7 citations · 15 across the 17 of their papers we have counts for
5 papers · 1 filter
Domain-Aware Machine Learning for Accelerating MILP-Based Motion Planning with Temporal Logic and Chance Constraints
Junyang Cai, Weimin Huang, Brendan Long +4
Motion-planning problems with temporal-logic or chance constraints are often encoded as mixed-integer linear programs (MILPs). Although these encodings provide rigorous specificati…
Repairing Learning-Enabled Controllers While Preserving What Works
Pengyuan Lu, Matthew Cleaveland, Oleg Sokolsky +2
Learning-enabled controllers have been adopted in various cyber-physical systems (CPS). When a learning-enabled controller fails to accomplish its task from a set of initial states…
Causal Repair of Learning-enabled Cyber-physical Systems
Pengyuan Lu, Ivan Ruchkin, Matthew Cleaveland +2
Models of actual causality leverage domain knowledge to generate convincing diagnoses of events that caused an outcome. It is promising to apply these models to diagnose and repair…
Conformal Prediction Regions for Time Series using Linear Complementarity Programming
Matthew Cleaveland, Insup Lee, George J. Pappas +1
Conformal prediction is a statistical tool for producing prediction regions of machine learning models that are valid with high probability. However, applying conformal prediction…
Risk Verification of Stochastic Systems with Neural Network Controllers
Matthew Cleaveland, Lars Lindemann, Radoslav Ivanov +1
Motivated by the fragility of neural network (NN) controllers in safety-critical applications, we present a data-driven framework for verifying the risk of stochastic dynamical sys…