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Found 21 papers, 17 papers with code
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.
Individual Fairness in Bayesian Neural Networks
We study Individual Fairness (IF) for Bayesian neural networks (BNNs).
Use Perturbations when Learning from Explanations
Machine learning from explanations (MLX) is an approach to learning that uses human-provided explanations of relevant or irrelevant features for each input to ensure that model predictions are right for the right reasons.
Robust Explanation Constraints for Neural Networks
Post-hoc explanation methods are used with the intent of providing insights about neural networks and are sometimes said to help engender trust in their outputs.
Emergent Linguistic Structures in Neural Networks are Fragile
In this paper, focusing on the ability of language models to represent syntax, we propose a framework to assess the consistency and robustness of linguistic representations.
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).
Tractable Uncertainty for Structure Learning
Bayesian structure learning allows one to capture uncertainty over the causal directed acyclic graph (DAG) responsible for generating given data.
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.
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.
Gradient-Free Adversarial Attacks for Bayesian Neural Networks
In particular, we consider genetic algorithms, surrogate models, as well as zeroth order optimization methods and adapt them to the goal of finding adversarial examples for BNNs.
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.
Global Adversarial Robustness Guarantees for Neural Networks
We investigate global adversarial robustness guarantees for machine learning models.
Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control
Deep neural network controllers for autonomous driving have recently benefited from significant performance improvements, and have begun deployment in the real world.
Robustness of 3D Deep Learning in an Adversarial Setting
Understanding the spatial arrangement and nature of real-world objects is of paramount importance to many complex engineering tasks, including autonomous navigation.
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.
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees
In this paper, we study two variants of pointwise robustness, the maximum safe radius problem, which for a given input sample computes the minimum distance to an adversarial example, and the feature robustness problem, which aims to quantify the robustness of individual features to adversarial perturbations.
Efficient Learning of Optimal Markov Network Topology with k-Tree Modeling
The seminal work of Chow and Liu (1968) shows that approximation of a finite probabilistic system by Markov trees can achieve the minimum information loss with the topology of a maximum spanning tree.
Feature-Guided Black-Box Safety Testing of Deep Neural Networks
In this paper, we focus on image classifiers and propose a feature-guided black-box approach to test the safety of deep neural networks that requires no such knowledge.
