Schedule
The conference will take place at the Andrew Wiles Building, Mathematical Institute, University of Oxford. All tutorial, keynote, oral & poster presentation locations refer to this building; specific rooms are provided in the tables below.
Note that the banquet will take place at Reuben College, which is approximately 20 minutes walking distance from the Mathematical Institute.
Monday, July 15
Three tutorials are offered - one in the morning in a plenary format (single-track), and two in the afternoon in a semi-plenary format (two parallel-tracks).
| Time | Location | Event |
|---|---|---|
|
08:30 – 09:00 |
Mezzanine | Registration |
|
09:00 – 10:30 |
L1 |
Distributionally Robust Optimization for Control (Part 1), Wolfram Wiesemann and Daniel Kuhn Chair: Simone Garatti |
|
10:30 – 11:00 |
Mezzanine | Coffee Break |
|
11:00 – 12:30 |
L1 |
Distributionally Robust Optimization for Control (Part 2), Wolfram Wiesemann and Daniel Kuhn Chair: Simone Garatti |
|
12:30 – 14:00 |
Lunch Break | |
|
14:00 – 15:30 |
L1 |
Learning under Requirements: Supervised and Reinforcement Learning with Constraints (Part 1), Alejandro Ribeiro, Luiz Chamon, Miguel Calvo-Fullana, and Santiago Paternain Chair: Konstantinos Gatsis |
| L3 |
Safety Filters for Control: Concepts, Theory and Practice (Part 1), Melanie Zeilinger and Claire Tomlin Chair: Simone Garatti |
|
|
15:30 – 16:00 |
Mezzanine | Coffee Break |
|
16:00 – 17:30 |
L1 |
Learning under Requirements: Supervised and Reinforcement Learning with Constraints (Part 2), Alejandro Ribeiro, Luiz Chamon, Miguel Calvo-Fullana, and Santiago Paternain Chair: Konstantinos Gatsis |
| L3 |
Safety Filters for Control: Concepts, Theory and Practice (Part 2), Melanie Zeilinger and Claire Tomlin Chair: Simone Garatti |
|
|
17:30 – 19:00 |
Mezzanine | Welcome Drinks Reception |
Tuesday, July 16
| Time | Location | Event |
|---|---|---|
|
08:30 – 09:00 |
Mezzanine | Registration |
|
09:00 – 09:15 |
L1 |
Opening remarks Antonis Papachristodoulou |
|
09:15 – 10:00 |
L1 |
Keynote: Representation-based Learning and Control for Dynamical Systems Na Li (Harvard University) Chair: Kostas Margellos |
|
10:00 – 11:00 |
L1 |
Oral Presentations – Session 1: Statistical Learning & Neural Networks (4 x 15min) Chair: Simone Garatti
Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Re-Training, Henrik Hose, Alexander Gräfe and Sebastian Trimpe
Rademacher Complexity of Neural ODEs via Chen-Fliess Series, Joshua Hanson and Maxim Raginsky
Data-Driven Robust Covariance Control for Uncertain Linear Systems, Joshua Pilipovsky and Panagiotis Tsiotras
Data Driven Verification of Positive Invariant Sets for Discrete, Nonlinear Systems, Amy K. Strong and Leila J. Bridgeman |
|
11:00 – 11:30 |
Mezzanine | Coffee Break |
|
11:30 – 12:15 |
L1 |
Keynote: Learning Enabled Multi-Agent Systems in Societal Systems Transformation S. Shankar Sastry (UC Berkeley) Chair: Alessandro Abate |
|
12:15 – 13:30 |
Mezzanine | Lunch Break |
|
13:30 – 14:30 |
South Mezzanine |
Poster Session 1 |
|
14:30 – 15:15 |
L1 |
Keynote: Optogenetic Feedback Control of Gene Expression in Single Cells Mary Dunlop (Boston University) Chair: Antonis Papachristodoulou |
|
15:15 – 16:15 |
L1 |
Oral Presentations – Session 2: Optimization and Uncertainty Quantification (4 x 15min) Chair: Mark Cannon
Inverse Optimal Control as an Errors-in-Variables Problem, Rahel Rickenbach, Anna Scampicchio and Melanie Zeilinger
Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for the Model-free LQR, Leonardo Felipe Toso, Donglin Zhan, James Anderson and Han Wang
Nonasymptotic Regret Analysis of Adaptive Linear Quadratic Control with Model Misspecification, Bruce Lee, Anders Rantzer and Nikolai Matni
Towards Model-Free LQR Control over Rate-Limited Channels, Aritra Mitra, Lintao Ye and Vijay Gupta |
|
16:15 – 16:45 |
Mezzanine | Coffee Break |
|
16:45 – 17:45 |
South Mezzanine |
Poster Session 2 |
|
17:45 – 18:00 |
L1 |
Chair: Maryam Kamgarpour |
|
19:00 – 22:00 |
Banquet Dinner |
Wednesday, July 17
| Time | Location | Event |
|---|---|---|
|
08:30 – 09:00 |
Mezzanine | Registration |
|
09:00 – 09:45 |
L1 |
Keynote: The state of optimal and learning control in the 2020s Jonas Buchli (DeepMind) Chair: Kostas Margellos |
|
09:45 – 10:45 |
L1 |
Oral Presentations – Session 3: Learning for Control (4 x 15min) Chair: Konstantinos Gatsis
Uncertainty Quantification of Set-Membership Estimation in Control and Perception: Revisiting the Minimum Enclosing Ellipsoid, Yukai Tang, Jean-Bernard Lasserre and Heng Yang
Learning Robust Policies for Uncertain Parametric Markov Decision Processes, Luke Rickard, Alessandro Abate and Kostas Margellos
System-level Safety Guard: Safe Tracking Control through Uncertain Neural Network Dynamics Models, Xiao Li, Yutong Li, Anouck Girard and Ilya Kolmanovsky
In vivo learning-based control of microbial populations density in bioreactors, Sara Maria Brancato, Davide Salzano, Francesco De Lellis, Davide Fiore, Giovanni Russo and Mario di Bernardo |
|
10:45 – 11:15 |
Mezzanine | Coffee Break |
|
11:15 – 12:00 |
L1 |
Keynote: Inductive Biases for Robot Reinforcement Learning Jan Peters (TU Darmstadt) Chair: Antonis Papachristodoulou |
|
12:00 – 13:00 |
Mezzanine | Lunch Break |
|
13:00 – 14:00 |
South Mezzanine |
Poster Session 3 |
|
14:00 – 14:45 |
L1 |
Keynote: Efficient & Realistic Simulation for Autonomous Driving Shimon Whiteson (University of Oxford) Chair: Alessandro Abate |
|
14:45 – 15:45 |
L1 |
Oral Presentations – Session 4: Reinforcement Learning for Control and Game Theory (4 x 15min) Chair: Jack Umenberger
Õ(T-1) Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games, Weichao Mao, Haoran Qiu, Chen Wang, Hubertus Franke, Zbigniew Kalbarczyk and Tamer Başar
Learning ϵ-Nash Equilibrium Policies in Stochastic Games with Unknown Independent Chains Using Online Mirror Descent, Tiancheng Qin and S. Rasoul Etesami
Bounded Robustness in Reinforcement Learning via Lexicographic Objectives, Daniel Jarne Ornia, Licio Romao, Lewis Hammond, Manuel Mazo Jr and Alessandro Abate
Conservative Model-based Imitation Learning, Victor Kolev, Rafael Rafailov, Kyle Hatch, Jiajun Wu and Chelsea Finn |
|
15:45 – 16:15 |
Mezzanine | Coffee Break |
|
16:15 – 17:15 |
South Mezzanine |
Poster Session 4 |
|
17:15 – 17:30 |
L1 |
Closing Remarks Alessandro Abate |
