Yisong Yue

Contact California Institute of Technology website: www.yisongyue.com Information 1200 E. California Blvd. email: [email protected] CMS, 305-16 Pasadena, CA 91125

Research Theory and application of statistical machine learning, with a particular focus on developing Interests novel methods for interactive machine learning and structured machine learning.

Research California Institute of Technology September 2014 - Present Appointments Position: Professor (May 2020 - Present) Previously: Assistant Professor (September 2014 - May 2020)

Disney Research August 2013 - August 2014 Position: Research Scientist

Carnegie Mellon University September 2010 - August 2013 Position: Postdoctoral Researcher Supervisors: Carlos Guestrin and Ramayya Krishnan

Cornell University May 2006 - August 2010 Position: Research Assistant Supervisors: Thorsten Joachims and Robert Kleinberg

Google June 2009 - September 2009 Position: Search Quality Analyst Intern Supervisor: Rajan Patel

Microsoft Research May 2007 - August 2007 Position: Research Intern Supervisor: Christopher Burges

Education Cornell University Ph.D. January 2011 Ph.D. in Computer Science Graduate Minor in Statistics Dissertation: New Learning Frameworks for Information Retrieval Thesis Committee: Thorsten Joachims (advisor), Robert Kleinberg, Christopher Burges, Ping Li, John Hopcroft

University of Illinois at Urbana-Champaign B.S. June 2005 Bachelor of Science in Computer Science Graduated with Highest Honors (Summa Cum Laude)

Illinois Math and Science Academy 1998 - 2001

Honors and Okawa Foundation Grant Recipient, 2018 Awards Best Reviewer, ICLR 2018 Best Paper Award, ICRA 2020 Best Student Paper Award, CVPR 2021 Best Paper Nomination, WSDM 2011, ICDM 2014, SSAC 2017, R-AL 2020 Microsoft Research Graduate Fellowship, 2008-2010 Google Student Award Winner, NYAS Machine Learning Symposium, 2009 Yahoo! Key Scientific Challenges Award, 2008

Advising Postdocs Supervised • Uriah Israel, active • Kamyar Azizzadenesheli, 2019-2020, Assistant Professor at Purdue • Yuxin Chen, 2017-2019, Assistant Professor at University of Chicago • Angie Liu, 2018-2021, Assistant Professor at Johns Hopkins Univeresity • Taehwan Kim, 2015-2017, Assistant Professor at UNIST • Ugo Rosolia, 2020-2021, Research Scientist at Amazon • Yanan Sui, 2016-2018, Assistant Professor at Tsinghua University • Romann Weber, 2015-2016, Research Scientist at Disney Research • Rose Yu, 2017-2018, Assistant Professor at UCSD

Ph.D. Students Advised • Jeremy Bernstein, California Institute of Technology, active • Victor Dorobantu, California Institute of Technology, active • Alex Farhang, California Institute of Technology, active • Ivan Jimenez Rodriguez, California Institute of Technology, active • Amy Kejun Li, California Institute of Technology, active • Hao Liu, California Institute of Technology, active • Guanya Shi, California Institute of Technology, active • Jennifer Sun, California Institute of Technology, active • Sabera Talukder, California Institute of Technology, active • Cameron Voloshin, California Institute of Technology, active • Christopher Yeh, California Institute of Technology, active • Eric Zhan, California Institute of Technology, active • Hoang Le, California Institute of Technology, Ph.D. 2019, Postdoctoral Researcher at Microsoft Research • Joseph Marino, California Institute of Technology, Ph.D. 2021, Research Scientist at DeepMind • Ellen Novoseller, California Institute of Technology, Ph.D. 2020, Postdoctoral Re- searcher at UC Berkeley • Jialin Song, California Institute of Technology, Ph.D. 2021, Research Scientist at NVIDIA AI • Stephan Zheng, California Institute of Technology, Ph.D. 2018, Research Scientist at Salesforce AI Teaching Machine Learning & Data Mining. Core machine learning and data mining course offered to graduate students and advanced undergraduates. Taught at Caltech: Winter 2015, Winter 2016, Winter 2017, Winter 2018, Winter 2019, Winter 2020.

Advanced Topics in Machine Learning. Advanced course on contemporary research topics in machine learning. Taught at Caltech: Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021.

Projects in Machine Learning. Project-based course matching students to mentors on projects of mutual interest. Taught at Caltech: Fall 2016, Fall 2017, Winter 2018, Fall 2018, Winter 2019, Fall 2019, Winter 2020.

Tutorials • “Imitation Learning.” co-taught with Hoang M. Le, ICML 2018 Tutorial, Stockholm, Sweden, July 2018. • “Practical Online Retrieval Evaluation.” co-taught with Filip Radlinski, SIGIR 2011 Tutorial, Beijing, China, July 2011. • “Learning to Rank.” co-taught with Filip Radlinski, NESCAI 2008 Tutorial, Ithaca, NY, May 2008 • “An Introduction to Structured Output Learning Using Support Vector Machines.” Microsoft Research Web Learning Group, Redmond, WA, August 2007.

Professional Organizing Committee Activites • Fundraising Chair, AISTATS 2016

Journal Reviewing • Data Mining and Knowledge Discovery • Information Processing & Management • Information Retrieval • Journal of Artificial Intelligence Research • Neural Networks • Transactions on Knowledge and Data Engineering • Transactions on the Web

Conference Reviewing • AAAI 2014, 2015, 2017 (SPC), 2020 (SPC) • ACL 2012 • ACML 2011, 2012, 2014 • AISTATS 2019 (SPC) • CIKM 2012 • COLING 2010, 2014 • COLT 2015 • ECML/PKDD 2008 • EMNLP 2011, 2012 • ICLR 2018, 2019, 2020 • ICML 2007, 2008, 2009, 2010, 2011, 2012, 2013 (AC), 2014, 2016 (AC), 2017 (AC), 2018 (AC), 2019 (AC), 2020 (AC), 2021 (SAC) • IJCAI 2016 (SPC), 2019 (SPC) • KDD 2011, 2015 (SPC), 2016 (SPC), 2017 (SPC) • L4DC 2021 • NAACL-HLT 2012, 2013 • NeurIPS 2008, 2009, 2010, 2011, 2012, 2014, 2015, 2016, 2017, 2018 (AC), 2019 (AC), 2020 (SAC), 2021 (SAC) • SIGIR 2008, 2009, 2010, 2013, 2014 • SoCG 2010 • UAI 2020 (AC) • UBICOMP 2014 • UIST 2015 • WSDM 2011, 2012, 2013, 2014, 2015, 2016 (SPC) • WWW 2011, 2012, 2013, 2014, 2017

Book Reviewing & Editing • Introduction to Information Retrieval, Chapter 18, Matrix decompositions & latent semantic indexing

Other Multi-Agent Behavior Modeling Workshop, @CVPR 2021 Service Learning Meets Combinatorial Algorithms Workshop, @NeurIPS 2020

Real-World Experiment Design and Active Learning Workshop, @ICML 2020

Safety and Robustness in Decision Making Workshop, @NeurIPS 2019

Real-world Sequential Decision Making Worksop, @ICML 2019

AI for Science Workshop, @Caltech, 2018, 2019

Southern California Machine Learning Symposium, @Caltech, November 2016

Large-Scale Sports Analytics Workshop, @KDD 2014, @KDD 2015, @KDD 2016

Personalization Workshop, @NeurIPS 2014, @ICML 2016

Invited Thorsten Joachims, Thomas Hofmann, Yisong Yue, Chun-Nam Yu. (2009) “Predicting Articles Structured Objects with Support Vector Machines.” Communications of the ACM (CACM), Research Highlight, 52(11), 97–104, November 2009. Journal Guanya Shi, Wolfgang Hnig, Xichen Shi, Yisong Yue, Soon-Jo Chung. (2021) “Neural- Papers Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Inter- actions.” IEEE Transactions on Robotics (T-RO), 2021.

Bruce Wittmann, Yisong Yue, Frances Arnold. (2021) “Machine Learning-Assisted Directed Evolution Navigates a Combinatorial Epistatic Fitness Landscape with Minimal Screening Burden.” Cell Systems, August 2021.

Andrew J. Taylor, Victor D. Dorobantu, Yisong Yue, Paulo Tabuada, Aaron D. Ames. (2021) “Sampled-Data Stabilization with Control Lyapunov Functions via Quadratically Constrained Quadratic Programs.” IEEE Control Systems Letters (L-CSS), June 2021.

Yidan Qin, Max Allan, Yisong Yue, Joel Burdick, Mahdi Azizian. (2021) “Learning Invari- ant Representation of Tasks for Robust Surgical State Estimation.” IEEE Robotics and Automation Letters (RA-L), 2021.

Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon- Jo Chung. (2021) “Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems.” IEEE Robotics and Automation Letters (RA-L), April, 2021.

Logan Cross, Jeff Cockburn, Yisong Yue, John ODoherty. (2020) “Using deep reinforce- ment learning to reveal how the brain encodes abstract state-space representations in high- dimensional environments.” Neuron, February, 2021.

Michael R. Maser, Alexander Y. Cui, Serim Ryou, Travis J. DeLano, Yisong Yue, and Sarah E. Reisman. (2021) “Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions.” Journal of Chemical Information and Modeling (JCIM), January, 2021.

Andrew J. Taylor, Andrew Singletary, Yisong Yue, Aaron D. Ames. (2020) “A Control Barrier Perspective on Episodic Learning via Projection-to-State Safety.” In IEEE Control Systems Letters (L-CSS), July 2020.

Benjamin Rivire, Wolfgang Hoenig, Yisong Yue, Soon-Jo Chung. (2020) “GLAS: Global-to- Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learn- ing.” IEEE Robotics and Automation Letters, June, 2020.

Zachary Ross, Yisong Yue, Men-Andrin Meier, Egill Hauksson, Thomas Heaton. (2019) “PhaseLink: A Deep Learning Approach to Seismic Phase Association.” Journal of Geo- physical Research - Solid Earth, DOI:0.1029/2018JB016674R, January, 2019.

Men-Andrin Meier, Zachary Ross, Anshul Ramachandran, Ashwin Balakrishna, Suraj Nair, Peter Kundzicz, Zefeng Li, Jennifer Andrews, Egill Hauksson, Yisong Yue. (2019) “Reli- able Real-time Seismic Signal/Noise Discrimination with Machine Learning.” Journal of Geophysical Research - Solid Earth, DOI:0.1029/2018JB016661, January, 2019.

Long Sha, Patrick Lucey, Yisong Yue, Xinyu Wei, Jennifer Hobbs, Charlie Rohlf, Sridha Sridharan. (2018) “Interactive Sports Analytics: An Intelligent Interface for Utilizing Trajectories for Interactive Sports Play Retrieval and Analytics.” ACM Transactions on Computer-Human Interaction (TOCHI), 25(2), 13:1–13:31, April, 2018.

Siyuan Liu, Yisong Yue, Ramayya Krishnan. (2015) “Non-Myopic Adaptive Route Plan- ning in Uncertain Congestion Environments.” IEEE Transactions on Knowledge Discovery and Engineering (TKDE), 27(9), 2438–2451, DOI:10.1109/TKDE.2015.2411278, September, 2015.

Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims. (2012) “The K-armed Dueling Bandits Problem.” Journal of Computer and System Sciences (JCSS), Special Issue on Learning Theory, DOI:10.1016/j.jcss.2011.12.028, May, 2012.

Olivier Chapelle, Thorsten Joachims, Filip Radlinski, Yisong Yue. (2012) “Large Scale Val- idation and Analysis of Interleaved Search Evaluation.” ACM Transactions on Information Systems (TOIS), 30(1), 6:1–6:41, February, 2012.

Conference Andrew J. Taylor, Victor D. Dorobantu, Sarah Dean, Benjamin Recht, Yisong Yue, Aaron Papers D. Ames. (2021) “Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty.” IEEE Conference on Decision and Control (CDC), December 2021.

Angela Gao, Jorge Castellanos, Yisong Yue, Zachary Ross, Katherine Bouman. (2021) “DeepGEM: Generalized Probablistic Expectation-Maximization Framework for Solving In- verse Problems with Model Mismatch.” Neural Information Processing Systems (NeurIPS), December 2021.

Joseph Marino, Alexandre Pich, Alessandro Davide Ialongo, Yisong Yue. (2021) “Itera- tive Amortized Policy Optimization.” Neural Information Processing Systems (NeurIPS), December 2021.

Guanya Shi, Kamyar Azizzadenesheli, Soon-Jo Chung, Yisong Yue. (2021) “Meta-Adaptive Nonlinear Control: Theory and Algorithms.” Neural Information Processing Systems (NeurIPS), December 2021.

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 “The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions.” Neural Information Processing Systems (NeurIPS), December 2021.

Cameron Voloshin, Hoang M. Le, Nan Jiang, Yisong Yue. (2021) “The Caltech Off-Policy Policy Evaluation Benchmarking Suite.” Neural Information Processing Systems (NeurIPS), December 2021.

Ivan D. Jimenez Rodriguez, Ugo Rosolia, Aaron D. Ames, Yisong Yue. (2021) “Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control.” International Conference on Intelligent Robots and Systems (IROS), September 2021.

Dawna Bagherian, James Gornet, Jeremy Bernstein, Yu-Li Ni, Yisong Yue, Markus Meister. (2021) “Fine-Grained System Identification of Nonlinear Neural Circuits.” ACM Conference on Knowledge Discovery and Data Mining (KDD), August 2021.

Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anand- kumar. (2021) “Competitive Policy Optimization.” Conference on Uncertainty in Artificial Intelligence (UAI), July 2021.

Yang Liu, Jeremy Bernstein, Markus Meister, Yisong Yue. (2021) “Learning by Turning: Neural Architecture Aware Optimisation.” International Conference on Machine Learning (ICML), July 2021. Jennifer J. Sun, Ann Kennedy, Eric Zhan, David J. Anderson, Yisong Yue, Pietro Perona. (2021) “Task Programming: Learning Data Efficient Behavior Representations.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2021.

Kejun Li, Maegan Tucker, Erdem Byk, Ellen Novoseller, Joel W. Burdick, Yanan Sui, Dorsa Sadigh, Yisong Yue, Aaron D. Ames. (2021) “ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes.” International Conference on Robotics and Automation (ICRA), May 2021.

Ayya Alieva, Aiden Aceves, Jialin Song, Stephen Mayo, Yisong Yue, Yuxin Chen. (2021) “Learning to Make Decisions via Submodular Regularization.” International Conference on Learning Representations (ICLR), May 2021.

Dimitar Ho, Hoang M. Le, John Doyle, Yisong Yue. (2021) “Online Robust Control of Non- linear Systems with Large Uncertainty.” International Conference on Artificial Intelligence and Statistics (AISTATS), April 2021.

Cameron Voloshin, Nan Jiang, Yisong Yue. (2021) “Minimax Model Learning.” Interna- tional Conference on Artificial Intelligence and Statistics (AISTATS), April 2021.

Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue. (2021) “Active Learning under Label Shift.” International Conference on Artificial Intelligence and Statistics (AISTATS), April 2021.

Neil Abcouwer, Shreyansh Daftry, Siddarth Venkatraman, Tyler del Sesto, Olivier Toupet, Ravi Lanka, Jialin Song, Yisong Yue, Masahiro Ono. (2021) “Machine Learning Based Path Planning for Improved Rover Navigation.” IEEE Aerospace Conference (AeroConf), March 2021.

Akella Ravi Tej, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Anima Anandkumar, Yisong Yue. (2021) “Deep Bayesian Quadrature Policy Optimization.” AAAI Conference on Artificial Intelligence (AAAI), February 2021.

Jialin Song, Ravi Lanka, Yisong Yue, Bistra Dilkina. (2020) “A General Large Neighborhood Search Framework for Solving Integer Programs.” Neural Information Processing Systems (NeurIPS), December, 2020.

Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman. (2020) “Online Op- timization with Memory and Competitive Control.” Neural Information Processing Systems (NeurIPS), December, 2020.

Jeremy Bernstein, Arash Vahdat, Yisong Yue, Ming-Yu Liu. (2020) “On the distance be- tween two neural networks and the stability of learning.” Neural Information Processing Systems (NeurIPS), December, 2020.

Jeremy Bernstein, Jiawei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar, Yisong Yue. (2020) “Learning compositional functions via multiplicative weight updates.” Neural Information Processing Systems (NeurIPS), December, 2020.

Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman. (2020) “The Power of Predictions in Online Control.” Neural Information Processing Systems (NeurIPS), De- cember, 2020.

Ameesh Shah, Eric Zhan, Jennifer J. Sun, Abhinav Verma, Yisong Yue, Swarat Chaud- huri. (2020) “Learning Differentiable Programs with Admissible Neural Heuristics.” Neural Information Processing Systems (NeurIPS), December, 2020.

Maegan Tucker, Myra Cheng, Ellen Novoseller, Richard Cheng, Yisong Yue, Joel W. Bur- dick, Aaron D. Ames (2020), “Human Preference-Based Learning for High-dimensional Op- timization of Exoskeleton Walking Gaits.” In International Conference on Intelligent Robots and Systems (IROS), October 2020.

Ellen Novoseller, Yibing Wei, Yanan Sui, Yisong Yue, Joel Burdick. (2020) “Dueling Poste- rior Sampling for Preference-Based Reinforcement Learning.” In Conference on Uncertainty in Artificial Intelligence (UAI), August 2020.

Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew Hausknecht. (2020) “Learning Calibratable Policies using Programmatic Style-Consistency.” In International Conference on Machine Learning (ICML), July 2020.

Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, Rose Yu. (2020) “Mul- tiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis.” In Interna- tional Conference on Machine Learning (ICML), July 2020.

Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue. (2020) “Robust Regression for Safe Exploration in Control.” In Conference on Learning for Dynamics and Control (L4DC), June 2020.

Andrew J. Taylor, Andrew Singletary, Yisong Yue, Aaron D. Ames. (2020) “Learning for Safety-Critical Control with Control Barrier Functions.” In Conference on Learning for Dynamics and Control (L4DC), June 2020.

Guanya Shi, Wolfgang Hoenig, Yisong Yue, Soon-Jo Chung. (2020) “Neural-Swarm: Decen- tralized Close-Proximity Multirotor Control Using Learned Interactions.” In International Conference on Robotics and Automation (ICRA), May 2020.

Maegan Tucker, Ellen Novoseller, Claudia Kann, Yanan Sui, Yisong Yue, Joel Burdick, Aaron D. Ames. (2020) “Preference-Based Learning for Exoskeleton Gait Optimization.” In International Conference on Robotics and Automation (ICRA), May 2020.

Jialin Song, Yury S Tokpanov, Yuxin Chen, Dagny Fleischman, Katherine T Fountaine, Yisong Yue, Harry A Atwater. (2020) “Mirrored Plasmonic Filter Design via Active Learn- ing of Multi-Fidelity Physical Models.” IEEE Conference on Lasers and Electro-Optics (CLEO), May 2020.

Baihong Jin, Yingshui Tan, Alexander Nettekoven, Yuxin Chen, Ufuk Topcu, Yisong Yue, Alberto Sangiovanni Vincentelli. (2019) “An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing.” In IEEE International Conference on Machine Learning and Applications (ICMLA), December 2019.

Abhinav Verma, Hoang M. Le, Yisong Yue, Swarat Chaudhuri. (2019) “Imitation-Projected Programmatic Reinforcement Learning.” In Neural Information Processing Systems (NeurIPS), December 2019.

Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue. (2019) “NAOMI: Non- Autoregressive Multiresolution Sequence Imputation.” In Neural Information Processing Systems (NeurIPS), December 2019. Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Maneual Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, Adish Singla. (2019) “Teaching Multiple Concepts to Forgetful Learners.” In Neural Information Processing Systems (NeurIPS), December 2019.

Nikhil Ghosh, Yuxin Chen, Yisong Yue. (2019) “Landmark Ordinal Embedding.” In Neural Information Processing Systems (NeurIPS), December 2019.

Andrew J. Taylor, Victor D. Dorobantu, Meera Krishnamoorthy, Hoang M. Le, Yisong Yue, Aaron D. Ames. (2019) “A Control Lyapunov Perspective on Episodic Learning via Pro- jection to State Stability.” In IEEE Conference on Decision and Control (CDC), December 2019.

Andrew J. Taylor, Victor D. Dorobantu, Hoang M. Le, Yisong Yue, Aaron D. Ames. (2019) “Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems.” In International Conference on Intelligent Robots and Systems (IROS), November 2019.

Jialin Song, Ravi Lanka, Yisong Yue, Masahiro Ono. (2019) “Co-Training for Policy Learn- ing.” In Conference on Uncertainty in Artificial Intelligence (UAI), July 2019.

Mohamadreza Ahmadi, Bo Wu, Yuxin Chen, Yisong Yue, Ufuk Topcu. (2019) “Barrier Certificates for Assured Machine Teaching.” In American Control Conference (ACC), July 2019.

Hoang M. Le, Cameron Voloshin, Yisong Yue. (2019) “Batch Policy Learning under Con- straints.” In International Conference on Machine Learning (ICML), June 2019.

Richard Cheng, Abhinav Verma, Gabor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdick. (2019). “Control Regularization for Reduced Variance Reinforcement Learning Batch Policy Learning under Constraints.” In International Conference on Machine Learning (ICML), June 2019.

Guanya Shi, Xichen Shi, Michael O’Connell, Rose Yu, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, Soon-Jo Chung. (2019) “Neural Lander: Stable Drone Landing Control using Learned Dynamics.” In International Conference on Robotics and Automation (ICRA), Canada, May 2019.

Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey. (2019) “Generating Multi- Agent Trajectories using Programmatic Weak Supervision.” In International Conference on Learning Representations (ICLR), May 2019.

Jialin Song, Yuxin Chen, Yisong Yue. (2019) “A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes.” In International Conference on Artificial Intelligence and Statistics (AISTATS), April 2019.

Kevin Yang, Yuxin Chen, Alycia Lee, Yisong Yue. (2019) “Batched Stochastic Bayesian Op- timization via Combinatorial Constraints Design.”In International Conference on Artificial Intelligence and Statistics (AISTATS), April 2019.

Joseph Marino, Milan Cvitkovic, Yisong Yue. (2018) “A General Method for Amortizing Variational Filtering.” In Neural Information Processes Systems (NeurIPS), December 2018.

Yuxin Chen, Adish Singla, Oisin Mac Aodha, Pietro Perona, Yisong Yue. (2018) “Under- standing the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners.”, In Neural Information Processes Systems (NeurIPS), December 2018. Yanan Sui, Masrour Zoghi, Katja Hofmann, Yisong Yue. (2018) “Advancements in Dueling Bandits.” In International Joint Conference on Artificial Intelligence (IJCAI), Survey Track, July 2018.

Joseph Marino, Yisong Yue, Stephan Mandt. (2018) “Iterative Amortized Inference.” In International Conference on Machine Learning (ICML), July 2018.

Hoang M. Le, Nan Jiang, Alekh Agarwal, Miroslav Dudk, Yisong Yue, Hal Daume III. (2018) “Hierarchical Imitation and Reinforcement Learning.” In International Conference on Machine Learning (ICML), July 2018.

Yanan Sui, Vincent Zhuang, Joel Burdick, Yisong Yue. (2018) “Stagewise Safe Bayesian Optimization with Gaussian Processes.” In International Conference on Machine Learning (ICML), July 2018.

Oisin Mac Aodha, Shihan Su, Yuxin Chen, Pietro Perona, Yisong Yue. (2018) “Teaching Categories to Human Learners with Visual Explanations.” In IEEE Conference on Com- puter Vision and Pattern Recognition (CVPR), June 2018.

Yuxin Chen, Oisin Mac Aodha, Shihan Su, Pietro Perona, Yisong Yue. (2018) “Near- Optimal Machine Teaching via Explanatory Teaching Sets.” In International Conference on Artificial Intelligence and Statistics (AISTATS), April 2018.

Akifumi Wachi, Yanan Sui, Yisong Yue, Masahiro Ono. (2018) “Safe Exploration and Optimization of Constrained MDPs using Gaussian Processes.” In AAAI Conference on Artificial Intelligence (AAAI), February 2018.

Michela Munoz Fernandez, Yisong Yue, and Romann Weber. (2017) “Telemetry Anomaly Detection System using Machine Learning to Streamline Mission Operations.” In IEEE International Conference on Space Mission Challenges for Information Technology (SMC- IT), September 2017.

Yanan Sui, Yisong Yue, Joel Burdick. (2017) “Correlational Dueling Bandits with Applica- tion to Clinical Treatment in Large Decision Spaces.” In International Joint Conference on Artificial Intelligence (IJCAI), August 2017.

Yanan Sui, Vincent Zhuang, Joel Burdick, Yisong Yue. (2017) “Multi-dueling Bandits with Dependent Arms.” In Conference on Uncertainty in Artificial Intelligent (UAI), August 2017.

Hoang M. Le, Yisong Yue, Peter Carr, Patrick Luecy. (2017) “Coordinated Multi-Agent Imitation Learning.” In International Conference on Machine Learning (ICML), August 2017.

Sarah Taylor, Taehwan Kim, Yisong Yue, Moshe Mahler, James Krahe, Anastasio Garcia Rodriguez, Jessica Hodfgins, Iain Matthews. (2017) “A Deep Learning Approach to Gener- alized Speech Animation.” In ACM Conference on Computer Graphics (SIGGRAPH), July 2017.

Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain Matthews and Greg Mori. (2017) “Factorized Variational Autoencoders for Modeling Audience Re- actions to Movies.” In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July, 2017. Eyr´unEyj´olfsd´ottir,Kristin Branson, Yisong Yue, Pietro Perona. (2017) “Learning recur- rent representations for hierarchical behavior modeling.” In International Conference on Learning Representations (ICLR), April, 2017.

Hoang M. Le, Peter Carr, Yisong Yue, Patrick Lucey. (2017) “Data-Driven Ghosting using Deep Imitation Learning.” In MIT Sloan Sports Analytics Conference (SSAC), March, 2017.

Matteo Ronchi, Joon Sik Kim, Yisong Yue. (2016) “A Rotation Invariant Latent Factor Model for Moveme Discovery from Static Poses.” In IEEE International Conference on Data Mining (ICDM), December, 2016.

Stephan Zheng, Yisong Yue, Patrick Lucey. (2016) “Generating Long-term Trajectories Using Deep Hierarchical Networks.” In Neural Information Processing Systems (NeurIPS), December, 2016.

Jianhui Chen, Hoang M. Le, Peter Carr, Yisong Yue, James J. Little. (2016) “Learning Online Smooth Predictors for Real-time Camera Planning using Recurrent Decision Trees.” In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 2016.

Hoang M. Le, Andrew Kang, Yisong Yue, Peter Carr. (2016) “Smooth Imitation Learning for Online Sequence Prediction.” In International Conference on Machine Learning (ICML), June, 2016.

Long Sha, Patrick Lucey, Yisong Yue, Peter Carr, Charlie Rohlf, Iain Matthews. (2016) “Chalkboarding: A New Spatiotemporal Query Paradigm for Sports Play Retrieval.” In ACM Conference on Intelligent User Interfaces (IUI), March, 2016.

Bryan He, Yisong Yue. (2015) “Smooth Interactive Submodular Set Cover.” In Neural Information Processing Systems (NeurIPS), December, 2015.

Taehwan Kim, Yisong Yue, Sarah Taylor, Iain Matthews. (2015) “A Decision Tree Frame- work for Spatiotemporal Sequence Prediction.” In ACM Conference on Knowledge Discov- ery and Data Mining (KDD), August, 2015.

Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue, Sridha Sridharan, Iain Matthews. (2014) “Large-Scale Analysis of Soccer Matches using Spatiotemporal Tracking Data.” In IEEE International Conference on Data Mining (ICDM), December, 2014.

Yisong Yue, Patrick Lucey, Peter Carr, Alina Bialkowski, Iain Matthews. (2014) “Learning Fine-Grained Spatial Models for Dynamic Sports Play Prediction.” In IEEE International Conference on Data Mining (ICDM), December, 2014.

Yisong Yue, Chong Wang, Khalid El-Arini, Carlos Guestrin. (2014) “Personalized Collab- orative Clustering.” In International World Wide Web Conference (WWW), April, 2014.

Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue, Iain Matthews. (2014) “Win at Home and Draw Away: Automatic Formation Analysis Highlighting the Differences in Home and Away Team Behaviors.” In MIT Sloan Sports Analytics Conference (SSAC), February, 2014.

Patrick Lucey, Alina Bialkowski, Peter Carr, Yisong Yue, Iain Matthews. (2014) “How to Get an Open Shot: Analyzing Team Movement in Basketball using Tracking Data.” In MIT Sloan Sports Analytics Conference (SSAC), February, 2014. Siyuan Liu, Yisong Yue, Ramayya Krishnan. (2013) “Adaptive Collective Routing Using Gaussian Process Dynamic Congestion Models.” In ACM Conference on Knowledge Dis- covery and Datamining (KDD), August, 2013.

Stephane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell. (2013) “Learn- ing Policies for Contextual Submodular Prediction.” In International Conference on Ma- chine Learning (ICML), June, 2013.

Yisong Yue, Lavanya Marla, Ramayya Krishnan. (2012) “An Efficient Simulation-based Approach to Ambulance Fleet Allocation and Dynamic Redeployment.” In AAAI Confer- ence on Artificial Intelligence (AAAI), Special Track on Computational Sustainability and Artificial Intelligence, July, 2012.

Yisong Yue, Sue Ann Hong, Carlos Guestrin. (2012) “Hierarchical Exploration for Accel- erating Contextual Bandits.” In International Conference on Machine Learning (ICML), June, 2012.

Yisong Yue, Carlos Guestrin. (2011) “Linear Submodular Bandits and their Application to Diversified Retrieval.” In Neural Information Processing Systems (NeurIPS), December, 2011.

Yisong Yue, Thorsten Joachims. (2011) “Beat the Mean Bandit.” In International Confer- ence on Machine Learning (ICML), June, 2011.

Christina Brandt, Thorsten Joachims, Yisong Yue, Jacob Bank. (2011) “Dynamic Ranked Retrieval.” In ACM Conference on Web Search and Data Mining (WSDM), February, 2011.

Ainur Yessenalina, Yisong Yue, Claire Cardie. (2010) “Multi-level Structured Models for Document-level Sentiment Classification.” In Conference on Empirical Methods in Natural Language Processing (EMNLP), October, 2010.

Yisong Yue, Yue Gao, Olivier Chapelle, Ya Zhang, Thorsten Joachims. (2010) “Learning More Powerful Test Statistics for Click-Based Retrieval Evaluation.” In ACM Conference on Information Retrieval (SIGIR), July, 2010.

Yisong Yue, Rajan Patel, Hein Roehrig. (2010) “Beyond Position Bias: Examining Result Attractiveness as a Source of Presentation Bias in Clickthrough Data.” In International World Wide Web Conference (WWW), April, 2010.

Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims. (2009) “The K-armed Dueling Bandits Problem.” In Conference on Learning Theory (COLT), June, 2009.

Yisong Yue, Thorsten Joachims. (2009) “Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem.” In International Conference on Machine Learning (ICML), June, 2009.

Yisong Yue, Thorsten Joachims. (2008) “Predicting Diverse Subsets Using Structural SVMs.” In International Conference on Machine Learning (ICML), July, 2008.

Yisong Yue, Thomas Finley, Filip Radlinski, Thorsten Joachims. (2007) “A Support Vector Method for Optimizing Average Precision.” In ACM Conference on Information Retrieval (SIGIR), July, 2007.

Selected Jialin Song, Yury Tokpanov, Yuxin Chen, Dagny Fleischman, Kate Fountaine, Harry Atwa- Workshop Papers (Unrefereed) ter, Yisong Yue. (2018) “Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes.” NeurIPS 2018 Workshop on Machine Learning for Molecules and Materials, Montreal, QC, Canada, December, 2018.

Kaushik Krishnan, Lavanya Marla, Yisong Yue. (2016) “Robust Ambulance Allocation Using Risk-based Metrics.” COMSNETS 2016 Workshop on Intelligence Transportation Systems, Bangalore, India, January, 2016.

Stephan Zheng, Yisong Yue. (2015) “Scalable Training of Interpretable Spatial Latent Fac- tor Models.” NeurIPS 2015 Workshop on Non-convex Optimization for Machine Learning, Montreal, QC, Canada, December, 2015.

Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue, Sridha Sridharan, Iain Matthews. (2014) “Identifying Team Style in Soccer using Formations Learned from Spatiotemporal Tracking Data.” ICDM International Workshop on Spatial and Spatio-temporal Data Min- ing (SSTDM), Shenzhen, China, December, 2014.

Jiaji Zhou, Stephane Ross, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell. “Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization.” ICML Workshop on Inferning: Interactions between Inference and Learn- ing, Atlanta, GA, June, 2013.

Yisong Yue. (2009) “Online Gradient Descent using Interactive User Feedback.” NeurIPS Workshop on Analysis and Design of Algorithms for Interactive Machine Learning, Whistler, Canada, December, 2009.

Yisong Yue, Thorsten Joachims. (2008) “Interactively Optimizing Information Systems as a Dueling Bandits Problem.” NeurIPS Workshop on Beyond Search: Computational Intelligence for the Web, Whistler, Canada, December, 2008.

Yisong Yue, Christopher Burges. (2007) “On Using Stochastic Perturbation Stochastic Approximation for Learning to Rank; and, the Empirical Optimality of LambdaRank.” NeurIPS Workshop on Machine Learning for the Web, Whistler, Canada, December, 2007.

Patents Joel W. Burdick, Yanan Sui, Yisong Yue, Nicholas A. Terrafranca. (2019) “Dueling Bandits Algorithm for Neuromodulation Therapy.” US Patent 20190374777

George Peter Carr, Jianhui Chen, Yisong Yue. (2019) “Automatic device operation and object tracking based on learning of smooth predictors.” US Patent 14881010

George Peter Carr, Zhiwei Deng, Rajitha Navarathna, Yisong Yue, Stephan Mandt. (2019) “Factorized Variational Autoencoders.” US Patent 20190026631

Patrick Lucey, Alina Bialkowski, George Peter Carr, Iain Matthews, Yisong Yue. (2018) “Analysis of team behaviors using role and formation information.” US Patent 10062033

George Peter Carr, Hoang M Le, Yisong Yue. (2018) “Data-driven ghosting using deep imitation learning.” US Patent 15830710

Patrick Lucey, Alina Bialkowski, George Peter Carr, Iain Matthews, Yisong Yue. (2016) “Analysis of team behaviors using role and formation information.” US Patent 14498977

Sarah Taylor, Taehwan Kim, Yisong Yue, Iain Matthews. (2015) “Generating Speech Ani- mation in Synchronization with a Target Audio Speech.” US Patent 14614214 Additional “Improving Policy Learning via Programmatic Domain Knowledge.” Presentations • California Institute of Technology, April 2021 • Emory University, September 2020 • Stanford University, July 2020

“Learning for Safety-Critical Control in Dynamica Systems.” • California Institute of Technology, April 2021 • Learning Meets Control Seminar, January 2021 • Physics Meets ML Seminar, July 2020

“Artificial Intelligence: How it Works and What it Means for the Future.” • Caltech Watson Lecture, January 2021

“AI for Adaptive Experiment Design.” • Microsoft Research, Webinar Series on Directions in ML: AutoML and Automating Algorithms, December 2020. • Jet Propulsion Laboratory, La Canada, CA, May 2019 • Google, Mountain View, CA, March 2019 • California Institute of Technology, Pasadena, CA, January 2019

“Learning to Optimize as Policy Learning.” • Princeton University, October 2020 • TTIC Workshop on Automated Algorithm Design, Chicago, IL, August 2019

“New Frontiers in Imitation Learning.” • University of California Santa Barbara, Santa Barbara, CA, January 2020 • University of Chicago, Chicago, IL, November 2019 • University of Illinois at Urbana-Champaign, Urbana, IL, November 2019 • Carnegie Mellon University, Pittsburgh, PA, April 2019 • University of California Los Angeles, Los Angeles, CA, November 2018 • University of Maryland, College Park, MD, October 2018 • California Institute of Technology, Pasadena, CA, October 2018 • Microsoft Research, Redmond, WA, October 2018 • University of Washington, Seattle, WA, October 2018 • Rice University, Houston, TX, September 2018 • Intel AI DevCon, San Francisco, CA, May 2018 • Johns Hopkins University, Baltimore, MD, April 2018 • Google Brain, Mountain View, CA, April 2018 • University of Texas, Austin, TX, March, 2018 • Cornell University, Ithaca, NY, January, 2018 • University of Oxford, Oxford, UK, November, 2017 • Gatsby Computational Neuroscience Unit, London, UK, November, 2017 • Disney Research, Zurich, Switzerland, November, 2017 • Columbia University, New York City, NY, November, 2017 • New York University, New York City, NY, October, 2017 • Southern California Machine Learning Symposium, Los Angeles, CA, October, 2017 • Microsoft Research Colloquium, Cambridge, MA, September, 2017 • Amazon, Seattle, WA, July, 2017

“Policy Learning with Certifiable Guarantees.” • University of California Los Angeles, Los Angeles, CA, October, 2019

“Real-World Bayesian Optimization.” • KDD 2019 Workshop for Data Collection, Curation, and Labeling for Mining and Learning, Anchorage, AK, August 2019 • ICML 2019 Workshop on Human-in-the-Loop Learning, Long Beach, CA, June 2019

“Two Vignettes in Robust Detection and Adversarial Analysis in Control.” • CVPR 2019 Workshop on Adversarial Machine Learning in Real-World Computer Vision Systems, Long Beach, June 2019

“Structured Imitation & Reinforcement Learning.” • NeurIPS 2018 Workshop on Imitation Learning and its Challenges in Robotics, Mon- treal, QC, Canada, December 2018

“The Dueling Bandits Problem.” • Microsoft Research, Redmond, WA, October 2018 • Massachusetts Institute of Technology, Cambridge, MA, September, 2017 • SDM 2017 Machine Learning Methods for Recommender Systems Workshop, Houston, TX, April, 2017 • Carnegie Mellon University, Pittsburgh, PA, October, 2016 • Algorithms for Human Machine Interaction Workshop, Berkeley, CA, November, 2015 • University of California San Diego, La Jolla, CA, October, 2015 • University of Illinois at Chicago, Chicago, IL, October, 2015 • Data-Driven Algorithmics Workshop, Cambridge, MA, September, 2015

“Imitation + Inference.” • ICML 2018 Workshop on Tractable Probabilistic Models, Stockholm, Sweden, July 2018

“Machine Teaching for Human Learners.” • IJCAI 2018 Workshop on Humanizing AI, Stockholm, Sweden, July 2018

“Learning to Optimize for Structured Output Spaces.” • University of California Santa Barbara, Santa Barbara, CA, April 2017 • California Institute of Technology, Pasadena, CA, January, 2017 • Jet Propulsion Laboratory, Pasadena, CA, November, 2016 • Carnegie Mellon University, Pittsburgh, PA, October, 2016 “Building Predictive Behavioral Models via Large Scale Imitation Learning.” • Machine Learning and Human Behavior Symposium, Irvine, CA, March, 2017

“A Decision Tree Framework for Data-Driven Speech Animation.” • STATS LLC, Chicago, IL, October, 2016 • Carnegie Mellon University, Pittsburgh, PA, March, 2016 • USC Information Sciences Institute, Los Angeles, CA, January, 2016 • ETH Z¨urich, Z¨urich, Switzerland, December, 2015 • University of Southern California, Los Angeles, CA, October, 2015

“Machine Learning for Sports, Animation & Medicine.” • Reflections Projections, Urbana, IL, October, 2015

“Machine Learning for Personalized Clustering.” • NeurIPS 2014 Workshop on Human Propelled Machine Learning, Montreal, QC, Canada, December, 2014 • Carnegie Mellon University, Pittsburgh, PA, October, 2014

“Balancing the Explore/Exploit Tradeoff in Interactive Structured Prediction.” • Cornell University, Ithaca, NY, December, 2014 • University of California Irvine, Irvine, CA, November, 2014 • California Institute of Technology, Pasadena, CA, October, 2014 • NeurIPS 2013 Workshop on Discrete and Combinatorial Problems in Machine Learn- ing, South Lake Tahoe, NV, December, 2013

“Automated Decision Making Under Uncertainty: Applications to Urban Transportation Systems and Beyond.” • NYU CUSP Workshop on Social Media and Peer Networks, New York City, NY, July 2013

“Learning with Humans in the Loop.” • Disney Research, Pittsburgh, PA, May, 2013 • University of Virginia, Charlottesville, VA, April 2013 • University of California Los Angeles, Los Angeles, CA, April 2013 • Yale University, New Haven, CT, April 2013 • University of Michigan, Ann Arbor, MI, March 2013 • Washington University in St. Louis, St. Louis, MO, March 2013 • Microsoft Research, Redmond, WA, March 2013 • University of Utah, Salt Lake City, UT, March 2013 • California Institute of Technology, Pasadena, CA, March 2013 • Dartmouth College, Hanover, NH, February 2013 • University of Maryland, College Park, MD, February 2013 • Purdue University, West Lafayette, IN, February 2013 • University of Rochester, Rochester, NY, February 2013 • Harvard University, Cambridge, MA, February 2013 • Johns Hopkins University, Baltimore, MD, February 2013 • Northeastern University, Boston, MA, February 2013 • Boston University, Boston, MA, February 2013 • University of Connecticut, Storrs, CT, January 2013

“Optimizing Recommender Systems as a Submodular Bandits Problem.” • Google, Mountain View, CA, June 2013 • University of Toronto, Toronto, ON, Canada, November 2012 • Stanford University, Stanford, CA, October 2012 • University of California San Diego, La Jolla, CA, June 2012 • Carnegie Mellon University, Pittsburgh, PA, March 2012 • University of Washington, Seattle, WA, February 2012

“An Introduction to Structural SVMs and its Application to Information Retrieval.” • University of California Berkeley, Berkeley, CA, October 2012 • Carnegie Mellon University, Pittsburgh, PA, November 2010

“Practical and Reliable Retrieval Evaluation Through Online Experimentation.” • WSDM 2012 Workshop on Web Search Click Data, Seattle, WA, February 2012

“An Interactive Learning Approach to Optimizing Information Retrieval Systems.” • Carnegie Mellon University, Pittsburgh, PA, September 2010 • Yahoo! Research, Santa Clara, CA, August 2010 • Google Z¨urich, Z¨urich, Switzerland, July 2010 • Microsoft Research Asia, Beijing, China, June 2010

“New Learning Frameworks for Information Retrieval.” • Microsoft Research, Redmond, WA, March 2010 • Google, Mountain View, CA, March 2010 • Johns Hopkins University, Baltimore, MD, March 2010 • Yahoo! Research, Sunnyvale, CA, February 2010 • Carnegie Mellon University, Pittsburgh, PA, February 2010 • Cornell University, Ithaca, NY, February 2010 • IBM TJ Watson, Hawthorne, NY, December 2009

“Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem.” • New York Academy of Sciences Machine Learning Symposium, New York, NY, Novem- ber 2009 • Cornell University, Ithaca, NY, February 2009

“Diversified Retrieval as Structured Prediction.” • Google, Mountain View, CA, August 2009 • SIGIR 2009 Workshop on Redundancy, Diversity, and Interdocument Relevance, Boston, MA, July 2009 • Cornell University, Ithaca, NY, April 2008

“Towards Interactive Approaches to Learning to Rank.” • SIGIR 2009 Workshop on Learning to Rank, Boston, MA, July 2009

“Information Retrieval as Structured Prediction.” • University of Massachusetts Amherst, Amherst, MA, April 2009 • Microsoft Research Asia, Beijing, China, August 2008

“A Support Vector Method for Optimizing Average Precision.” • Microsoft Research Machine, Redmond, WA, July 2007 • Cornell University, Ithaca, NY, April 2007

Research DARPA PAI: HR00111890035, “Physics-infused Learning for Autonomous Dynamic Robots”, Grants September 2018. Awarded JPL PDF: IAMS100379, “Scalable Risk-aware Autonomy using Imitation Learning”, Septem- ber, 2017.

NSF CPS: Frontiers, “Collaborative Research: Data-Driven Cyberphysical Systems”, Au- gust, 2017

NSF AitF: #1637598, “Algorithmic Challenges in Smart Grids: Control, Optimization & Learning”, October 2016 to September 2020.

JPL PDF: IAMS100224, “Risk-aware Machine Learning for Resilient Space Exploration”, June, 2016.

NSF RI: Medium: #1564330, “Drosophila Behavior to Sports Analytics: Automated Dis- covery of Macro-Variables from Raw Spatiotemporal Data”, May, 2016 to April, 2020.