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Articles by Antonio Filieri
IAM policy autopilot: Static analysis for policy generation from application code
Every product a customer returns is a moment where Amazon either recovers value or writes it off — and India's ReCommerce business is on a multi-million-dollar mission to recover more of it, more intelligently, at scale. Machine learning is the core lever: predicting whether a returned unit is sellable without a human touching it, detecting damage and fraud inside sealed packaging from images, routing each unit to its highest-value disposition, and pricing recovered inventory dynamically.
Compositional taint analysis for enforcing security policies at scale
Automated static dataflow analysis is an effective technique for detecting security critical issues like sensitive data leak, and vulnerability to injection attacks. Ensuring high precision and recall requires an analysis that is context, field and object sensitive. However, it is challenging to attain high precision and recall and scale to large industrial code bases.
[2307.04927] Probabilistic Counterexample Guidance for Safer Reinforcement Learning
arXiv:2307.04927 (cs) Download a PDF of the paper titled Probabilistic Counterexample Guidance for Safer Reinforcement Learning, by Xiaotong Ji and Antonio Filieri Download PDF Submission history From: Xiaotong Ji [ view email] [v1] Mon, 10 Jul 2023 22:28:33 UTC (2,589 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Links to Code Toggle...
Input splitting for cloud-based static application security testing platforms
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day.
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