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Found 10 papers, 4 papers with code
Robust Counterfactual Explanations in Machine Learning: A Survey
Counterfactual explanations (CEs) are advocated as being ideally suited to providing algorithmic recourse for subjects affected by the predictions of machine learning models.
Recourse under Model Multiplicity via Argumentative Ensembling (Technical Report)
Model Multiplicity (MM) arises when multiple, equally performing machine learning models can be trained to solve the same prediction task.
Promoting Counterfactual Robustness through Diversity
Counterfactual explanations shed light on the decisions of black-box models by explaining how an input can be altered to obtain a favourable decision from the model (e. g., when a loan application has been rejected).
Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
In this work, we propose Provably RObust and PLAusible Counterfactual Explanations (PROPLACE), a method leveraging on robust optimisation techniques to address the aforementioned limitations in the literature.
Formalising the Robustness of Counterfactual Explanations for Neural Networks
Existing attempts towards solving this problem are heuristic, and the robustness to model changes of the resulting CFXs is evaluated with only a small number of retrained models, failing to provide exhaustive guarantees.
Robot Swarms as Hybrid Systems: Modelling and Verification
A swarm robotic system consists of a team of robots performing cooperative tasks without any centralized coordination.
Verification of Neural Networks: Enhancing Scalability through Pruning
Verification of deep neural networks has witnessed a recent surge of interest, fueled by success stories in diverse domains and by abreast concerns about safety and security in envisaged applications.
SMarTplan: a Task Planner for Smart Factories
Smart factories are on the verge of becoming the new industrial paradigm, wherein optimization permeates all aspects of production, from concept generation to sales.
Automated Verification of Neural Networks: Advances, Challenges and Perspectives
Neural networks are one of the most investigated and widely used techniques in Machine Learning.
On the Synthesis of Guaranteed-Quality Plans for Robot Fleets in Logistics Scenarios via Optimization Modulo Theories
In manufacturing, the increasing involvement of autonomous robots in production processes poses new challenges on the production management.
