machine Learning (ICML), June, 2009.
    [pdf][slides][video]
• Machine Teaching -- we aim to develop novel algorithms for automated machine teaching, where the goal is for the machine to select examples to teach (typically a human). Problems of interest include adaptive teaching for forgetful learners, and interpretable teaching with explanations.
  More Info

  • Average-case Complexity of Teaching Convex Polytopes via Halfspace Queries
    Akash Kumar, Adish Singla, Yisong Yue, Yuxin Chen
    [arxiv]
  • Teaching Multiple Concepts to Forgetful Learners
    Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, Adish Singla
    Neural Information Processing Systems (NeurIPS), December 2019.
    [pdf][arxiv]
  • Barrier Certificates for Assured Machine Teaching
    Mohamadreza Ahmadi, Bo Wu, Yuxin Chen, Yisong Yue, Ufuk Topcu
    American Control Conference (ACC), July 2019.
    [pdf][arxiv]
    
  • Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners
    Yuxin Chen, Adish Singla, Oisin Mac Aodha, Pietro Perona, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December 2018.
    [pdf][arxiv (updated)]
  • Teaching Categories to Human Learners with Visual Explanations
    Oisin Mac Aodha, Shihan Su, Yuxin Chen, Pietro Perona, Yisong Yue
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.
    [arxiv]
  • 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]
• Dynamic Structured Prediction -- we aim to design algorithms for structured prediction in dynamic settings. Technical challenges include balancing the exploration/exploitation tradeoff, and solving complex planning problems over time.
  More Info

  • 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.
    [pdf][arxiv][demo video][code]
  • Coordinated Multi-Agent Imitation Learning
    Hoang M. Le, Yisong Yue, Peter Carr, Patrick Lucey
    International Conference on Machine Learning (ICML), August 2017.
    [pdf][arxiv][data][press release]
    
  • Data-Driven Ghosting using Deep Imitation Learning
    Hoang M. Le, Peter Carr, Yisong Yue, Patrick Lucey
    MIT Sloan Sports Analytics Conference (SSAC), March, 2017.
    (Best Paper Nomination)
    [pdf][project][demo video][press release]
  • Smooth Interactive Submodular Set Cover
    Bryan He, Yisong Yue
    Neural Information Processing Systems (NeurIPS), December, 2015.
    [pdf]
  • Non-Myopic Adaptive Route Planning in Uncertain Congestion Environments
    Siyuan Liu, Yisong Yue, Ramayya Krishnan
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(9), 2438--2451, DOI:10.1109/TKDE.2015.2411278, September, 2015.
    [preprint][online]
  • Adaptive Collective Routing Using Gaussian Process Dynamic Congestion Models
    Siyuan Liu, Yisong Yue, Ramayya Krishnan
    ACM Conference on Knowledge Discovery and Data Mining (KDD), August, 2013.
    [pdf]
  • An Efficient Simulation-based Approach to Ambulance Fleet Allocation and Dynamic Redeployment
    Yisong Yue, Lavanya Marla, Ramayya Krishnan
    AAAI Conference on Artificial Intelligence (AAAI), Special Track on Computational Sustainability and Artificial Intelligence, July, 2012.
    [pdf][spotlight slide][poster][press release][data]
  • Linear Submodular Bandits and their Application to Diversified Retrieval
    Yisong Yue, Carlos Guestrin
    Neural Information Processing Systems (NeurIPS), December, 2011.
    [pdf][poster]
  • Dynamic Ranked Retrieval
    Christina Brandt, Thorsten Joachims, Yisong Yue, Jacob Bank
    ACM Conference on Web Search and Data Mining (WSDM), February, 2011.
    (Best Paper Nomination)
    [pdf][software][video]
• Large Margin Structured Prediction -- we aim to develop large-margin learning methods for structured prediction settings. Examples including learning rankings, submodular functions, and multi-level models.
  More Info

  • Multi-level Structured Models for Document-level Sentiment Classification
    Ainur Yessenalina, Yisong Yue, Claire Cardie
    Conference on Empirical Methods in Natural Language Processing (EMNLP), October, 2010.
    [pdf][software][data]
  • Predicting Structured Objects with Support Vector Machines
    Thorsten Joachims, Thomas Hofmann, Yisong Yue, Chun-Nam Yu
    Communications of the ACM (CACM), Research Highlight, 52(11), 97--104, November, 2009.
    (With a technical perspective by John Shawe-Taylor.)
    [pdf][online]
  • Predicting Diverse Subsets Using Structural SVMs
    Yisong Yue, Thorsten Joachims
    International Conference on Machine Learning (ICML), June, 2008.
    [pdf][slides][software][video]
  • A Support Vector Method for Optimizing Average Precision
    Yisong Yue, Thomas Finley, Filip Radlinski, Thorsten Joachims
    ACM Conference on Information Retrieval (SIGIR), July, 2007.
    [pdf][slides][software]
• Implicit Human Feedback -- we aim to better understand biases that affect how we interpret implicit human feedback, such as clicks on a ranked list of search results.
  More Info

  • Large-Scale Validation and Analysis of Interleaved Search Evaluation
    Olivier Chapelle, Thorsten Joachims, Filip Radlinski, Yisong Yue
    ACM Transactions on Information Systems (TOIS), 30(1), 6:1--6:41, February, 2012.
    (Selected for ACM Notable Computing Books and Articles of 2012)
    [pdf]
  • Learning More Powerful Test Statistics for Click-Based Retrieval Evaluation
    Yisong Yue, Yue Gao, Olivier Chapelle, Ya Zhang, Thorsten Joachims
    ACM Conference on Information Retrieval (SIGIR), July, 2010.
    [pdf][slides]
  • Beyond Position Bias: Examining Result Attractiveness as a Source of Presentation Bias in Clickthrough Data
    Yisong Yue, Rajan Patel, Hein Roehrig
    International World Wide Web Conference (WWW), April, 2010.
    [pdf][slides]
• Richer User Interactions -- we aim to develop methods that better capture the design space of interactions in richer user interfaces. We are interested in both learning from rich interaction feedback, and designing recommendation algorithms that better exploit such interfaces.
  More Info

  • Interactive Sports Analytics: An Intelligent Interface for Utilizing Trajectories for Interactive Sports Play Retrieval and Analytics
    Long Sha, Patrick Lucey, Yisong Yue, Xinyu Wei, Jennifer Hobbs, Charlie Rohlf, Sridha Sridharan
    ACM Transactions on Computer-Human Interaction (TOCHI), April 2018.
    [pdf]
  • Chalkboarding: A New Spatiotemporal Query Paradigm for Sports Play Retrieval
    Long Sha, Patrick Lucey, Yisong Yue, Peter Carr, Charlie Rohlf, Iain Matthews
    ACM Conference on Intelligent User Interfaces (IUI), March, 2016.
    [pdf][demo video][press release]
  • Personalized Collaborative Clustering
    Yisong Yue, Chong Wang, Khalid El-Arini, Carlos Guestrin
    International World Wide Web Conference (WWW), April, 2014.
    [pdf][slides][data]
  • Dynamic Ranked Retrieval
    Christina Brandt, Thorsten Joachims, Yisong Yue, Jacob Bank
    ACM Conference on Web Search and Data Mining (WSDM), February, 2011.
    (Best Paper Nomination)
    [pdf][software][video]

• Conformal Generative Modeling on Triangulated Surfaces
  Victor Dorobantu, Charlotte Borcherds, Yisong Yue
  [arxiv][project][code]
• Eventual Discounting Temporal Logic Counterfactual Experience Replay
  Cameron Voloshin, Abhinav Verma, Yisong Yue
  [arxiv]
• FI-ODE: Certified and Robust Forward Invariance in Neural ODEs
  Yujia Huang, Ivan Dario Jimenez Rodriguez, Huan Zhang, Yuanyuan Shi, Yisong Yue
  [arxiv][code]
• Online Adaptive Controller Selection in Time-Varying Systems: No-Regret via Contractive Perturbations
  Yiheng Lin, James Preiss, Emile Anand, Yingying Li, Yisong Yue, Adam Wierman
  [arxiv]
• Neurosymbolic Programming for Science
  Jennifer J. Sun, Megan Tjandrasuwita, Atharva Sehgal, Armando Solar-Lezama, Swarat Chaudhuri, Yisong Yue, Omar Costilla-Reyes
  [arxiv]
• POLAR: Preference Optimization and Learning Algorithms for Robotics
  Maegan Tucker, Kejun Li, Yisong Yue, Aaron D. Ames
  [arxi]
• The MABe22 Benchmarks for Representation Learning of Multi-Agent Behavior
  Jennifer J. Sun, Andrew Ulmer, Dipam Chakraborty, Brian Geuther, Edward Hayes, Heng Jia, Vivek Kumar, Zachary Partridge, Alice Robie, Catherine E. Schretter, Chao Sun, Keith Sheppard, Param Uttarwar, Pietro Perona, Yisong Yue, Kristin Branson, Ann Kennedy
  [arxiv]
• Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings
  Sabera Talukder, Jennifer J. Sun, Matthew Leonard, Bingni W. Brunton, Yisong Yue
  [arxiv]
• Kernel Interpolation as a Bayes Point Machine
  Jeremy Bernstein, Alex Farhang, Yisong Yue
  [arxiv]
• Computing the Information Content of Trained Neural Networks
  Jeremy Bernstein, Yisong Yue
  [arxiv][code]
• Distributionally Robust Learning for Unsupervised Domain Adaptation
  Haoxuan Wang, Anqi Liu, Zhiding Yu, Yisong Yue, Anima Anandkumar
  [arxiv]
• On the Benefits of Early Fusion in Multimodal Representation Learning
  George Barnum, Sabera Talukder, Yisong Yue
  [arxiv]
• Architecture Agnostic Neural Networks
  Sabera Talukder, Guruprasad Raghavan, Yisong Yue
  [arxiv]
• Average-case Complexity of Teaching Convex Polytopes via Halfspace Queries
  Akash Kumar, Adish Singla, Yisong Yue, Yuxin Chen
  [arxiv]
• Triply Robust Off-Policy Evaluation
  Anqi Liu, Hao Liu, Anima Anandkumar, Yisong Yue
  [arxiv]
• Learning to Search via Retrospective Imitation
  Jialin Song, Ravi Lanka, Albert Zhao, Aadyot Bhatnagar, Yisong Yue, Masahiro Ono
  [arxiv]
• Detecting Adversarial Examples via Neural Fingerprinting
  Sumanth Dathathri, Stephan Zheng, Tianwei Yin, Richard M. Murray, Yisong Yue
  [arxiv][code]
• Long-term Forecasting using Higher Order Tensor-Train RNNs
  Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue
  [arxiv][code]

• BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos
  Jennifer J. Sun, Pierre Karashchuk, Amil Dravid, Serim Ryou, Sonia Fereidooni, John Tuthill, Aggelos Katsaggelos, Bingni W. Brunton, Georgia Gkioxari, Ann Kennedy, Yisong Yue, Pietro Perona
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
  [arxiv]
• Compactly Restrictable Metric Policy Optimization Problems
  Victor D. Dorobantu, Kamyar Azizzadenesheli, Yisong Yue
  IEEE Transactions on Automation Control (TAC), Technical Note, 2022.
  [arxiv]
• The Mixed-Observable Constrained Linear Quadratic Regulator Problem: the Exact Solution and Practical Algorithms
  Ugo Rosolia, Yuxiao Chen, Shreyansh Daftry, Masahiro Ono, Yisong Yue, Aaron D. Ames
  IEEE Transactions on Automation Control (TAC), Technical Note, 2022.
  [arxiv][online]
• Unsupervised Learning of Neurosymbolic Encoders
  Eric Zhan*, Jennifer J. Sun*, Ann Kennedy, Yisong Yue, Swarat Chaudhuri
  Transactions of Machine Learning Research (TMLR), 2022.
  [arxiv][code]
• End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functions
  Ryan Cosner, Yisong Yue, Aaron D. Ames
  IEEE Conference on Decision and Control (CDC), December, 2022.
  [pdf]
• Safety of Sampled-Data Systems with Control Barrier Functions via Approximate Discrete Time Models
  Andrew J. Taylor, Victor D. Dorobantu, Ryan K. Cosner, Yisong Yue, Aaron D. Ames
  IEEE Conference on Decision and Control (CDC), December, 2022.
  [arxiv]
• Policy Optimization with Linear Temporal Logic Constraints
  Cameron Voloshin, Hoang M. Le, Swarat Chaudhuri, Yisong Yue
  Neural Information Processing Systems (NeurIPS), December, 2022.
  [arxiv]
• Investigating Generalization by Controlling Normalized Margin
  Alexander Farhang, Jeremy Bernstein, Kushal Tirumala, Yang Liu, Yisong Yue
  International Conference on Machine Learning (ICML), July 2022.
  [arxiv][code]
• LyaNet: A Lyapunov Framework for Training Neural ODEs
  Ivan Dario Jimenez Rodriguez, Aaron D. Ames, Yisong Yue
  International Conference on Machine Learning (ICML), July 2022.
  [arxiv][code]
• Learning Pseudo-Backdoors for Mixed Integer Programs
  Aaron Ferber, Jialin Song, Bistra Dilkina, Yisong Yue
  International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), June 2022.
  [arxiv]
• Interpreting Expert Annotation Differences in Animal Behavior
  Megan Tjandrasuwita, Jennifer J. Sun, Ann Kennedy, Swarat Chaudhuri, Yisong Yue
  CVPR Workshop on CV4Animals, June 2022.
  [arxiv]
• Self-Supervised Keypoint Discovery in Behavioral Videos
  Jennifer J. Sun*, Serim Ryou*, Roni Goldshmid, Brandon Weissbourd, John Dabiri, David J. Anderson, Ann Kennedy, Yisong Yue, Pietro Perona
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2022.
  [arxiv]
• Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis
  Albert Tseng, Jennifer J. Sun, Yisong Yue
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2022.
  [arxiv]
• Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies
  Ivan Dario Jimenez Rodriguez*, Noel Csomay-Shanklin*, Yisong Yue, Aaron D. Ames
  Conference on Learning for Dynamics and Control (L4DC), June 2022.
  [arxiv][video]
• Safety-Aware Preference-Based Learning for Safety-Critical Control
  Ryan K. Cosner, Maegan Tucker, Andrew J. Taylor, Kejun Li, Tamás G. Molnár, Wyatt Ubellacker, Anil Alan, Gábor Orosz, Yisong Yue, Aaron D. Ames
  Conference on Learning for Dynamics and Control (L4DC), June 2022.
  [arxiv]
• Competitive Control with Delayed Imperfect Information
  Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman
  American Control Conference (ACC), June 2022.
  [arxiv]
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds
  Michael O’Connell*, Guanya Shi*, Xichen Shi, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
  Science Robotics, May 2022.
  [arxiv][online][code][video][press release]
• Natural Multicontact Walking for Robotic Assistive Devices via Musculoskeletal Models and Hybrid Zero Dynamics
  Kejun Li, Maegan Tucker, Rachel Gehlhar, Yisong Yue, Aaron Ames
  IEEE Robotics and Automation Letters (RA-L), May 2022
  [arxiv][demo video]
• MLNav: Learning to Safely Navigate on Martian Terrains
  Shreyansh Daftry, Neil Abcouwer, Tyler Del Sesto, Siddarth Venkatraman, Jialin Song, Lucas Igel, Amos Byon, Ugo Rosolia, Yisong Yue, Masahiro Ono
  IEEE Robotics and Automation Letters (RA-L), May 2022
  [conference][journal][video]
• Self-Supervised Online Learning for Safety-Critical Control using Stereo Vision
  Ryan Cosner*, Ivan Dario Jimenez Rodriguez*, Tamas G. Molnar, Wyatt Ubellacker, Yisong Yue, Katherine Bouman, Aaron Ames
  International Conference on Robotics and Automation (ICRA), May 2022
  [arxiv]
• Neurosymbolic Programming
  Swarat Chaudhuri, Kevin Ellis, Oleksandr Polozov, Rishabh Singh, Armando Solar-Lezama, Yisong Yue
  Foundations and Trends in Programming Languages, Volume 7: No. 3, pages 158-243, December 2021.
  [preprint][online]
• Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty
  Andrew J. Taylor*, Victor D. Dorobantu*, Sarah Dean*, Benjamin Recht, Yisong Yue, Aaron D. Ames
  IEEE Conference on Decision and Control (CDC), December 2021.
  [arxiv]
• DeepGEM: Generalized Expectation-Maximization for Blind Inversion
  Angela Gao, Jorge Castellanos, Yisong Yue, Zachary Ross, Katherine Bouman
  Neural Information Processing Systems (NeurIPS), December 2021.
  [pdf][code][project]
• Iterative Amortized Policy Optimization
  Joseph Marino, Alexandre Piché, Alessandro Davide Ialongo, Yisong Yue
  Neural Information Processing Systems (NeurIPS), December 2021.
  [arxiv][code]
• Meta-Adaptive Nonlinear Control: Theory and Algorithms
  Guanya Shi, Kamyar Azizzadenesheli, Michael O'Connell, Soon-Jo Chung, Yisong Yue
  Neural Information Processing Systems (NeurIPS), December 2021.
  [arxiv][code]
• Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning
  Cameron Voloshin, Hoang M. Le, Nan Jiang, Yisong Yue
  Neural Information Processing Systems (NeurIPS), December 2021.
  [arxiv][code]
• The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions
  Jennifer J. Sun, Tomomi Karigo, Dipam Chakraborty, Sharada P. Mohanty, Benjamin Wild, Quan Sun, Chen Chen, David J. Anderson, Pietro Perona, Yisong Yue, Ann Kennedy
  Neural Information Processing Systems (NeurIPS), December 2021.
  [arxiv][dataset][code][challenge]
• End-to-End Sequential Sampling and Reconstruction for MR Imaging
  Tianwei Yin*, Zihui Wu*, He Sun, Adrian V. Dalca, Yisong Yue, Katherine L. Bouman
  Machine Learning for Health (ML4H), December 2021.
  (Best Paper Award)
  [arxiv][project]
• Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control
  Ivan D. Jimenez Rodriguez, Ugo Rosolia, Aaron D. Ames, Yisong Yue
  International Conference on Intelligent Robots and Systems (IROS), September 2021.
  [arxiv][demo video]
• 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]
• Informed training set design enables efficient machine learning-assisted directed protein evolution
  Bruce Wittmann, Yisong Yue, Frances Arnold
  Cell Systems, August 2021.
  [online][bioRxiv][code]
• Fine-Grained System Identification of Nonlinear Neural Circuits
  Dawna Bagherian, James Gornet, Jeremy Bernstein, Yu-Li Ni, Yisong Yue, Markus Meister
  ACM Conference on Knowledge Discovery and Data Mining (KDD), August 2021.
  [pdf][arxiv][code]
• Competitive Policy Optimization
  Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar
  Conference on Uncertainty in Artificial Intelligence (UAI), July 2021.
  [arxiv]
• Learning by Turning: Neural Architecture Aware Optimisation
  Yang Liu*, Jeremy Bernstein*, Markus Meister, Yisong Yue
  International Conference on Machine Learning (ICML), July 2021.
  [arxiv][code]
• Sampled-Data Stabilization with Control Lyapunov Functions via Quadratically Constrained Quadratic Programs
  Andrew J. Taylor*, Victor D. Dorobantu*, Yisong Yue, Paulo Tabuada, Aaron D. Ames
  IEEE Control Systems Letters (L-CSS), June 2021.
  [arxiv]
• 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 Invariant Representation of Tasks for Robust Surgical State Estimation
  Yidan Qin, Max Allan, Yisong Yue, Joel Burdick, Mahdi Azizian
  IEEE Robotics and Automation Letters (RA-L), May 2021.
  [arxiv]
• ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
  Kejun Li, Maegan Tucker, Erdem Bıyık, Ellen Novoseller, Joel W. Burdick, Yanan Sui, Dorsa Sadigh, Yisong Yue, Aaron D. Ames
  International Conference on Robotics and Automation (ICRA), May 2021.
  [arxiv][video]
• 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]
• Minimax Model Learning
  Cameron Voloshin, Nan Jiang, Yisong Yue
  International Conference on Artificial Intelligence and Statistics (AISTATS), April 2021.
  [pdf][arxiv]
• Active Learning under Label Shift
  Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue
  International Conference on Artificial Intelligence and Statistics (AISTATS), April 2021.
  [arxiv]
• Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
  Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon-Jo Chung
  IEEE Robotics and Automation Letters (RA-L), April 2021.
  [arxiv]
• Machine Learning Based Path Planning for Improved Rover Navigation
  Neil Abcouwer, Shreyansh Daftry, Siddarth Venkatraman, Tyler del Sesto, Olivier Toupet, Ravi Lanka, Jialin Song, Yisong Yue, Masahiro Ono
  IEEE Aerospace Conference (AeroConf), March 2021.
  [arxiv][video]
• Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments
  Logan Cross, Jeff Cockburn, Yisong Yue, John O’Doherty
  Neuron, February 2021
  [online][press release]
• Deep Bayesian Quadrature Policy Optimization
  Akella Ravi Tej, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Anima Anandkumar, Yisong Yue
  AAAI Conference on Artificial Intelligence (AAAI), February 2021.
  [arxiv][code]
• Multi-Label Classification Models for the Prediction of Cross-Coupling Reaction Conditions
  Michael Maser, Alexander Cui, Serim Ryou, Travis DeLano, Yisong Yue, Sarah Reisman
  Journal of Chemical Information and Modeling (JCIM), January 2021
  [online][ChemRxiv]
• 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]
• Online Optimization with Memory and Competitive Control
  Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman
  Neural Information Processing Systems (NeurIPS), December 2020.
  [arxiv]
• The Power of Predictions in Online Control
  Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman
  Neural Information Processing Systems (NeurIPS), December 2020.
  [arxiv]
• 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]
• Learning compositional functions via multiplicative weight updates
  Jeremy Bernstein, Jiawei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar, Yisong Yue
  Neural Information Processing Systems (NeurIPS), December 2020.
  [arxiv][code]
• 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]
• Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits
  Maegan Tucker, Myra Cheng, Ellen Novoseller, Richard Cheng, Yisong Yue, Joel W. Burdick, Aaron D. Ames
  International Conference on Intelligent Robots and Systems (IROS), October 2020.
  [arxiv]
• Dueling Posterior Sampling for Preference-Based Reinforcement Learning
  Ellen Novoseller, Yibing Wei, Yanan Sui, Yisong Yue, Joel Burdick
  Conference on Uncertainty in Artificial Intelligence (UAI), August 2020.
  [arxiv]
• A Control Barrier Perspective on Episodic Learning via Projection-to-State Safety
  Andrew J. Taylor, Andrew Singletary, Yisong Yue, Aaron D. Ames
  IEEE Control Systems Letters (L-CSS), July 2020.
  [arxiv]
• Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions
  Serim Ryou, Michael R. Maser, Alexander Y. Cui, Travis J. DeLano, Yisong Yue, Sarah E. Reisman
  ICML 2020 Workshop on Graph Representation Learning and Beyond, July 2020.
  [arxiv]
• 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]
• Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
  Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, Rose Yu
  International Conference on Machine Learning (ICML), July 2020.
  [pdf][arxiv][code]
• GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning
  Benjamin Rivière, Wolfgang Hoenig, Yisong Yue, Soon-Jo Chung
  IEEE Robotics and Automation Letters (RA-L), June 2020.
  (Best Paper Nomination)
  [pdf][arxiv][demo video]
• Robust Regression for Safe Exploration in Control
  Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue
  Conference on Learning for Dynamics and Control (L4DC), June 2020.
  [arxiv]
• Learning for Safety-Critical Control with Control Barrier Functions
  Andrew Taylor, Andrew Singletary, Yisong Yue, Aaron Ames
  Conference on Learning for Dynamics and Control (L4DC), June 2020.
  [arxiv]
• Mirrored Plasmonic Filter Design via Active Learning of Multi-Fidelity Physical Models
  Jialin Song, Yury S Tokpanov, Yuxin Chen, Dagny Fleischman, Katherine T Fountaine, Yisong Yue, Harry A Atwater
  IEEE Conference on Lasers and Electro-Optics (CLEO), May 2020.
  [online]
• Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions
  Guanya Shi, Wolfgang Hoenig, Yisong Yue, Soon-Jo Chung
  International Conference on Robotics and Automation (ICRA), May 2020.
  [pdf][arxiv][demo video]
• 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]
• An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing
  Baihong Jin, Yingshui Tan, Alexander Nettekoven, Yuxin Chen, Ufuk Topcu, Yisong Yue, Alberto Sangiovanni Vincentelli
  IEEE International Conference on Machine Learning and Applications (ICMLA), December 2019.
  [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][demo video]
• NAOMI: Non-Autoregressive Multiresolution Sequence Imputation
  Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue
  Neural Information Processing Systems (NeurIPS), December 2019.
  [pdf][arxiv][code][demo video]
• Teaching Multiple Concepts to Forgetful Learners
  Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, Adish Singla
  Neural Information Processing Systems (NeurIPS), December 2019.
  [pdf][arxiv]
• Landmark Ordinal Embedding
  Nikhil Ghosh, Yuxin Chen, Yisong Yue
  Neural Information Processing Systems (NeurIPS), December 2019.
  [pdf][arxiv]
• A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability
  Andrew J. Taylor*, Victor D. Dorobantu*, Meera Krishnamoorthy, Hoang M. Le, Yisong Yue, Aaron D. Ames
  IEEE Conference on Decision and Control (CDC), December 2019.
  [pdf][arxiv]
• Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems
  Andrew J. Taylor*, Victor D. Dorobantu*, Hoang M. Le, Yisong Yue, Aaron D. Ames
  International Conference on Intelligent Robots and Systems (IROS), November 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][arxiv][slides][code]
• Barrier Certificates for Assured Machine Teaching
  Mohamadreza Ahmadi, Bo Wu, Yuxin Chen, Yisong Yue, Ufuk Topcu
  American Control Conference (ACC), July 2019.
  [pdf][arxiv]
  
• 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]
• Control Regularization for Reduced Variance Reinforcement Learning
  Richard Cheng, Abhinav Verma, Gabor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdick
  International Conference on Machine Learning (ICML), June 2019.
  [pdf][arxiv][code]
• 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]
• 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.
  [pdf][arxiv][demo video][code]
• 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]
• Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design
  Kevin Yang, Yuxin Chen, Alycia Lee, Yisong Yue
  International Conference on Artificial Intelligence and Statistics (AISTATS), April 2019.
  [pdf][arxiv]
• PhaseLink: A Deep Learning Approach to Seismic Phase Association
  Zachary Ross, Yisong Yue, Men-Andrin Meier, Egill Hauksson, Thomas Heaton
  Journal of Geophysical Research - Solid Earth, DOI:0.1029/2018JB016674R, January 2019.
  [online][arxiv]
• Reliable Real-time Seismic Signal/Noise Discrimination with Machine Learning
  Men-Andrin Meier, Zachary Ross, Anshul Ramachandran, Ashwin Balakrishna, Suraj Nair, Peter Kundzicz, Zefeng Li, Jennifer Andrews, Egill Hauksson, Yisong Yue
  Journal of Geophysical Research - Solid Earth, DOI:0.1029/2018JB016661, January 2019.
  [online][arxiv]
• Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes
  Jialin Song, Yury S. Tokpanov, Yuxin Chen, Dagny Fleischman, Kate T. Fountaine, Harry A. Atwater, Yisong Yue
  NeurIPS 2018 Workshop on Machine Learning for Molecules and Materials, December 2018.
  [arxiv]
• A General Method for Amortizing Variational Filtering
  Joseph Marino, Milan Cvitkovic, Yisong Yue
  Neural Information Processing Systems (NeurIPS), December 2018.
  [pdf][arxiv][code]
• Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners
  Yuxin Chen, Adish Singla, Oisin Mac Aodha, Pietro Perona, Yisong Yue
  Neural Information Processing Systems (NeurIPS), December 2018.
  [pdf][arxiv (updated)]
• Advancements in Dueling Bandits
  Yanan Sui, Masrour Zoghi, Katja Hofmann, Yisong Yue
  International Joint Conference on Artificial Intelligence (IJCAI), Survey Track, July 2018.
  [pdf]
• Iterative Amortized Inference
  Joseph Marino, Yisong Yue, Stephan Mandt
  International Conference on Machine Learning (ICML), July 2018.
  [pdf][arxiv][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]
• 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]
• Teaching Categories to Human Learners with Visual Explanations
  Oisin Mac Aodha, Shihan Su, Yuxin Chen, Pietro Perona, Yisong Yue
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.
  [arxiv]
• Interactive Sports Analytics: An Intelligent Interface for Utilizing Trajectories for Interactive Sports Play Retrieval and Analytics
  Long Sha, Patrick Lucey, Yisong Yue, Xinyu Wei, Jennifer Hobbs, Charlie Rohlf, Sridha Sridharan
  ACM Transactions on Computer-Human Interaction (TOCHI), April 2018.
  [pdf]
• 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]
• Safe Exploration and Optimization of Constrained MDPs using Gaussian Processes
  Akifumi Wachi, Yanan Sui, Yisong Yue, Masahiro Ono
  AAAI Conference on Artificial Intelligence (AAAI), February 2018.
  [pdf]
• Telemetry Anomaly Detection System using Machine Learning to Streamline Mission Operations
  Michela Munoz Fernandez, Yisong Yue, Romann Weber
  IEEE International Conference on Space Mission Challenges for Information Technology (SMC-IT), September 2017.
  [pdf]
• Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces
  Yanan Sui, Yisong Yue, Joel Burdick
  International Joint Conference on Artificial Intelligence (IJCAI), August 2017.
  [pdf][arxiv]
• 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]
• Coordinated Multi-Agent Imitation Learning
  Hoang M. Le, Yisong Yue, Peter Carr, Patrick Lucey
  International Conference on Machine Learning (ICML), August 2017.
  [pdf][arxiv][data][press release]
  
• 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]
  
• Factorized Variational Autoencoders for Modeling Audience Reactions to Movies
  Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain Matthews, Greg Mori
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July, 2017.
  [pdf][press release][radio interview]
• Learning recurrent representations for hierarchical behavior modeling
  Eyrun Eyjolfsdottir, Kristin Branson, Yisong Yue, Pietro Perona
  International Conference on Learning Representations (ICLR), April, 2017.
  [pdf][arxiv][supplementary]
• Data-Driven Ghosting using Deep Imitation Learning
  Hoang M. Le, Peter Carr, Yisong Yue, Patrick Lucey
  MIT Sloan Sports Analytics Conference (SSAC), March, 2017.
  (Best Paper Nomination)
  [pdf][project][demo video][press release]
• A Rotation Invariant Latent Factor Model for Moveme Discovery from Static Poses
  Matteo Ronchi, Joon Sik Kim, Yisong Yue
  IEEE International Conference on Data Mining (ICDM), December, 2016.
  [pdf][project]
• Generating Long-term Trajectories Using Deep Hierarchical Networks
  Stephan Zheng, Yisong Yue, Patrick Lucey
  Neural Information Processing Systems (NeurIPS), December, 2016.
  [pdf][data]
• Learning Online Smooth Predictors for Real-time Camera Planning using Recurrent Decision Trees
  Jianhui Chen, Hoang M. Le, Peter Carr, Yisong Yue, James J. Little
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
  (Oral Presentation)
  [pdf][press release][Sports Illustrated][DataScience.com][code]
• 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][code]
• Chalkboarding: A New Spatiotemporal Query Paradigm for Sports Play Retrieval
  Long Sha, Patrick Lucey, Yisong Yue, Peter Carr, Charlie Rohlf, Iain Matthews
  ACM Conference on Intelligent User Interfaces (IUI), March, 2016.
  [pdf][demo video][press release]
• Robust Ambulance Allocation Using Risk-based Metrics
  Kaushik Krishnan, Lavanya Marla, Yisong Yue
  COMSNETS 2016 Workshop on Intelligence Transportation Systems, January, 2016.
  [pdf]
• Scalable Training of Interpretable Spatial Latent Factor Models
  Stephan Zheng, Yisong Yue
  NeurIPS 2015 Workshop on Non-convex Optimization for Machine Learning, December, 2015.
  [pdf]
• Smooth Interactive Submodular Set Cover
  Bryan He, Yisong Yue
  Neural Information Processing Systems (NeurIPS), December, 2015.
  [pdf]
• Non-Myopic Adaptive Route Planning in Uncertain Congestion Environments
  Siyuan Liu, Yisong Yue, Ramayya Krishnan
  IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(9), 2438--2451, DOI:10.1109/TKDE.2015.2411278, September, 2015.
  [preprint][online]
• A Decision Tree Framework for Spatiotemporal Sequence Prediction
  Taehwan Kim, Yisong Yue, Sarah Taylor, Iain Matthews
  ACM Conference on Knowledge Discovery and Data Mining (KDD), August, 2015.
  [pdf][demo]
• Identifying Team Style in Soccer using Formations Learned from Spatiotemporal Tracking Data
  Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue, Sridha Sridharan, Iain Matthews
  ICDM 2014 International Workshop on Spatial and Spatio-temporal Data Mining (SSTDM), December, 2014.
  [pdf]
• Learning Fine-Grained Spatial Models for Dynamic Sports Play Prediction
  Yisong Yue, Patrick Lucey, Peter Carr, Alina Bialkowski, Iain Matthews
  IEEE International Conference on Data Mining (ICDM), December, 2014.
  (Best Paper Nomination)
  [pdf][demo][press release]
• Large-Scale Analysis of Soccer Matches using Spatiotemporal Tracking Data
  Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue, Sridha Sridharan, Iain Matthews
  IEEE International Conference on Data Mining (ICDM), December, 2014.
  [pdf]
• Personalized Collaborative Clustering
  Yisong Yue, Chong Wang, Khalid El-Arini, Carlos Guestrin
  International World Wide Web Conference (WWW), April, 2014.
  [pdf][slides][data]
• "Win at Home and Draw Away": Automatic Formation Analysis Highlighting the Differences in Home and Away Team Behaviors
  Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue, Iain Matthews
  MIT Sloan Sports Analytics Conference (SSAC), February, 2014.
  [pdf]
• "How to Get an Open Shot": Analyzing Team Movement in Basketball using Tracking Data
  Patrick Lucey, Alina Bialkowski, Peter Carr, Yisong Yue, Iain Matthews
  MIT Sloan Sports Analytics Conference (SSAC), February, 2014.
  [pdf]
• Adaptive Collective Routing Using Gaussian Process Dynamic Congestion Models
  Siyuan Liu, Yisong Yue, Ramayya Krishnan
  ACM Conference on Knowledge Discovery and Data Mining (KDD), August, 2013.
  [pdf]
• Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization
  Jiaji Zhou, Stephane Ross, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell
  ICML Workshop on Inferning: Interactions between Inference and Learning, June, 2013.
  [pdf][software]
• Learning Policies for Contextual Submodular Prediction
  Stephane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell
  International Conference on Machine Learning (ICML), June, 2013.
  [pdf][software][video]
• An Efficient Simulation-based Approach to Ambulance Fleet Allocation and Dynamic Redeployment
  Yisong Yue, Lavanya Marla, Ramayya Krishnan
  AAAI Conference on Artificial Intelligence (AAAI), Special Track on Computational Sustainability and Artificial Intelligence, July, 2012.
  [pdf][spotlight slide][poster][press release][data]
• Hierarchical Exploration for Accelerating Contextual Bandits
  Yisong Yue, Sue Ann Hong, Carlos Guestrin
  International Conference on Machine Learning (ICML), June, 2012.
  [pdf][slides][poster][video]
• The K-armed Dueling Bandits Problem
  Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims
  Journal of Computer and System Sciences (JCSS), Special Issue on Learning Theory, DOI:10.1016/j.jcss.2011.12.028, May, 2012.
  [pdf][online]
• Large-Scale Validation and Analysis of Interleaved Search Evaluation
  Olivier Chapelle, Thorsten Joachims, Filip Radlinski, Yisong Yue
  ACM Transactions on Information Systems (TOIS), 30(1), 6:1--6:41, February, 2012.
  (Selected for ACM Notable Computing Books and Articles of 2012)
  [pdf]
• Linear Submodular Bandits and their Application to Diversified Retrieval
  Yisong Yue, Carlos Guestrin
  Neural Information Processing Systems (NeurIPS), December, 2011.
  [pdf][poster]
• Beat the Mean Bandit
  Yisong Yue, Thorsten Joachims
  International Conference on Machine Learning (ICML), June, 2011.
  [pdf][slides][poster][video]
• Dynamic Ranked Retrieval
  Christina Brandt, Thorsten Joachims, Yisong Yue, Jacob Bank
  ACM Conference on Web Search and Data Mining (WSDM), February, 2011.
  (Best Paper Nomination)
  [pdf][software][video]
• New Learning Frameworks for Information Retrieval
  Yisong Yue
  Ph.D. Dissertation, Cornell University, January, 2011.
  [pdf]
• Multi-level Structured Models for Document-level Sentiment Classification
  Ainur Yessenalina, Yisong Yue, Claire Cardie
  Conference on Empirical Methods in Natural Language Processing (EMNLP), October, 2010.
  [pdf][software][data]
• Learning More Powerful Test Statistics for Click-Based Retrieval Evaluation
  Yisong Yue, Yue Gao, Olivier Chapelle, Ya Zhang, Thorsten Joachims
  ACM Conference on Information Retrieval (SIGIR), July, 2010.
  [pdf][slides]
• Beyond Position Bias: Examining Result Attractiveness as a Source of Presentation Bias in Clickthrough Data
  Yisong Yue, Rajan Patel, Hein Roehrig
  International World Wide Web Conference (WWW), April, 2010.
  [pdf][slides]
• Predicting Structured Objects with Support Vector Machines
  Thorsten Joachims, Thomas Hofmann, Yisong Yue, Chun-Nam Yu
  Communications of the ACM (CACM), Research Highlight, 52(11), 97--104, November, 2009.
  (With a technical perspective by John Shawe-Taylor.)
  [pdf][online]
• The K-armed Dueling Bandits Problem
  Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims
  Conference on Learning Theory (COLT), June, 2009.
  [pdf][slides]
• Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem
  Yisong Yue, Thorsten Joachims
  International Conference on Machine Learning (ICML), June, 2009.
  [pdf][slides][video]
• Predicting Diverse Subsets Using Structural SVMs
  Yisong Yue, Thorsten Joachims
  International Conference on Machine Learning (ICML), June, 2008.
  [pdf][slides][software][video]
• On Using Simultaneous Perturbation Stochastic Approximation for IR Measures, and the Empirical Optimality of LambdaRank
  Yisong Yue, Christopher Burges
  NeurIPS Machine Learning for Web Search Workshop, December, 2007.
  [pdf][tech report]
• A Support Vector Method for Optimizing Average Precision
  Yisong Yue, Thomas Finley, Filip Radlinski, Thorsten Joachims
  ACM Conference on Information Retrieval (SIGIR), July, 2007.
  [pdf][slides][software]

• Imitation Learning, co-taught with Hoang Le, presented at ICML 2018.
  [link]
• An Introduction to Ensemble Methods: Bagging, Boosting, Random Forests and More, presented at Disney Research.
  [slides]
• Practical Online Retrieval Evaluation, co-taught with Filip Radlinski, presented at SIGIR 2011.
  [slides][demo scripts]
• Learning to Rank, co-taught with Filip Radlinski, presented at NESCAI 2008.
  [part1][part2]

• AI for Adaptive Experiment Design, Genentech AI Seminar, Feburary, 2023.
  [slides][older video]
• Controlling the Structure of Inference and Learning in Neural Networks, DeepMath, November 2022.
  [slides]
• Learning for Reliable Control in Dynamical Systems, UCSD AI Seminar, April 2022.
  [slides]
• Neurosymbolic Programming, Caltech Explainable AI for Science Workshop, September, 2021.
  [slides][video]
• Personalized Preference Learning from Spinal Cord Stimulation to Exoskeletons, July, 2021.
  [video][slides]
• Improving Policy Learning via Programmatic Domain Knowledge, Caltech, April, 2021.
  [slides]
• Competitive Algorithms for Online Control, Simons Institute Workshop for Mathematics of Online Decision Making, October, 2020.
  [slides]
• Learning to Optimize as Policy Learning, Princeton University, October, 2020.
  [slides]
• Learning for Safety-Critical Control in Dynamical Systems, Control Meets Learning, January, 2021.
  [slides][video]
• New Frontiers in Imitation Learning, University of Chicago, November, 2019.
  [slides]
• Policy Learning with Certifiable Guarantees, University of California Los Angeles, October, 2019.
  [slides]
• Real-World Bayesian Optimization, KDD 2019 Workshop on Data Collection, Curation, and Labeling for Mining and Learning, August, 2019.
  [slides][video]
• Two Vignettes in Robust Detection and Adversarial Analysis in Control, CVPR 2019 Workshop on Adversarial Machine Learning in Real-World Computer Vision Systems, June, 2019.
  [slides]
• Structured Imitation and Reinforcement Learning, NeurIPS 2018 Workshop on Imitation Learning for Robotics, December, 2018.
  [slides]
• Machine Teaching for Human Learners, IJCAI 2018 Workshop on Humanizing AI, July, 2018.
  [slides]
• Inference + Imitation, ICML 2018 Workshop on Tractable Probabilistic Models, July, 2018.
  [slides]
• The Dueling Bandits Problem, Massachusetts Institute of Technology, September, 2017.
  [slides]
• Learning to Optimize for Structured Output Spaces, University of California Santa Barbara, April, 2017.
  [slides]
• Recent Applications of Latent Factor Models, Second Spectrum, September, 2015.
  [slides]
• Learning Spatial Models of Basketball Gameplay, KDD 2015 Workshop on Large-Scale Sports Analytics, August, 2015.
  [slides]
• Balancing the Explore/Exploit Tradeoff in Interactive Structured Prediction, Cornell University, December, 2014.
  [slides][video]
• Learning with Humans in the Loop, Disney Research, May, 2013.
  [slides]
• Optimizing Recommender Systems as a Submodular Bandit Problem, University of Toronto, November, 2012.
  [slides]
• An Introduction to Structural SVMs and its Application to Information Retrieval, University of California Berkeley, October, 2012.
  [slides]
• Practical and Reliable Retrieval Evaluation Through Online Experimentation, WSDM 2012 Workshop on Web Search Click Data, February, 2012.
  [slides]
• An Interactive Learning Approach to Optimizing Information Retrieval Systems, Carnegie Mellon University, September, 2010.
  [slides][video]
• New Learning Frameworks for Information Retrieval, Microsoft Research, March, 2010.
  [video]
• Diversified Retrieval as Structured Prediction, SIGIR 2009 Workshop on Redundancy, Diversity and Interdependent Document Relevance, July, 2009.
  [slides]
• Information Retrieval as Structured Prediction, University of Massachusetts Amherst, April, 2009.
  [slides]