Agenda
Learning for Dynamics and Control (L4DC)
Timings are in PDT
Day 1: Thursday, June 11th, 2020
08:30-08:35: Welcome and Introduction by organizers
08:35-09:05: Karen Willcox - Predictive digital twins: Where data-driven learning meets physics-based modeling
09:05-09:15: Break
09:15-10:15: Dynamics Learning II (4 talks)
10:15-10:30: Break
10:30-11:15: Policy Learning II (3 talks)
11:15-11:30: Break
11:30 – 12:00: Catherine Wolfram - Measuring the Socioeconomic Returns to High Quality Electricity
Day 2: Friday, June 12th, 2020
08:40-08:45: Welcome to 2nd Day by organizers
08:45-09:15: Leslie Kaelbling - Doing for our robots what nature did for us
09:15-09:45: John Lygeros - Data Enabled Predictive Control: Stochastic systems and implicit dynamic predictors
09:45-10:00: Break
10:00-11:00: Policy Learning I (4 talks)
10:45-11:00: Break
11:15-12:00: Dynamics Learning I (3 talks)
12:00 – 12:15: Break
12:15-12:45: Chelsea Finn - Extrapolation via Adaptation
Dynamics Learning Talks
Dynamics Learning I
Finite Sample System Identification: Optimal Rates and the Role of Regularization
Yue Sun, Samet Oymak and Maryam Fazel
Sample Complexity of Kalman Filtering for Unknown Systems
Anastasios Tsiamis, Nikolai Matni and George Pappas
A Spatially and Temporally Attentive Joint Trajectory Prediction Framework for Modeling Vessel Intent
Jasmine Sekhon and Cody Fleming
Dynamics Learning II
Learning to Correspond Dynamical Systems
Nam Hee Kim, Zhaoming Xie and Michiel van de Panne
Learning Dynamical Systems with Side Information
Amir Ali Ahmadi and Bachir El Khadir
Learning nonlinear dynamical systems from a single trajectory
Dylan Foster, Tuhin Sarkar and Alexander Rakhlin
Universal Simulation of Dynamical Systems by Recurrent Neural Nets
Joshua Hanson and Maxim Raginsky
Policy Learning Talks
Policy Learning I
Policy Optimization for H_2 Linear Control with H_infinity Robustness Guarantee: Implicit Regularization and Global Convergence
Kaiqing Zhang, Bin Hu and Tamer Basar
Scalable Reinforcement Learning of Localized Policies for Multi-Agent Networked Systems
Guannan Qu, Adam Wierman and Na Li
Learning Convex Optimization Control Policies
Akshay Agrawal, Shane Barratt, Stephen Boyd and Bartolomeo Stellato
Online Data Poisoning Attacks
Xuezhou Zhang, Xiaojin Zhu and Laurent Lessard
Policy Learning II
Data-driven distributionally robust LQR with multiplicative noise
Peter Coppens, Mathijs Schuurmans and Panagiotis Patrinos
Learning the model-free linear quadratic regulator via random search
Hesameddin Mohammadi, Mihailo R. Jovanovic' and Mahdi Soltanolkotabi
Optimistic robust linear quadratic dual control
Jack Umenberger and Thomas B Schon