Thursday, September 15, 2022

[DMANET] CFP - Special Issue: The Interplay of Discrete Optimization and Machine Learning

   Call for Papers

   "The Interplay of Discrete Optimization and Machine Learning"

   Research Topic (Special Issue)
   Frontiers in Applied Mathematics and Statistics / Optimization

   Submission Deadline: January 6, 2023

*** Description ***
Many problems arising in the fields of machine learning and data science
are of an inherently discrete or combinatorial nature. However, the
solution of such problems is often approached with suboptimal heuristic
methods or easier but inexact relaxations, despite the availability of
powerful modern algorithmic techniques, e.g., in mixed-integer linear
and nonlinear programming. One reason for this may be the typical need
for scalability in machine learning applications, but several recent
results demonstrated that sophisticated discrete models and
problem-specific solvers can, in fact, enable the exact solution also in
large-scale regimes. Conversely, discrete optimization can also benefit
from machine learning techniques, e.g., by means of learning-enhanced
heuristics or via replacing expert-designed algorithmic decisions such
as branching within a branch-and-cut mixed-integer solver framework.
Expanding, improving and further investigating such aspects at the
intersection of discrete optimization and machine learning motivates
this Research Topic.

*** Scope ***
Contributions should address
- the solution of a discrete/combinatorial (optimization) problem from
machine learning/data science, exactly or with certifiable solution
quality bounds, or
- the improvement of general-purpose or problem-specific solution
methods for discrete/combinatorial (optimization) problems by utilizing
machine learning techniques.

Topics of interest and applications include, but are not limited to:
- learning-based components of mixed-integer linear and nonlinear
programming frameworks, e.g., branching, node or cut selection, and
other algorithmic parameter/selection rules
- learning-based solution methods for discrete problems with provable
approximation bounds
- novel models and (exact) solution approaches for discrete problems
arising in machine learning or data science, e.g., subset selection,
sparse regression, classification, clustering, neural architecture
search, etc.

Both submissions with a theoretical focus and mainly empirical studies
are welcome.

*** Submission Details ***
The deadline for manuscript submissions is *January 6, 2023*.

Authors are encouraged to submit an abstract of their intended
contribution by *November 11, 2022*.

Abstracts are not mandatory and will not appear alongside accepted final
papers. It can be written as an informal description of the work and is
meant to help authors clarify possible questions of suitability of
intended contributions regarding the special issue's scope before a full
manuscript needs to be submitted, and also enable the editorial team to
easier keep track of upcoming full submissions.

Note that Frontiers in Applied Mathematics and Statistics is an
open-access journal, and article processing fees (APFs) will become due
for published papers. APFs may depend on article type, and substantial
discounts can often be granted to authors; further information can be
found on the journal webpages.

All manuscript submissions will be rigorously peer-reviewed.

*** Further Information ***
The Guest Editors of this special issue are
    - Gonzalo Munoz       (Universidad de O'Higgins, Chile)
    - Elias Khalil        (University of Toronto, Canada)
    - Sebastian Pokutta   (TU Berlin & ZIB, Germany)
    - Andreas M. Tillmann (TU Braunschweig, Germany)

For further information, online submission, and the option to
"participate" in and receive updates on the Research Topic, please visit
the RT website

Feel free to share this CfP and the webpage link with interested colleagues.

We look forward to your contributions that help advance the theoretical
foundations and methodological state-of-the-art in discrete optimization
with and for machine learning.

Dr. Andreas M. Tillmann
Institute for Mathematical Optimization
Cluster of Excellence SE²A - Sustainable and Energy-Efficient Aviation
TU Braunschweig, Germany

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