5 papers · 1 filter
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…