Search Results for author:
Found 50 papers, 27 papers with code
HSVI-based Online Minimax Strategies for Partially Observable Stochastic Games with Neural Perception Mechanisms
For the partially-informed agent, we propose a continual resolving approach which uses lower bounds, pre-computed offline with heuristic search value iteration (HSVI), instead of opponent counterfactual values.
Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks
The lack of transparency of Deep Neural Networks continues to be a limitation that severely undermines their reliability and usage in high-stakes applications.
Learning Algorithms for Verification of Markov Decision Processes
The presented framework focuses on probabilistic reachability, which is a core problem in verification, and is instantiated in two distinct scenarios.
STR-Cert: Robustness Certification for Deep Text Recognition on Deep Learning Pipelines and Vision Transformers
Robustness certification, which aims to formally certify the predictions of neural networks against adversarial inputs, has become an integral part of important tool for safety-critical applications.
Partially Observable Stochastic Games with Neural Perception Mechanisms
Stochastic games are a well established model for multi-agent sequential decision making under uncertainty.
Probabilistic Reach-Avoid for Bayesian Neural Networks
Such computed lower bounds provide safety certification for the given policy and BNN model.
When to Trust AI: Advances and Challenges for Certification of Neural Networks
Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for deployment in a wide range of applications, such as autonomous systems, medical diagnosis and natural language processing.
Point-based Value Iteration for Neuro-Symbolic POMDPs
This requires functions over continuous-state beliefs, for which we propose a novel piecewise linear and convex representation (P-PWLC) in terms of polyhedra covering the continuous-state space and value vectors, and extend Bellman backups to this representation.
Adversarial Robustness Certification for Bayesian Neural Networks
We study the problem of certifying the robustness of Bayesian neural networks (BNNs) to adversarial input perturbations.
Provable Preimage Under-Approximation for Neural Networks (Full Version)
Neural network verification mainly focuses on local robustness properties, which can be checked by bounding the image (set of outputs) of a given input set.
Sample Efficient Model-free Reinforcement Learning from LTL Specifications with Optimality Guarantees
Linear Temporal Logic (LTL) is widely used to specify high-level objectives for system policies, and it is highly desirable for autonomous systems to learn the optimal policy with respect to such specifications.
Compositional Probabilistic and Causal Inference using Tractable Circuit Models
Probabilistic circuits (PCs) are a class of tractable probabilistic models, which admit efficient inference routines depending on their structural properties.
Bayesian Network Models of Causal Interventions in Healthcare Decision Making: Literature Review and Software Evaluation
After describing the search methodology, the selected research papers are briefly reviewed, with the view to identify publicly available models and datasets that are well suited to analysis using the causal interventional analysis software tool developed in Wang B, Lyle C, Kwiatkowska M (2021).
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.
When are Local Queries Useful for Robust Learning?
We finish by giving robust learning algorithms for halfspaces on $\{0, 1\}^n$ and then obtaining robustness guarantees for halfspaces in $\mathbb{R}^n$ against precision-bounded adversaries.
Robustness of Unsupervised Representation Learning without Labels
When used in adversarial training, they improve most unsupervised robustness measures, including certified robustness.
Learning Dynamics and Generalization in Reinforcement Learning
Solving a reinforcement learning (RL) problem poses two competing challenges: fitting a potentially discontinuous value function, and generalizing well to new observations.
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks
A fundamental problem in adversarial machine learning is to quantify how much training data is needed in the presence of evasion attacks.
Individual Fairness Guarantees for Neural Networks
We consider the problem of certifying the individual fairness (IF) of feed-forward neural networks (NNs).
Robustness Guarantees for Credal Bayesian Networks via Constraint Relaxation over Probabilistic Circuits
In many domains, worst-case guarantees on the performance (e. g., prediction accuracy) of a decision function subject to distributional shifts and uncertainty about the environment are crucial.
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.
Strategy Synthesis for Zero-Sum Neuro-Symbolic Concurrent Stochastic Games
Second, we introduce two novel representations for the value functions and strategies, constant-piecewise-linear (CON-PWL) and constant-piecewise-constant (CON-PWC) respectively, and propose Minimax-action-free PI by extending a recent PI method based on alternating player choices for finite state spaces to Borel state spaces, which does not require normal-form games to be solved.
The King is Naked: on the Notion of Robustness for Natural Language Processing
There is growing evidence that the classical notion of adversarial robustness originally introduced for images has been adopted as a de facto standard by a large part of the NLP research community.
Certifiers Make Neural Networks Vulnerable to Availability Attacks
While this is a key concept towards safe and secure AI, we show for the first time that this approach comes with its own security risks, as such fallback strategies can be deliberately triggered by an adversary.
A Language for Modeling And Optimizing Experimental Biological Protocols
We should ideally start from an integrated description of both the model and the steps carried out to test it, to concurrently analyze uncertainties in model parameters, equipment tolerances, and data collection.
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.
Provable Guarantees on the Robustness of Decision Rules to Causal Interventions
Robustness of decision rules to shifts in the data-generating process is crucial to the successful deployment of decision-making systems.
On Guaranteed Optimal Robust Explanations for NLP Models
We build on abduction-based explanations for ma-chine learning and develop a method for computing local explanations for neural network models in natural language processing (NLP).
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.
Assessing Robustness of Text Classification through Maximal Safe Radius Computation
Neural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction.
On the Benefits of Invariance in Neural Networks
Many real world data analysis problems exhibit invariant structure, and models that take advantage of this structure have shown impressive empirical performance, particularly in deep learning.
Probabilistic Safety for Bayesian Neural Networks
We study probabilistic safety for Bayesian Neural Networks (BNNs) under adversarial input perturbations.
Invariant Causal Prediction for Block MDPs
Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challenges.
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.
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.
On the Hardness of Robust Classification
However if the adversary is restricted to perturbing $O(\log n)$ bits, then the class of monotone conjunctions can be robustly learned with respect to a general class of distributions (that includes the uniform distribution).
Robustness Guarantees for Deep Neural Networks on Videos
The widespread adoption of deep learning models places demands on their robustness.
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.
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.
Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control
A rise in popularity of Deep Neural Networks (DNNs), attributed to more powerful GPUs and widely available datasets, has seen them being increasingly used within safety-critical domains.
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.
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.
Reachability Analysis of Deep Neural Networks with Provable Guarantees
Verifying correctness of deep neural networks (DNNs) is challenging.
Concolic Testing for Deep Neural Networks
Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program.
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm
In this paper we focus on the $L_0$ norm and aim to compute, for a trained DNN and an input, the maximal radius of a safe norm ball around the input within which there are no adversarial examples.
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.
Safety Verification of Deep Neural Networks
Our method works directly with the network code and, in contrast to existing methods, can guarantee that adversarial examples, if they exist, are found for the given region and family of manipulations.
