research
∙
08/28/2023
Distributionally Robust Statistical Verification with Imprecise Neural Networks
A particularly challenging problem in AI safety is providing guarantees ...
share
research
∙
04/25/2023
Fulfilling Formal Specifications ASAP by Model-free Reinforcement Learning
We propose a model-free reinforcement learning solution, namely the ASAP...
research
∙
04/06/2023
Causal Repair of Learning-enabled Cyber-physical Systems
Models of actual causality leverage domain knowledge to generate convinc...
research
∙
02/21/2023
Using Semantic Information for Defining and Detecting OOD Inputs
As machine learning models continue to achieve impressive performance ac...
research
∙
02/20/2023
Take Me Home: Reversing Distribution Shifts using Reinforcement Learning
Deep neural networks have repeatedly been shown to be non-robust to the ...
research
∙
02/19/2023
Imprecise Bayesian Neural Networks
Uncertainty quantification and robustness to distribution shifts are imp...
research
∙
01/26/2023
Conservative Safety Monitors of Stochastic Dynamical Systems
Generating accurate runtime safety estimates for autonomous systems is v...
research
∙
10/14/2022
Let's Talk Through Physics! Covert Cyber-Physical Data Exfiltration on Air-Gapped Edge Devices
Although organizations are continuously making concerted efforts to hard...
research
∙
07/24/2022
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data
Machine learning models are prone to making incorrect predictions on inp...
research
∙
06/13/2022
Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations
Adversarial training (AT) and its variants have spearheaded progress in ...
research
∙
05/22/2022
PAC-Wrap: Semi-Supervised PAC Anomaly Detection
Anomaly detection is essential for preventing hazardous outcomes for saf...
research
∙
05/17/2022
A Framework for Checkpointing and Recovery of Hierarchical Cyber-Physical Systems
This paper tackles the problem of making complex resource-constrained cy...
research
∙
01/07/2022
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
Machine learning methods such as deep neural networks (DNNs), despite th...
research
∙
11/04/2021
Monotonic Safety for Scalable and Data-Efficient Probabilistic Safety Analysis
Autonomous systems with machine learning-based perception can exhibit un...
research
∙
11/03/2021
Confidence Composition for Monitors of Verification Assumptions
Closed-loop verification of cyber-physical systems with neural network c...
research
∙
08/13/2021
Detecting OODs as datapoints with High Uncertainty
Deep neural networks (DNNs) are known to produce incorrect predictions w...
research
∙
06/03/2021
Robust Learning via Persistency of Excitation
Improving adversarial robustness of neural networks remains a major chal...
research
∙
03/23/2021
Are all outliers alike? On Understanding the Diversity of Outliers for Detecting OODs
Deep neural networks (DNNs) are known to produce incorrect predictions w...
research
∙
06/20/2018
Self-Driving Vehicle Verification Towards a Benchmark
Industrial cyber-physical systems are hybrid systems with strict safety ...
research
∙
10/10/2017