1st Workshop on Formal Verification of Machine Learning (WFVML 2022)

About This Workshop

Our workshop accepted 23 high quality papers. See list of accepted papers.

When machine learning-based building blocks become widely available for complex and critical systems such as autonomous vehicles, medical devices, or cyber-security systems, their behavior must also be exactly characterized to ensure the high assurance of the entire system. Most existing research treats a machine learning model such as a deep neural network as a black box and uses simple empirical metrics such as accuracy to quantify their performance. However, accuracy alone is not sufficient to assure that the model always obeys even basic specifications. Formal verification algorithms for machine learning aim to formally prove or disprove desired specifications of a machine learning model. Some common specifications include safety, fault tolerance, fairness, robustness and correctness. 

The aims of this workshop are:

• Bring together researchers interested in the emerging field of machine learning verification from a broad range of disciplines (such as computer-aided verification, programming languages, robotics and control, computer security, and optimization) with different perspectives to this problem; 

• Raise awareness of the importance of formal verification methods in the machine learning community and stimulate more research that can tackle open challenges on the verification of real-world applications such as robotics and control;

• Chart out important and promising future directions towards novel verification algorithms with better scalability and applicability.

Our workshop features 6 invited speakers spanning a quite diverse research background, including robotics, programming languages, optimization and computer security. Please checkout our workshop schedule.

Workshop Highlights

Best paper awards🏆

🥇Best Paper Award: IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound

🥈Best Paper Runner-up: Backward Reachability for Neural Feedback Loops
Nicholas Rober (MIT); Michael Everett (MIT); Jonathan How (MIT) 

Workshop Organizers

Important Dates

• Paper Submission Deadline: May 27, 2022 AoE Extended to Jun 3, 2022 AoE on CMT

• Author Notification: Sent

• Camera ready version upload (on CMT): June 30, 2022 AoE

• Video recording: July 1, 2022 AoE

• Workshop date: July 22 (Friday) (See Detailed Schedule and Accepted Papers)