Search Results for author:
Found 17 papers, 14 papers with code
Uncertainty Propagation in Stochastic Systems via Mixture Models with Error Quantification
Uncertainty propagation in non-linear dynamical systems has become a key problem in various fields including control theory and machine learning.
Probabilistic Reach-Avoid for Bayesian Neural Networks
Such computed lower bounds provide safety certification for the given policy and BNN model.
Adversarial Robustness Certification for Bayesian Neural Networks
We study the problem of certifying the robustness of Bayesian neural networks (BNNs) to adversarial input perturbations.
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming
In this paper, we introduce BNN-DP, an efficient algorithmic framework for analysis of adversarial robustness of Bayesian Neural Networks (BNNs).
Individual Fairness in Bayesian Neural Networks
We study Individual Fairness (IF) for Bayesian neural networks (BNNs).
On the Robustness of Bayesian Neural Networks to Adversarial Attacks
Despite significant efforts, both practical and theoretical, training deep learning models robust to adversarial attacks is still an open problem.
Individual Fairness Guarantees for Neural Networks
We consider the problem of certifying the individual fairness (IF) of feed-forward neural networks (NNs).
Certification of Iterative Predictions in Bayesian Neural Networks
We consider the problem of computing reach-avoid probabilities for iterative predictions made with Bayesian neural network (BNN) models.
Adversarial Robustness Guarantees for Gaussian Processes
Gaussian processes (GPs) enable principled computation of model uncertainty, making them attractive for safety-critical applications.
Bayesian Inference with Certifiable Adversarial Robustness
We consider adversarial training of deep neural networks through the lens of Bayesian learning, and present a principled framework for adversarial training of Bayesian Neural Networks (BNNs) with certifiable guarantees.
Probabilistic Safety for Bayesian Neural Networks
We study probabilistic safety for Bayesian Neural Networks (BNNs) under adversarial input perturbations.
Robustness of Bayesian Neural Networks to Gradient-Based Attacks
Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications.
Safety Guarantees for Planning Based on Iterative Gaussian Processes
Gaussian Processes (GPs) are widely employed in control and learning because of their principled treatment of uncertainty.
Global Adversarial Robustness Guarantees for Neural Networks
We investigate global adversarial robustness guarantees for machine learning models.
Adversarial Robustness Guarantees for Classification with Gaussian Processes
We apply our method to investigate the robustness of GPC models on a 2D synthetic dataset, the SPAM dataset and a subset of the MNIST dataset, providing comparisons of different GPC training techniques, and show how our method can be used for interpretability analysis.
Statistical Guarantees for the Robustness of Bayesian Neural Networks
We introduce a probabilistic robustness measure for Bayesian Neural Networks (BNNs), defined as the probability that, given a test point, there exists a point within a bounded set such that the BNN prediction differs between the two.
Robustness Guarantees for Bayesian Inference with Gaussian Processes
Bayesian inference and Gaussian processes are widely used in applications ranging from robotics and control to biological systems.
