Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

Safety is an essential component for deploying reinforcement learning (RL) algorithms in
real-world scenarios, and is critical during the learning process itself. A natural first approach …

Robustness verification that aims to formally certify the prediction behavior of neural
networks has become an important tool for understanding model behavior and obtaining …

We study the robustness verification problem of tree based models, including random forest
(RF) and gradient boosted decision tree (GBDT). Formal robustness verification of decision …

The decision logic for the ACAS X family of aircraft collision avoidance systems is
represented as a large numeric table. Due to storage constraints of certified avionics …

Adversarial Robustness for Machine Learning summarizes the recent progress on this topic
and introduces popular algorithms on adversarial attack, defense and veri? cation. Sections …

We investigate the problem of verifying the strategic properties of multi-agent systems
equipped with machine learningbased perception units. We introduce a novel model of …

Sequential decision-making problems can be modeled as Markov decision processes and
solved with value iteration to produce a table of values. However, the numeric table can be …

Neural network approximations have become attractive to compress data for automation and
autonomy algorithms for use on storage-limited and processing-limited aerospace …

Game-theoretic concepts have been extensively studied in economics to provide insight into
competitive behaviour and strategic decision making. As computing systems increasingly …