3 citations · 6 across the 4 of their papers we have counts for
6 papers
Abstract Interpretation-Based Feature Importance for SVMs
Abhinandan Pal, Francesco Ranzato, Caterina Urban +1
We propose a symbolic representation for support vector machines (SVMs) by means of abstract interpretation, a well-known and successful technique for designing and implementing st…
A Review of Formal Methods applied to Machine Learning
Caterina Urban, Antoine Miné
We review state-of-the-art formal methods applied to the emerging field of the verification of machine learning systems. Formal methods can provide rigorous correctness guarantees…
Fair Training of Decision Tree Classifiers
Francesco Ranzato, Caterina Urban, Marco Zanella
We study the problem of formally verifying individual fairness of decision tree ensembles, as well as training tree models which maximize both accuracy and individual fairness. In…
What Programs Want: Automatic Inference of Input Data Specifications
Caterina Urban
Nowadays, as machine-learned software quickly permeates our society, we are becoming increasingly vulnerable to programming errors in the data pre-processing or training software,…
Perfectly Parallel Fairness Certification of Neural Networks
Caterina Urban, Maria Christakis, Valentin Wüstholz +1
Recently, there is growing concern that machine-learning models, which currently assist or even automate decision making, reproduce, and in the worst case reinforce, bias of the tr…
Permission Inference for Array Programs
Jérôme Dohrau, Alexander J. Summers, Caterina Urban +2
Information about the memory locations accessed by a program is, for instance, required for program parallelisation and program verification. Existing inference techniques for this…