[TYPES/announce] CFP: 3rd Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS 2020), Los Angeles, 20 July 2020

[Guy Katz]

3rd Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS
2020)
A satellite event of the CAV conference
Los Angeles, California, USA
July 20, 2020
https://fomlas2020.wixsite.com/fomlas2020


=====================================

SCOPE

In recent years, deep learning has emerged as a highly effective way
for creating real-world software, and is revolutionizing the way
complex systems are being designed all across the board. In
particular, this new approach is being applied to autonomous systems
(e.g., autonomous cars, aircraft), achieving exciting results that are
beyond the reach of manually created software. However, these
significant changes have created new challenges when it comes to
explainability, predictability and correctness: Can I explain why my
drone turned right at that angle? Can I predict what it will do next?
Can I know for sure that my autonomous car will never accelerate
towards a pedestrian? These are questions with far-reaching
consequences for safety, accountability and public adoption of
ML-enabled autonomous systems. One promising avenue for tackling these
difficulties is by developing formal methods capable of analyzing and
verifying these new kinds of systems.

The goal of this workshop is to facilitate discussion regarding how
formal methods can be used to increase predictability, explainability,
and accountability of ML-enabled autonomous systems. The workshop
welcomes results ranging from concept formulation (by connecting these
concepts with existing research topics in verification, logic and game
theory), through algorithms, methods and tools for analyzing
ML-enabled systems, to concrete case studies and examples.

The workshop will also include a special session and discussion on the
VNNLIB initiative, aimed at creating a standard format and a benchmark
library for neural network verification.

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

- Formal specifications for systems with ML components

- SAT-based and SMT-based methods for analyzing systems with deep
  neural network components

- Mixed-integer Linear Programming and optimization-based methods for
  the verification of systems with deep neural network components

- Testing approaches for ML components

- Statistical approaches to the verification of systems with ML
  components

- Approaches for enhancing the explainability of ML-based systems

- Techniques for analyzing hybrid systems with ML components

- Verification of quantized and low-precision neural networks

=====================================

IMPORTANT DATES (all dates are AOE)

  Abstract submission         April 12, 2020
  Full paper submission      April 19, 2020
  Author notification            June 4, 2020
  Workshop                         July 20, 2020


=====================================

COMMITTEE

Conference Chairs:

  Aws Albarghouthi        (University of Wisconsin–Madison, USA)
  Guy Katz                     (The Hebrew University of Jerusalem, Israel)
  Nina Narodytska         (VMWare Research, USA)


Program Committee:

  Clark Barrett                  (Stanford University, USA)
  Chih-Hong Cheng         (Denso Automotive Deutschland GmbH, Germany)
  Arie Gurfinkel                (University of Waterloo, Canada)
  Xiaowei Huang              (University of Liverpool, UK)
  Suman Jana                  (Columbia University, USA)
  Jean-Baptiste Jeannin   (University of Michigan, USA)
  Susmit Jha                     (SRI, USA)
  Alessio Lomuscio          (Imperial College London, UK)
  Luca Pulina                    (University of Sassari, Italy)
  Gagandeep Singh          (ETH, Switzerland)
  Armando Tacchella        (Università di Genova, Italy)
  Aleksandar Zeljic           (Stanford University, USA)
  Zhen Zhang                   (Utah State University, USA)


=====================================

SUBMISSIONS

Three categories of submissions are invited:

- Original papers: contain original research and sufficient detail to
  assess the merits and relevance of the submission. For papers
  reporting experimental results, authors are strongly encouraged to
  make their data available.

- Presentation-only papers: describe work recently published or
  submitted. We see this as a way to provide additional access to
  important developments that the workshop attendees may be unaware
  of.

- Extended abstracts: given the informal style of the workshop, we
  strongly encourage the submission of preliminary reports of work in
  progress. These reports may range in length from very short to full
  papers, and will be judged based on the expected level of interest
  for the community.

All accepted papers will be posted online as part of informal
proceedings on the day of the conference.

Papers in all categories will be peer-reviewed. Papers should be
submitted as a single column, standard-conforming PDF, using the LNCS
style. The suggested page limit is 10 pages, not counting
references. Technical details may be included in an appendix to be
read at the reviewers' discretion (also not counted towards the page
limit).

To submit a paper, use EasyChair:
https://easychair.org/my/conference?conf=fomlas2020
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://LISTS.SEAS.UPENN.EDU/pipermail/types-announce/attachments/20200304/e61fc894/attachment-0001.html>