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Mariam Kiran <[log in to unmask]>
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Mariam Kiran <[log in to unmask]>
Thu, 19 Apr 2018 09:41:56 -0700
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Dear All,

Apologies for cross posting.

This is a Joint Workshop between ICML (
018/CallForWorkshops), AAMAS (http://celweb.vuse.vanderbilt
.edu/aamas18/workshopsList/) and IJCAI (

Modern technological advances in engineering fields such as automotive,
aerospace, robotic, even data centers and networks, are exploiting machine
learning to improve and maintain mission critical activities. These systems
are large, complex and require real-time learning with feedback to ensure
they function as desired. Detecting anomalies, analyzing failures and
predicting future system state are imperative and are becoming part of
engineering integrative approaches. Research in algorithmic methods to make
real-time decisions based on fast arriving, high-volume condition data,
on-site feedback and data models is needed to train machine learning models
quickly and correctly.

This workshop aims to bring together diverse researchers from areas such as
reinforcement learning, autonomous agents, game theory, controls and
operations engineering teams to develop approaches which enable real-time
discovery, inference and computational tools. These techniques are aimed to
influence engineering operations teams in aerospace, self-driving
automotive, robotics, data centers and any engineering operations that
automate mission-critical and safety applications.

We encourage focus on aspects of deep learning to solve problems into
domains where continuous training and fast results are needed without
jeopardizing prediction accuracy. However, we also encourage exploration of
new innovative machine learning approaches, which can solve these problems
with improved latency. We are also seeking contributions in advances of
streaming and distributed algorithms, heterogeneous and high-dimensional
data sets and real-time decision- making algorithms for operations.

Some possible topics of interests but not confined are:

   - *Adaptation*: How can systems learn and adapt to changes in the
   environment (especially in dynamic environments) when training data is less
   and requires quick model assumption. How can principles of autonomous
   agents working together to build large engineering systems be exploited to
   react in dynamic situations.
   - *Noisy and poor data sets*: How can machine learning models be trained
   to understand noisy data sets for quick learning. Missing data exploration?
   - *Detecting anomalous behavior:* How can anomalies be detected quickly
   and partitioned appropriately such that correct actions are applied?
   - *Improving latency:* How can machine learning algorithms be improved
   to produce results quickly than previously anticipated?
   - *Improving software and hardware performance:* Exploring models of
   GPU, HPC processing and FPGAs to improve the performance of algorithms can
   greatly influence their use in engineering design. Experimental
   demonstrations are encouraged to display this.
   - *Reinforcement learning:* How can machine learn correct behavior? Can
   training be made quicker with guidance to allow algorithms to produce
   corrective measure when anomalies are detected?
   - *Human factors:* how can engineers maintaining the system interact
   with the self- autonomous system
   - *Open problems in engineering where machine learning is not proving
   fruitful*. What are the open problems in operations where practical
   machine learning is difficult to apply? What are the limitations and how
   can these be improved?

Workshop dates will be between 14th and 15th of July 2018, located as part
of Joint IJCAI/ECAI/AAMAS/ICML Call for Workshops. Specific dates will be
announced soon.

Important dates:


   - Submission deadline: 28th May, 2018, 23:59 (PDT)
   - Author notification: 15th June, 2018
   - Camera-ready (final) paper deadline: 1st July, 2018
   - Workshop: 14th or 15th July, 2018 (To be confirmed)

Submission Guidelines:


The abstract and paper submission deadline is the 28th of May 2018. Please
upload the final PDF as an updated version in your existing submission on

All submissions must obey the following formatting requirements.

Submit papers of *no more than six (6)* single–spaced pages for long papers
(and *four (4) for short papers*), including figures, tables, any
appendices, etc., followed by as many pages as necessary for references.

Submit papers formatted for printing on Letter-sized (8.5” by 11”) paper.
Paper text blocks must follow ACM guidelines: double-column, with each
column 9.25” by 3.33”, 0.33” space between columns. Each column must use
10-point font or larger, and contain no more than 55 lines of text.
It is your responsibility to ensure that your submission satisfies the
above requirements. If you are using LaTeX, you can make use of template
for ACM conference proceedings.

For your posters, we suggest A0 size measuring 841 × 1189 mm (33.1 × 46.8
in). Note that the workshop venue cannot accommodate posters larger than
910 × 1220 mm (36 × 48 in).

All papers must be original and not simultaneously submitted to another
journal or conference. The following paper categories are welcome:

   - Full papers describing mature solutions of deep learning in safety
   critical systems from various engineering domains such as security networks
   and self-autonomous cars or more.
   - Short paper on early demonstrations of deep learning in safety
   critical systems from various engineering domains.
   - Posters on early works (PhD students and early career researchers are
   particularly encouraged)

Selected papers will be invited for publication, in Journal special issues
such as Journal of Machine learning (pending).

Program Committee:


   - Mariam Kiran, Lawrence Berkeley National Lab, US
   - Alex Sim, Lawrence Berkeley National Lab, US
   - John Wu, Lawrence Berkeley National Lab, US
   - Samir Khan, University of Tokyo, Tokyo, Japan
   - Takehisa Yairi, University of Tokyo, Japan
   - Rajkumar Kettimuthu, Argonne National Laboratory, US

Organizing committee:


Mariam Kiran, Lawrence Berkeley National Lab

Samir Khan University of Tokyo, Tokyo, Japan

Contact: All questions about submissions should be emailed to <[log in to unmask]
, [log in to unmask]>.

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