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<!DOCTYPE html>
<html lang="en">
<head>
<title>DivExplorer Project</title>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="description" content="The DivExplorer Project">
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bulma@0.9.2/css/bulma.min.css">
<style>
.narrow {
width: max-content;
}
</style>
</head>
<body>
<div class="section">
<div class="title has-text-warning-dark">
The DivExplorer Project
</div>
<div class="block has-text-weight-medium">
<p>
DivExplorer enables to analyze subgroup performance in datasets, efficiently identifying the data subgroups that are anomalous.
</p>
<p class="mt-2">
Given a dataset, DivExplorer can find subgroups where specified attributes have higher or lower average value compared to the overall dataset.
As an example, this can be used to find subgroups in a census dataset that have higher than average income.
</p>
<p class="mt-2">
In machine learning, DivExplorer enables the idenfitication of data subgroups for which classifiers have higher false-positive or false-negative rates than the average, or the identification of subgroups that are ranked higher or lower than the average.
</p>
<p class="mt-2">
Here, you can find the papers and videos related to the project, as well a Python package you can use to analyze your datasets.
</p>
</div>
<div class="block">
<div class="subtitle has-text-weight-bold has-text-success-dark">Python Package</div>
<div class="has-text-weight-medium mt-3">
You can analyze your datasets using the
<a href="https://pypi.org/project/divexplorer/">divexplorer</a>
Python package, and you can look at its
<a href="https://github.com/divexplorer/divexplorer.git">source code and documentation</a>.
Here is a <a href="https://github.com/divexplorer/divexplorer/blob/main/notebooks/DivExplorerExample.ipynb">notebook</a>
that demonstrates how to use the package to analyze the behavior of datasets and classifiers. The notebook can be run on
<a href="https://colab.research.google.com">Google Colab</a> for your convenience.
</div>
</div>
<div class="block">
<div class="subtitle has-text-weight-bold has-text-success-dark">Videos</div>
<div class="columns">
<div class="column narrow is-narrow">
<div class="box has-text-centered narrow">
<iframe width="300" height="150" src="https://www.youtube.com/embed/C5IUNvgWHhU" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<div class="has-text-weight-semibold is-narrow">
5-minute introduction
</div>
</div>
</div>
<div class="column narrow is-narrow">
<div class="box has-text-centered narrow">
<div>
<iframe width="300" height="150" src="https://www.youtube.com/embed/qNS19pw3I8o" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
</div>
<div class="has-text-weight-semibold">
20-minute in-depth
</div>
</div>
</div>
</div>
</div>
<div class="block">
<div class="subtitle has-text-weight-bold has-text-success-dark">Papers</div>
<div class="mt-3">
<span class="paper_title has-text-weight-semibold">
<a href="staticICASSP23.pdf" class="has-text-link-dark">Exploring Subgroup Performance in End-To-End Speech Models</a>.
</span>
<span class="authors">A. Kodounas, E. Pastor, G. Attanasio, V. Mazzia, M. Giollo, T. Gueudre, L. Cagliero, L. de Alfaro, E. Baralis, D. Amberti.</span>
<span class="paper_details">In <em>Proceedings of the International Conference on Acoustings, Speech, and Signal Processing</em> (ICASSP)</span><span>, 2023.</span>
</div>
<div class="mt-3">
<span class="paper_title has-text-weight-semibold">
<a href="static/ICDE_Divergence_Discretization_Optimization.pdf" class="has-text-link-dark">A Hierarchical Approach to Anomalous Subgroup Discovery</a>.
</span>
<span class="authors">E. Pastor, E. Baralis, L. de Alfaro.</span>
<span class="paper_details">In <em>Proceedings of the 39th IEEE International Conference on Data Engineering</em>
(ICDE), 2023.</span>
</div>
<div class="mt-3">
<span class="paper_title has-text-weight-semibold">
<a href="static/DivExplorer.pdf" class="has-text-link-dark">Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence</a>.
</span>
<span class="authors">E. Pastor, L. de Alfaro, E. Baralis.</span>
<span class="paper_details">In <em>Proceedings of the 2021 ACM SIGMOD Conference</em></span><span>, 2021.</span>
</div>
<div class="mt-3">
<span class="paper_title has-text-weight-semibold">
<a href="static/DivExplorer_VLDB_Demo.pdf" class="has-text-link-dark">How Divergent Is Your Data?</a>
</span>
<span class="authors">E. Pastor, A. Gavgavian, E. Baralis, L. de Alfaro.</span>
<span class="paper_details">In <em>Proceedings of the 47th International Conference on Very Large Data Bases (VLDB), Demo Track</em></span><span>, 2021.</span>
</div>
<div class="mt-3">
<span class="paper_title has-text-weight-semibold">
<a href="static/KDD_2021_DivExplorer.pdf" class="has-text-link-dark">Identifying Biased Subgroups in Ranking and Classification</a>.
</span>
<span class="authors">E. Pastor, L. de Alfaro, E. Baralis.</span>
<span class="paper_details">In <em>Proceedings of the Responsible AI @ KDD 2021
Workshop</em>, 2021.</span>
</div>
</div>
<!--
<div class="block">
<div class="subtitle has-text-weight-bold">Slide Decks</div>
</div>
-->
<div class="block">
<div class="subtitle has-text-weight-bold has-text-success-dark">Project Members</div>
<div class="has-text-weight-medium">
<a hfer="https://dbdmg.polito.it/wordpress/people/elena-baralis/">Elena Baralis</a>,
<a href="https://luca.dealfaro.com">Luca de Alfaro</a>,
<a href="https://github.com/elianap">Eliana Pastor</a>.
</div>
</div>
</div>
</body>