5 papers
Learning-Based Distributionally Robust Model Predictive Control of Markovian Switching Systems with Guaranteed Stability and Recursive Feasibility
Mathijs Schuurmans, Panagiotis Patrinos
We present a data-driven model predictive control scheme for chance-constrained Markovian switching systems with unknown switching probabilities. Using samples of the underlying Ma…
Learning-Based Risk-Averse Model Predictive Control for Adaptive Cruise Control with Stochastic Driver Models
Mathijs Schuurmans, Alexander Katriniok, Hongtei Eric Tseng +1
We propose a learning-based, distributionally robust model predictive control approach towards the design of adaptive cruise control (ACC) systems. We model the preceding vehicle a…
Data-driven distributionally robust LQR with multiplicative noise
Peter Coppens, Mathijs Schuurmans, Panagiotis Patrinos
We present a data-driven method for solving the linear quadratic regulator problem for systems with multiplicative disturbances, the distribution of which is only known through sam…
Risk-averse risk-constrained optimal control
Pantelis Sopasakis, Mathijs Schuurmans, Panagiotis Patrinos
Multistage risk-averse optimal control problems with nested conditional risk mappings are gaining popularity in various application domains. Risk-averse formulations interpolate be…
Efficient semantic image segmentation with superpixel pooling
Mathijs Schuurmans, Maxim Berman, Matthew B. Blaschko
In this work, we evaluate the use of superpixel pooling layers in deep network architectures for semantic segmentation. Superpixel pooling is a flexible and efficient replacement f…