6 citations · 14 across the 8 of their papers we have counts for
5 papers · 1 filter
Abstract Interpretation-Based Data Leakage Static Analysis
Filip Drobnjaković, Pavle Subotić, Caterina Urban
Data leakage is a well-known problem in machine learning. Data leakage occurs when information from outside the training dataset is used to create a model. This phenomenon renders…
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…
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…