2 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
A Hierarchical Imprecise Probability Approach to Reliability Assessment of Large Language Models
Robab Aghazadeh-Chakherlou, Qing Guo, Siddartha Khastgir +3
Large Language Models (LLMs) are increasingly deployed across diverse domains, raising the need for rigorous reliability assessment methods. Existing benchmark-based evaluations pr…
A Scalable Framework for Safety Assurance of Self-Driving Vehicles based on Assurance 2.0
Shufeng Chen, Mariat James Elizebeth, Robab Aghazadeh Chakherlou +4
Assurance 2.0 is a modern framework developed to address the assurance challenges of increasingly complex, adaptive, and autonomous systems. Building on the traditional Claims-Argu…