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General Information

The 2019 AAAI Spring Symposium on Verification of Neural Networks (VNN19), to be held at Stanford University, March 25-27, 2019, aims to bring together researchers interested in methods and tools providing guarantees about the behaviours of neural networks and systems built from them.

Introduction

Methods based on machine learning are increasingly being deployed for a wide range of problems, including recommender systems, machine vision, autonomous driving, and beyond. While machine learning has made significant contributions to such applications, concerns remain about the lack of methods and tools to provide formal guarantees about the behaviours of the resulting systems.

In particular, for data-driven methods to be usable in safety-critical applications, including autonomous systems, robotics, cybersecurity, and cyber-physical systems, it is essential that the behaviours generated by neural networks are well-understood and can be predicted at design time. In the case of systems that are learning at run-time it is desirable that any change to the underlying system respects a given safety-envelope for the system.

While the literature on verification of traditionally designed systems is wide and successful, there has been a lack of results and efforts in this area until recently. The symposium intends to bring together researchers working on a range of techniques for the verification of neural networks, ranging from formal methods to optimisation and testing. The key objectives include: presentation of recent work in the area; discussion of key difficulties; collecting community benchmarks; and fostering collaboration.

One challenge for this research this area is that results are being published in several research communities, including formal verification, security and privacy, systems, and AI. One of the objectives of having a AAAI symposium is to help bridge these interdisciplinary divides to form a cross-cutting community interested in the verification and validation of systems based on machine learning.

The symposium workshop will include invited speakers, contributed papers, demonstrations, breakaway sessions, and panel sessions.

Topics of interest

The topics covered by the symposium include, but are not limited to, the following:

• Formal specifications for neural networks and systems based on them;
• SAT-based and SMT-based methods for the verification of machine learning systems;
• Mixed-integer Linear Programming methods for the verification of neural networks;
• Testing approaches to neural networks;
• Optimisation-based methods for the verification of neural networks;
• Statistical approaches to the verification of neural networks.

Registration

Register by March 1 at the AAAI Spring Symposium website.

Submissions

Submission is through easychair.

We will consider two types of submissions: previously published papers and novel contributions. Each submission must be clearly identified as belonging to one of these categories. Submissions of previously published papers pertaining to the topic above will be lightly reviewed by PC members and the PC chairs. Submissions of novel material will be reviewed in line with conference standards. Additionally, both regular papers (6-8 pages not including references) and short papers (2-4 pages) will be considered.

Key Dates

2 November 2018

9 November 2018: Submission deadline.

3 December 2018

14 December: Acceptance notification.

14 December 2018: Registration begins.

15 February 2019: Final version of papers due.

1 March 2019: Registration deadline.

25-27 March 2019: Symposium.

Proceedings

Compatibly with any copyright restriction, we will aim to collect the accepted papers as informal proceedings to be made available on the day of the symposium and on the symposium web page. We will consider a special issue in a journal should there be sufficient interest.

Program Chairs

Clark Barrett, Stanford, USA

Alessio Lomuscio, Imperial College London, UK

Program Committee

Xiaowei Huang, University of Liverpool, UK

Susmit Jha, SRI International, USA

Guy Katz, Hebrew University of Jerusalem, Israel

Nina Narodytska, VMware Research, USA

Luca Pulina, University of Sassari, Italy

Martin Vechev, ETH Zurich, Switzerland