13 papers
A Unified Perspective on Conformal Prediction and Wasserstein Distributionally Robust Optimization for Uncertainty Quantification
Kehan Long, Yiqi Zhao, Pol Mestres +3
Uncertainty quantification from finite data is central to machine learning, optimization, and automation systems, where decisions must remain reliable under limited samples and tes…
Conformal Prediction Regions for Continuous-Time Trajectories under Random Sampling
Joaquin Alvarez, Matteo Sesia, Jyotirmoy V. Deshmukh +1
Uncertainty quantification for continuous-time trajectories is a prerequisite in many safety-critical engineering domains. However, a major challenge in data-driven uncertainty qua…
Vision-Based Runtime Monitoring under Varying Specifications using Semantic Latent Representations
Bardh Hoxha, Oliver Schön, Hideki Okamoto +2
We study certified runtime monitoring of past-time signal temporal logic (ptSTL) from visual observations under partial observability. The monitor must infer safety-relevant quanti…
Multi-Variable Conformal Prediction: Optimizing Prediction Sets without Data Splitting
Laura Lützow, Simone Garatti, Marco C. Campi +2
Conformal prediction constructs prediction sets with finite-sample coverage guarantees, but its calibration stage is structurally constrained to a scalar score function and a singl…
When Environments Shift: Safe Planning with Generative Priors and Robust Conformal Prediction
Kaizer Rahaman, Jyotirmoy V. Deshmukh, Ashish R. Hota +1
Autonomous systems operate in environments that may change over time. An example is the control of a self-driving vehicle among pedestrians and human-controlled vehicles whose beha…
Conformal Predictive Monitoring for Multi-Modal Scenarios
Francesca Cairoli, Luca Bortolussi, Jyotirmoy V. Deshmukh +2
We consider the problem of quantitative predictive monitoring (QPM) of stochastic systems, i.e., predicting at runtime the degree of satisfaction of a desired temporal logic proper…
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…
PCA-DDReach: Efficient Statistical Reachability Analysis of Stochastic Dynamical Systems via Principal Component Analysis
Navid Hashemi, Lars Lindemann, Jyotirmoy Deshmukh
This study presents a scalable data-driven algorithm designed to efficiently address the challenging problem of reachability analysis. Analysis of cyber-physical systems (CPS) reli…
Distributionally Robust Predictive Runtime Verification under Spatio-Temporal Logic Specifications
Yiqi Zhao, Emily Zhu, Bardh Hoxha +3
Cyber-physical systems (CPS) designed in simulators, often consisting of multiple interacting agents (e.g. in multi-agent formations), behave differently in the real-world. We want…
Statistical Reachability Analysis of Stochastic Cyber-Physical Systems under Distribution Shift
Navid Hashemi, Lars Lindemann, Jyotirmoy V. Deshmukh
Reachability analysis is a popular method to give safety guarantees for stochastic cyber-physical systems (SCPSs) that takes in a symbolic description of the system dynamics and us…
Conformal Predictive Programming for Chance Constrained Optimization
Yiqi Zhao, Xinyi Yu, Matteo Sesia +2
We propose conformal predictive programming (CPP), a framework to solve chance constrained optimization problems, i.e., optimization problems with constraints that are functions of…
Robust Conformal Prediction for STL Runtime Verification under Distribution Shift
Yiqi Zhao, Bardh Hoxha, Georgios Fainekos +2
Cyber-physical systems (CPS) designed in simulators behave differently in the real-world. Once they are deployed in the real-world, we would hence like to predict system failures d…
Data-Driven Reachability Analysis of Stochastic Dynamical Systems with Conformal Inference
Navid Hashemi, Xin Qin, Lars Lindemann +1
We consider data-driven reachability analysis of discrete-time stochastic dynamical systems using conformal inference. We assume that we are not provided with a symbolic representa…