Volume •Abstracts •Preface •Authors •Keywords •BibTex Entries

FoMLAS2023: Volume Information

Proceedings of the 6th Workshop on Formal Methods for ML-Enabled Autonomous Systems

Editors: Nina Narodytska, Guy Amir, Guy Katz and Omri Isac

Kalpa Publications in Computing•Volume 16

7 articles•82 pages•Published: October 23, 2023

Papers

The Vehicle Tutorial: Neural Network Verification with Vehicle

Matthew Daggitt, Wen Kokke, Ekaterina Komendantskaya, Robert Atkey, Luca Arnaboldi, Natalia Slusarz, Marco Casadio, Ben Coke and Jeonghyeon Lee

1-5

Scaling Model Checking for Neural Network Analysis via State-Space Reduction and Input Segmentation

Mahum Naseer, Osman Hasan and Muhammad Shafique

6-28

Prediction and Control of Stochastic Agents Using Formal Methods

Avraham Raviv, Yuval Gerber, Liri Benzinou, Michelle Aluf-Medina and Hillel Kugler

29-34

Certified Private Inference on Neural Networks via Lipschitz-Guided Abstraction Refinement

Edoardo Manino, Bernardo Magri, Mustafa Mustafa and Lucas Cordeiro

35-46

Supporting Standardization of Neural Networks Verification with VNNLIB and CoCoNet

Stefano Demarchi, Dario Guidotti, Luca Pulina and Armando Tacchella

47-58

ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification

Marco Casadio, Luca Arnaboldi, Matthew Daggitt, Omri Isac, Tanvi Dinkar, Daniel Kienitz, Verena Rieser and Ekaterina Komendantskaya

59-70

Verifying Global Neural Network Specifications using Hyperproperties

David Boetius and Stefan Leue

71-82

Keyphrases

abstract interpretation², adversarial training², Artificial Intelligence, bias, deep learning, Deep Neural Networks, domain-specific languages, formal analysis, formal verification, homomorphic encryption, Hyperproperties, Input Node Sensitivity, Lipschitz constant, machine learning, Model Checking., Neural Network Verification³, neural networks verification, NLP, noise tolerance, polynomial approximation, privacy-preserving machine learning, programming languages, Reinforcement Learning, robustness, Safe Machine Learning, Software Engineering, state space reduction, Trustworthy Machine Learning, types.