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Found 19 papers, 6 papers with code
Recovery from Adversarial Attacks in Cyber-physical Systems: Shallow, Deep and Exploratory Works
Cyber-physical systems (CPS) have experienced rapid growth in recent decades.
Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach
The integration of machine learning (ML) into cyber-physical systems (CPS) offers significant benefits, including enhanced efficiency, predictive capabilities, real-time responsiveness, and the enabling of autonomous operations.
Repairing Learning-Enabled Controllers While Preserving What Works
However, existing repair techniques do not preserve previously correct behaviors.
Distributionally Robust Statistical Verification with Imprecise Neural Networks
A particularly challenging problem in AI safety is providing guarantees on the behavior of high-dimensional autonomous systems.
Fulfilling Formal Specifications ASAP by Model-free Reinforcement Learning
We propose a model-free reinforcement learning solution, namely the ASAP-Phi framework, to encourage an agent to fulfill a formal specification ASAP.
Causal Repair of Learning-enabled Cyber-physical Systems
However, given the high diversity and complexity of LECs, it is challenging to encode domain knowledge (e. g., the CPS dynamics) in a scalable actual causality model that could generate useful repair suggestions.
Using Semantic Information for Defining and Detecting OOD Inputs
This can render the current OOD detectors impermeable to inputs lying outside the training distribution but with the same semantic information (e. g. training class labels).
Take Me Home: Reversing Distribution Shifts using Reinforcement Learning
To aid in our estimates of Wasserstein distance, we employ dimensionality reduction through orthonormal projection.
Credal Bayesian Deep Learning
We show that CBDL is better at quantifying and disentangling different types of uncertainties than single BNNs, ensemble of BNNs, and Bayesian Model Averaging.
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data
Machine learning models are prone to making incorrect predictions on inputs that are far from the training distribution.
Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations
Algorithm design of AT and its variants are focused on training models at a specified perturbation strength $\epsilon$ and only using the feedback from the performance of that $\epsilon$-robust model to improve the algorithm.
PAC-Wrap: Semi-Supervised PAC Anomaly Detection
Anomaly detection is essential for preventing hazardous outcomes for safety-critical applications like autonomous driving.
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
We propose the new method iDECODe, leveraging in-distribution equivariance for conformal OOD detection.
Confidence Composition for Monitors of Verification Assumptions
To predict safety violations in a verified system, we propose a three-step confidence composition (CoCo) framework for monitoring verification assumptions.
Detecting OODs as datapoints with High Uncertainty
We demonstrate the difference in the detection ability of these techniques and propose an ensemble approach for detection of OODs as datapoints with high uncertainty (epistemic or aleatoric).
Improving Neural Network Robustness via Persistency of Excitation
Improving adversarial robustness of neural networks remains a major challenge.
Are all outliers alike? On Understanding the Diversity of Outliers for Detecting OODs
Deep neural networks (DNNs) are known to produce incorrect predictions with very high confidence on out-of-distribution (OOD) inputs.
Counterfactual Causality from First Principles?
In this position paper we discuss three main shortcomings of existing approaches to counterfactual causality from the computer science perspective, and sketch lines of work to try and overcome these issues: (1) causality definitions should be driven by a set of precisely specified requirements rather than specific examples; (2) causality frameworks should support system dynamics; (3) causality analysis should have a well-understood behavior in presence of abstraction.
Proceedings First Workshop on Causal Reasoning for Embedded and safety-critical Systems Technologies
The keynote by Hana Chockler (King's College) provided a broad perspective on the application of causal reasoning based on Halpern and Pearl's definitions of actual causality to a variety of application domains ranging from formal verification to legal reasoning.
