ICML 2022
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Workshop on Formal Verification of Machine Learning

Huan Zhang · Leslie Rice · Kaidi Xu · aditi raghunathan · Wan-Yi Lin · Cho-Jui Hsieh · Clark Barrett · Martin Vechev · Zico Kolter

Room 308

Formal verification of machine learning-based building blocks is important for complex and critical systems such as autonomous vehicles, medical devices, or cybersecurity systems where guarantees on safety, fault tolerance and correctness are essential. Formal verification of machine learning is an emerging and interdisciplinary field, intersecting with fields of computer-aided verification, programming languages, robotics, computer security, and optimization, with many challenging open problems. This workshop aims to raise awareness of the importance of formal verification methods in the machine learning community and to bring together researchers and practitioners interested in this emerging field from a broad range of disciplines and backgrounds. Organizers of this workshop include pioneering proponents of machine learning verification and six confirmed invited speakers who have solid works in this field with diverse research and demographic backgrounds. The workshop includes posters, contributed talks, and a panel to encourage novel contributed work and interdisciplinary discussions on open challenges.

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Timezone: America/Los_Angeles