Search Results for author:
Found 9 papers, 6 papers with code
Neural Structure Learning with Stochastic Differential Equations
Unfortunately, most existing structure learning approaches assume that the underlying process evolves in discrete-time and/or observations occur at regular time intervals.
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.
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).
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.
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.
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.
Statistically Robust Neural Network Classification
The SRR provides a distinct and complementary measure of robust performance, compared to natural and adversarial risk.
