Nuit Blanche: Saturday Morning Videos: L4DC: Learning for Dynamics and Control, May 30 & 31, 2019, MIT

Saturday, June 08, 2019

Saturday Morning Videos: L4DC: Learning for Dynamics and Control, May 30 & 31, 2019, MIT

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The L4DC: Learning for Dynamics and Control conference took place on May 30 & 31, 2019 at MIT. From the website page:

Over the next decade, the biggest generator of data is expected to be devices which sense and control the physical world.
This explosion of real-time data that is emerging from the physical world requires a rapprochement of areas such as machine learning, control theory, and optimization. While control theory has been firmly rooted in tradition of model-based design, the availability and scale of data (both temporal and spatial) will require rethinking of the foundations of our discipline. From a machine learning perspective, one of the main challenges going forward is to go beyond pattern recognition and address problems in data driven control and optimization of dynamical processes.
Our overall goal is to create a new community of people that think rigorously across the disciplines, asks new questions, and develops the foundations of this new scientific area

The organizers were: Ali Jadbabaie, George J. Pappas, Pablo Parrilo, Ben Recht, Melanie Zellinger.

Here are the videos followed by the posters at the conference:

Introductory remarks: Ali Jadbabaie (MIT) and Benjamin Recht (UC Berkeley)

Manfred Morari (University of Pennsylvania): “A Practitioner’s Perspective”

Francesco Borrelli (UC Berkeley): “Learning to Predict and Control”

Angela Schoellig (University of Toronto): “Machine Learning for Robotics: Safety and Performance Guarantees for Learning-Based Control”

Maryam Fazel (University of Washington): “Gradient based methods for linear system control and identification”

Stefano Soatto (UC Berkeley): “Dynamics and Control of Differential Learning”

Anca Dragan (UC Berkeley): “An Optimization-Centric Theory of Mind for Human-Robot Interaction”

Cathy Wu (Microsoft Research/MIT): “Integrating Autonomy into Urban Systems”

Planning and Discussion Session

Emma Brunskill (Stanford University): “Efficient Reinforcement Learning When Data is Costly”

Andreas Krause (ETH Zuerich): “Safe Exploration in Reinforcement Learning”

Claire Tomlin (UC Berkeley): “Safe Learning in Robotics”

Dorsa Sadigh (Stanford University): “Influencing Interactive Mixed-Autonomy Systems”

Rene Vidal (Johns Hopkins University): “From Optimization Algorithms to Continuous Dynamical Systems and Back”

Emo Todorov (University of Washington): “Acceleration-based methods for trajectory optimization through contacts”

Russ Tedrake (MIT): “Learning manipulation — and why I (still) like F=ma”

John Tsitsiklis (MIT): “The Shades of Reinforcement Learning”

Sergey Levine (UC Berkeley): “Robots That Learn By Doing”

Sham Kakade (University of Washington): “A No Regret Algorithm for Robust Online Adaptive Control”

Anima Anandkumar (CalTech): “Infusing Physics and Structure into Machine Learning”

Zico Kolter (Carnegie Mellon University): “Cheating with neural networks: How to make the networks you hate obey the constraints you love”

Posters

Murad Abu-Khalaf, Sertac Karaman & Daniela Rus, MIT, “Shared Linear Quadratic Regulation Control: A Reinforcement Learning Approach” PDF
Aaron Ames & Andrew J. Taylor, CalTech, “A Control Lyapunov Function Approach to Episodic Learning” PDF
Anuradha M. Annaswamy, MIT, “Stable and Fast Learning with Momentum and Adaptive Rates” PDF
Thomas Beckers, Technical University of Munich, “Gaussian Process Based Identification and Control with Formal Guarantees” PDF
Kostas Bekris, Rutgers University, “Closing the Reality Gap of Physics-Based Robot Simulation Through Task-Oriented Bayesian Optimization”
Julian Berberich, University of Stuttgart, “Data-Driven Model Predictive Control with Stability and Robustness Guarantees” PDF
Tom Bertalan, MIT, “On Learning Hamiltonian Systems from Data”
Calin Belta & Xiao Li, Boston University, “A Formal Methods Approach to Reinforcement Learning For Robotic Control”
Nicholas M. Boffi, Harvard University, and Jean-Jacques Slotine, MIT, “A continuous-time analysis of distributed stochastic gradient”
Byron Boots, Georgia Institute of Technology, “An Online Learning Approach to Model Predictive Control”
Octavia Camps, Northeastern University College of Engineering, “KW-DYAN: A Recurrent Dynamics-Based Network for Video Prediction” PDF
Bugra Can, Rutgers University, “Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances”
Pratik Chaudhari, Amazon Web Services, “P3O: Policy-on Policy-off Policy Optimization”
Alessandro Chiuso, ETH Zuerich, “CoRe: Control Oriented Learning – A Regularisation-Based Approach”
Glen Chou, University of Michigan, “Learning Constraints from Demonstrations”
Claus Danielson, Ankush Chakrabarty, Stefano Di Cairano, Mitsubishi Electric Research Laboratories, “Invariance for Safe Learning in Constraint-Enforcing Control”
Adithya Devraj, University of Florida, “Stochastic Approximation and the Need for Speed” PDF
Vikas Dhiman, UC San Diego, “Model-based Transfer Learning of Skills across Robots and Tools”
Zhe Du, University of Michigan, “Online Robust Switched System Identification”
Alec Farid, Princeton University, “PAC-Bayes Control: Learning Policies that Provably Generalize to Novel Environments” PDF
Dylan Foster, MIT, “Model Selection for Contextual Bandits”
Travis Gibson, Harvard Medical School, “Connections Between Adaptive Control and Machine Learning”
Stephanie Gil, Arizona State University, “Generalized Rollout Algorithms for POMDP with Application to Sequential Repair Problems”
Mert Gurbuzbalaban, Rutgers University, “Robust Accelerated Gradient Methods”
Josiah Hanna, University of Texas, “Robot Learning in Simulation with Action Transformations”
Hamed Hassani, University of Pennsylvania, “Distributed Scenarios in Submodular Optimization”
Jonathan How, MIT, “Knowledge Transfer via Learning to Teach in Cooperative Multiagent Reinforcement Learning”
Ameya Jagtap, Brown University, “Time-Parallel and Fractional Physics-Informed Neural Networks for Solving Transient PDEs” PDF
Yassir Jedra, KTH Royal Institute of Technology in Stockholm, “Sample Complexity Lower Bounds for Linear System Identification”
Angjoo Kanazawa, UC Berkeley, “SFV: Reinforcement Learning of Physical Skills from Videos”
Bahir El Kadir & Amir Ali Ahmadi, Princeton University, “Learning Dynamical Systems With Side Information”
Reza Khodayi-mehr, Duke University, “Model-Based Learning of Turbulent Flows using Mobile Robots” PDF
Dong-Ki Kim, MIT, “Knowledge Transfer via Learning to Teach in Cooperative Multiagent Reinforcement Learning”
George Kissas & Yibo Yang, University of Pennsylvania, “Learning the Flow Map of Dynamical Systems with Self-Supervised Neural Runge-Kutta Networks”
Alec Koppel, University of Pennsylvania, “Global Convergence of Policy Gradient Methods: A Nonconvex Optimization Perspective”
Abdul Rahman Kreidieh, UC Berkeley, “Scalable methods for the control of mixed autonomous system”
Nevena Lazic, Google, “POLITEX: Regret Bounds for Policy Iteration using Expert Prediction” PDF
Armin Lederer, Technical University of Munich, “Stable Feedback Linearization and Optimal Control for Gaussian Processes” PDF
Na Li, Harvard University, “The Role of Prediction in Online Control”
Jason Liang, MIT, “Learning the Contextual Demand Curve in Repeated Auctions”
Nikolai Matni, UC Berkeley, “Robust Guarantees for Perception-Based Control”
Jared Miller, Yang Zheng, Biel Roig-Solvas, Mario Sznaier, Antonis Papachristodoulou, Northeastern University/Harvard University/University of Oxford, “Chordal Decomposition in Rank Minimized SDPs”
Yannis Paschalidis, Boston University, “Distributionally Robust Learning and Applications to Predictive and Prescriptive Health Analytics” PDF
Panagiotis Patrinos & Mathijs Schuurmans, KU Leuven, “Safe Learning-Based Control of Stochastic Jump Linear Systems: A Distributionally Robust Approach” PDF
Amirhossein Reisizadeh, UC Santa Barbara, “Robust and Communication-Efficient Collaborative Learning” PDF
Anders Rantzer, Lund University, “On the Non-Robustness of Certainty Equivalence Control”
Lilian Ratliff, Sam Burden, Sam Coogan, Benjamin Chasnov & Tanner Fiez, University of Washington, “Certifiable Algorithms for Learning and Control in Multiagent Systems” PDF
Alejandro Ribeiro, University of Pennsylvania, “Know Your Limits: Learning Feasible Specifications Using Counterfactual Optimization”
Thomas Schön, Uppsala University, “Robust Exploration for Data-Driven Linear Quadratic Control”
Artin Spiridonof, Boston University, “Network Independence in Distributed Optimization” PDF
Lili Su, MIT, “Distributed Learning and Estimation in the Presence of Byzantine Agents”
Friedrich Solowjow & Sebastian Trimpe, Max Planck Institute for Intelligent Systems – Stuttgart, Germany, “Event-Triggered Learning”
Karan Singh, Princeton University, “Online Control with Adversarial Disturbances”
Madeleine Udell, Cornell University, “OBOE: Collaborative Filtering for Automated Machine Learning” PDF
Min Wen, University of Pennsylvania, “Constrained Cross-Entropy Method for Safe Reinforcement Learning”
Zhi Xu, MIT, “On Reinforcement Learning Using Monte Carlo Tree Search with Supervised Learning: Non-Asymptotic Analysis”
Lin F. Yang, Princeton University, “Sample-Optimal Parametric Q-Learning with Linear Transition Models” PDF
Yan Zhang, Duke University, “Distributed Off-Policy Actor-Critic Reinforcement Learning with Policy Consensus”

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