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Simple design
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jtimko16 authored Feb 28, 2024
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Hello, World!</title>
<title>Workshop on Automated Machine Learning (AutoML) - 2024</title>
<style>
body {
font-family: 'Arial', sans-serif;
background-color: #f4f4f4;
margin: 0;
padding: 0;
color: #333;
}

header {
background-color: #3498db;
color: #fff;
text-align: center;
padding: 20px;
}

section {
display: flex;
max-width: 1200px;
margin: 0 auto;
padding: 20px;
}

main {
flex: 2;
background-color: #fff;
padding: 20px;
}


h2 {
color: #3498db;
}

ul {
list-style-type: circle;
padding: 0;
}

li {
margin-bottom: 10px;
}

/* Additional styles for workshop description */
.workshop-description {
background-color: #ecf0f1;
padding: 20px;
margin-top: 20px;
border-radius: 8px;
}

.pc-members {
margin-top: 20px;
}
</style>
</head>
<body>
<h1>Hello, World!</h1>
<p>AutoML workshop 2024 site.</p>

<header>
<h1>Workshop on Automated Machine Learning (AutoML)</h1>
</header>

<section>
<main>
<!-- Your existing content remains unchanged -->

<!-- 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.


</p>

</p>
We invite submissions on the topics of:
<ul>
<li>Model selection, hyper-parameter optimization, and model search</li>
<li>Neural architecture search</li>
<li>Internet of things (IoT) and automation</li>
<li>Automation bias and misuse</li>
<li>Meta-learning and transfer learning</li>
<li>Bayesian optimization for AutoML</li>
<li>Evolutionary algorithms for AutoML</li>
<li>Multi-fidelity optimization</li>
<li>Predictive models of performance</li>
<li>Automatic feature extraction / construction</li>
<li>Automatic data cleaning</li>
<li>Automatic feature engineering</li>
<li>Automation of semi-supervised and unsupervised machine learning</li>
<li>Robustness of AutoML systems</li>
<li>Human-in-the-loop approaches for AutoML</li>
<li>Trust in AutoML</li>
<li>Learning to learn new algorithms and strategies</li>
<li>Hyperparameter agnostic algorithms</li>
<li>AutoML for neuro-fuzzy systems</li>
</ul>

<!-- Format section -->
<h2>Format:</h2>
<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
from experts in Automated Machine Learning (AutoML) to enrich the workshop experience and provide
valuable insights from leaders in the field.
</p>

<!-- Attendance section -->
<h2>Attendance:</h2>
<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
domain. The workshop is intended also to benefit designers and machine learning users in the broad
area of machine learning. We anticipate receiving approximately 35 submissions.
</p>

</div>
<!-- List of potential workshop PC members -->
<div class="pc-members">
<h2>List of Potential Workshop PC Members:</h2>
<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>
<li>Dmitri Rozgonjuk, Robert Bosch GmbH</li>
<li>David Camacho, Departamento de Sistemas Informáticos, Technical University of Madrid, Madrid, Spain</li>
<li>Francesco Piccialli, Department of Mathematics and Applications, University of Naples, Italy</li>
<!-- Add more PC members as needed -->
</ul>
</div>
</main>


</section>

</body>
</html>

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