9 citations · 14 across the 9 of their papers we have counts for
9 papers
PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks
Xiyue Zhang, Benjie Wang, Marta Kwiatkowska +1
Most methods for neural network verification focus on bounding the image, i.e., set of outputs for a given input set. This can be used to, for example, check the robustness of neur…
Provable Preimage Under-Approximation for Neural Networks (Full Version)
Xiyue Zhang, Benjie Wang, Marta Kwiatkowska
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. However, often it is im…
Compositional Probabilistic and Causal Inference using Tractable Circuit Models
Benjie Wang, Marta Kwiatkowska
Probabilistic circuits (PCs) are a class of tractable probabilistic models, which admit efficient inference routines depending on their structural properties. In this paper, we int…
Bayesian Network Models of Causal Interventions in Healthcare Decision Making: Literature Review and Software Evaluation
Artem Velikzhanin, Benjie Wang, Marta Kwiatkowska
This report summarises the outcomes of a systematic literature search to identify Bayesian network models used to support decision making in healthcare. After describing the search…
Robustness Guarantees for Credal Bayesian Networks via Constraint Relaxation over Probabilistic Circuits
Hjalmar Wijk, Benjie Wang, Marta Kwiatkowska
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…
Tractable Uncertainty for Structure Learning
Benjie Wang, Matthew Wicker, Marta Kwiatkowska
Bayesian structure learning allows one to capture uncertainty over the causal directed acyclic graph (DAG) responsible for generating given data. In this work, we present Tractable…
Provable Guarantees on the Robustness of Decision Rules to Causal Interventions
Benjie Wang, Clare Lyle, Marta Kwiatkowska
Robustness of decision rules to shifts in the data-generating process is crucial to the successful deployment of decision-making systems. Such shifts can be viewed as interventions…
Assessing Robustness of Text Classification through Maximal Safe Radius Computation
Emanuele La Malfa, Min Wu, Luca Laurenti +3
Neural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction. In this paper, we focus on rob…
Statistically Robust Neural Network Classification
Benjie Wang, Stefan Webb, Tom Rainforth
Despite their numerous successes, there are many scenarios where adversarial risk metrics do not provide an appropriate measure of robustness. For example, test-time perturbations…