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.pc-members { | ||
margin-top: 20px; | ||
} | ||
.organizers-section { | ||
background-color: #f9f9f9; | ||
padding: 20px; | ||
margin-top: 20px; | ||
border-radius: 8px; | ||
} | ||
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</style> | ||
</head> | ||
<body> | ||
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<header> | ||
<h1>Workshop on Automated Machine Learning (AutoML)</h1> | ||
<h1>Workshop on Automated Machine Learning (AutoML) - ECAI 2024</h1> | ||
</header> | ||
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<section> | ||
<main> | ||
<!-- Your existing content remains unchanged --> | ||
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<!-- Workshop description --> | ||
<div class="workshop-description"> | ||
<h2>Workshop Description:</h2> | ||
<p> | ||
As the demand for machine learning applications surges, it becomes evident that the available pool of knowledgeable data scientists cannot | ||
scale proportionally with the increasing data volumes and diverse application requirements in our digital world. To address this challenge, | ||
various automated machine learning (AutoML) frameworks have emerged, aiming to bridge the gap in human expertise by automating the construction | ||
of machine learning pipelines. AutoML research aims to automate the machine learning process progressively, with the objective of making effective | ||
methods accessible to everyone. Therefore, the workshop is designed for a diverse audience, including core machine learning researchers involved | ||
in various ML domains related to AutoML, such as neural architecture search, hyperparameter optimization, meta-learning, and explainability | ||
within the AutoML context. It also caters to domain experts seeking to apply machine learning to novel problem domains. | ||
scale proportionally with the increasing data volumes and diverse application requirements in our digital world. To address this challenge, | ||
various automated machine learning (AutoML) frameworks have emerged, aiming to bridge the gap in human expertise by automating the construction | ||
of machine learning pipelines. AutoML research aims to automate the machine learning process progressively, with the objective of making effective | ||
methods accessible to everyone. Therefore, the workshop is designed for a diverse audience, including core machine learning researchers involved | ||
in various ML domains related to AutoML, such as neural architecture search, hyperparameter optimization, meta-learning, and explainability | ||
within the AutoML context. It also caters to domain experts seeking to apply machine learning to novel problem domains. | ||
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</p> | ||
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<p> </p> | ||
</p> | ||
We invite submissions on the topics of: | ||
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<b>We invite submissions on the topics of:</b> | ||
<ul> | ||
<li>Model selection, hyper-parameter optimization, and model search</li> | ||
<li>Neural architecture search</li> | ||
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<li>Hyperparameter agnostic algorithms</li> | ||
<li>AutoML for neuro-fuzzy systems</li> | ||
</ul> | ||
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<p> </p> | ||
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<b>Submissions:</b> | ||
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<p> | ||
As workshop organizers, you will need to organize your own paper submission process, and ECAI cannot directly support you in that or cover any costs. | ||
However, there are a number of free tools available. Specifically, you are welcome to try a new tool (https://chairingtool.com) currently under development | ||
for IJCAI, which as the organizer of an ECAI workshop you can use free of charge and with premium support. | ||
</p> | ||
<!-- Format section --> | ||
<h2>Format:</h2> | ||
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<b>Format:</b> | ||
<p> | ||
The workshop will follow the classical format of presentations of peer-reviewed papers followed by | ||
discussion. The typical duration for the workshop is a full day. We will arrange invited talks | ||
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</p> | ||
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<!-- Attendance section --> | ||
<h2>Attendance:</h2> | ||
<b>Attendance:</b> | ||
<p> | ||
The workshop is timely and relevant for the data management and machine learning research | ||
communities due to the rapid growth in machine learning applications in almost every application | ||
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</div> | ||
<!-- List of potential workshop PC members --> | ||
<div class="pc-members"> | ||
<h2>List of Potential Workshop PC Members:</h2> | ||
<h3>List of Potential Workshop Participating Members:</h3> | ||
<ul> | ||
<li>Amin Beheshti, Professor, School of Computing, Macquarie University, Sydney, Australia</li> | ||
<li>Riccardo Tommasini, Associate Professor at the Institute National des Sciences Appliquées (INSA)</li> | ||
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<!-- Add more PC members as needed --> | ||
</ul> | ||
</div> | ||
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<div class="organizers-section"> | ||
<h2>Names, affiliations, and contact details of all workshop organisers:</h2> | ||
<ul> | ||
<li> | ||
Prof. Jerry Chun-Wei Lin<br> | ||
Faculty of Automatic Control, Electronics and Computer Science, Department of Distributed Systems and IT Devices, Silesian University of Technology, Poland<br> | ||
<a href="mailto:[email protected]">[email protected]</a> | ||
</li> | ||
<li> | ||
Assoc Prof. Radwa Elshawi<br> | ||
Institute of Computer Science, Tartu University<br> | ||
<a href="mailto:[email protected]">[email protected]</a> | ||
</li> | ||
<li> | ||
Assoc Prof Stefania Tomasiello<br> | ||
Department of Industrial Engineering, Università degli Studi di Salerno<br> | ||
<a href="mailto:[email protected]">[email protected]</a> | ||
</li> | ||
</ul> | ||
</div> | ||
</main> | ||
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