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Found 7 papers, 5 papers with code
Differentially-Private Decision Trees and Provable Robustness to Data Poisoning
By leveraging the better privacy-utility trade-off of PrivaTree we are able to train decision trees with significantly better robustness against backdoor attacks compared to regular decision trees and with meaningful theoretical guarantees.
Optimal Decision Tree Policies for Markov Decision Processes
While there is generally a trade-off between the performance and interpretability of machine learning models, we find that OMDTs limited to a depth of 3 often perform close to the optimal limit.
SoK: Explainable Machine Learning for Computer Security Applications
The security literature sometimes also fails to disentangle the role of the various stakeholders, e. g., by providing explanations to model users and designers while also exposing them to adversaries.
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
Overall, by organizing this competition we have introduced routing problems as an interesting problem setting for AI researchers.
Robust Optimal Classification Trees Against Adversarial Examples
Decision trees are a popular choice of explainable model, but just like neural networks, they suffer from adversarial examples.
Efficient Training of Robust Decision Trees Against Adversarial Examples
We can use algorithms that take adversarial attacks into account to fit trees that are more robust.
DEFenD: A Secure and Privacy-Preserving Decentralized System for Freight Declaration
In our system, economic operators make claims to the network about goods they insert into or remove from containers, and encrypt these claims so that they can only be read by the destination country's customs agency.
Cryptography and Security
