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As machine learning-based systems are increasingly deployed in safety-critical applications, providing formal guarantees on their trustworthiness becomes ever more important. To facilitate the investigation of this challenging problem, we propose the 2nd Workshop on Formal Verification of Machine Learning (WFVML). WFVML will raise awareness for the importance of the formal verification of machine learning systems, bring together researchers from diverse backgrounds with interest in the topic, and enable the discussion of open problems as well as promising avenues in this emerging research area. Building on the success of last year, WFVML features a diverse panel of 8 confirmed invited speakers who made foundational contributions to the young field and an experienced and diverse multi-institutional organizing team of 10, including pioneering proponents of machine learning verification. A schedule combining invited talks, contributed talks, poster sessions, and a panel will provide opportunities and input for open discussions, with remote participation enabled via Zoom. Please see our website ml-verification.com for more details.
Author Information
Mark Müller (ETH Zurich)
Brendon G. Anderson (University of California, Berkeley)
Leslie Rice (Carnegie Mellon University)
Zhouxing Shi (UCLA)
Shubham Ugare (UIUC)
Huan Zhang (CMU)
Martin Vechev (ETH Zurich)
Zico Kolter (Carnegie Mellon University / Bosch Center for AI)
Somayeh Sojoudi (University of California, Berkeley)
Cho-Jui Hsieh (UCLA)
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