10 papers
Reusing Operational Evidence After Context Changes: A Conservative Bayesian Framework for Autonomous Vehicle Safety
Robab Aghazadeh Chakherlou, Siddartha Khastgir, Xingyu Zhao
Operational evidence, i.e., evidence of operation without failure is an important component of confidence in the safety or reliability of a system in service, but it is costly to c…
Modeling Memory-Dependent Reliability of LLMs: A Hidden Markov Model
Robab Aghazadeh Chakherlou, Siddartha Khastgir, Peter Popov +1
Reliability assessment of large language models (LLMs) seeks to estimate the probability that a model produces correct responses under a specified operational profile. Conventional…
Probabilistic Robustness in Medical Image Classification
Yi Zhang, Siddartha Khastgir, Xingyu Zhao
Deep learning (DL) has shown strong performance in medical image classification, but its trustworthy deployment remains challenging in safety-critical clinical settings, where pred…
Non-Parametric Probabilistic Robustness: A Conservative Risk Estimator under Unknown Perturbation Distributions
Zheng Wang, Yi Zhang, Siddartha Khastgir +2
Deep learning (DL) models, despite their remarkable success, remain vulnerable to small input perturbations that can cause erroneous outputs, motivating the recent proposal of prob…
PRBench: A Standardized Probabilistic Robustness Benchmark
Yi Zhang, Zheng Wang, Zhen Chen +5
Deep learning models are notoriously vulnerable to imperceptible perturbations. Most existing research centers on adversarial robustness (AR), which evaluates models under worst-ca…
Uncertainty-Aware Measurement of Scenario Suite Representativeness for Autonomous Systems
Robab Aghazadeh Chakherlou, Siddartha Khastgir, Xingyu Zhao +2
Assuring the trustworthiness and safety of AI systems, e.g., autonomous vehicles (AV), depends critically on the data-related safety properties, e.g., representativeness, completen…