64 research outputs found
Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction
Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment
ML + FV = ? A Survey on the Application of Machine Learning to Formal Verification
Formal Verification (FV) and Machine Learning (ML) can seem incompatible due
to their opposite mathematical foundations and their use in real-life problems:
FV mostly relies on discrete mathematics and aims at ensuring correctness; ML
often relies on probabilistic models and consists of learning patterns from
training data. In this paper, we postulate that they are complementary in
practice, and explore how ML helps FV in its classical approaches: static
analysis, model-checking, theorem-proving, and SAT solving. We draw a landscape
of the current practice and catalog some of the most prominent uses of ML
inside FV tools, thus offering a new perspective on FV techniques that can help
researchers and practitioners to better locate the possible synergies. We
discuss lessons learned from our work, point to possible improvements and offer
visions for the future of the domain in the light of the science of software
and systems modeling.Comment: 13 pages, no figures, 3 table
BIR: A Method for Selecting the Best Interpretable Multidimensional Scaling Rotation using External Variables
Interpreting nonlinear dimensionality reduction models using external features (or external variables) is crucial in many fields, such as psychology and ecology. Multidimensional scaling (MDS) is one of the most frequently used dimensionality reduction techniques in these fields. However, the rotation invariance of the MDS objective function may make interpretation of the resulting embedding difficult. This paper analyzes how the rotation of MDS embeddings affects sparse regression models used to interpret them and proposes a method, called the Best Interpretable Rotation (BIR) method, which selects the best MDS rotation for interpreting embeddings using external information
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