[Opt-Net] Call for Papers: Special Issue on "The Interplay of Discrete Optimization and Machine Learning"

[Andreas Tillmann]

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    Call for Papers

    "The Interplay of Discrete Optimization and Machine Learning"

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

https://www.frontiersin.org/research-topics/47207/the-interplay-of-discrete-optimization-and-machine-learning

    Submission Deadline: January 6, 2023
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*** 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

  https://www.frontiersin.org/research-topics/47207/the-interplay-of-discrete-optimization-and-machine-learning

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
https://www.tu-braunschweig.de/en/mo/team/tillmann