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Found 16 papers, 6 papers with code
Memory-Consistent Neural Networks for Imitation Learning
Imitation learning considerably simplifies policy synthesis compared to alternative approaches by exploiting access to expert demonstrations.
Curating Naturally Adversarial Datasets for Learning-Enabled Medical Cyber-Physical Systems
However, ensuring the robustness of these models is vital for building trustworthy AI systems.
Detection of Adversarial Physical Attacks in Time-Series Image Data
Nevertheless, DNN models have proven to be vulnerable to adversarial digital and physical attacks.
Guaranteed Conformance of Neurosymbolic Models to Natural Constraints
Next, using these memories we partition the state space into disjoint subsets and compute bounds that should be respected by the neural network in each subset.
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.
Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates
We formulate expert intervention as allowing the agent to execute option templates before learning an implementation.
Mako: Semi-supervised continual learning with minimal labeled data via data programming
This tool achieves similar performance, in terms of per-task accuracy and resistance to catastrophic forgetting, as compared to fully labeled data.
CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties (Technical Report)
First, to reduce the cost of human annotators, we use Infl, which prioritizes the most influential training samples for cleaning and provides cleaned labels to save the cost of one human annotator.
Improving Neural Network Robustness via Persistency of Excitation
Improving adversarial robustness of neural networks remains a major challenge.
ModelGuard: Runtime Validation of Lipschitz-continuous Models
This paper presents ModelGuard, a sampling-based approach to runtime model validation for Lipschitz-continuous models.
Confidence Calibration with Bounded Error Using Transformations
As machine learning techniques become widely adopted in new domains, especially in safety-critical systems such as autonomous vehicles, it is crucial to provide accurate output uncertainty estimation.
Improving Classifier Confidence using Lossy Label-Invariant Transformations
Providing reliable model uncertainty estimates is imperative to enabling robust decision making by autonomous agents and humans alike.
Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation
Our algorithm uses importance weighting to correct for the shift from the training to the real-world distribution.
Real-Time Detectors for Digital and Physical Adversarial Inputs to Perception Systems
To highlight this, we demonstrate the efficiency of the proposed detector on ImageNet, a task that is computationally challenging for the majority of relevant defenses, and on physically attacked traffic signs that may be encountered in real-time autonomy applications.
Verisig: verifying safety properties of hybrid systems with neural network controllers
This paper presents Verisig, a hybrid system approach to verifying safety properties of closed-loop systems using neural networks as controllers.
Systems and Control
Resilient Linear Classification: An Approach to Deal with Attacks on Training Data
Specifically, a generic metric is proposed that is tailored to measure resilience of classification algorithms with respect to worst-case tampering of the training data.
