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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
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<meta name="description" content="Quantifying Local Model Validity using Active Learning">
<meta property="og:title" content="Quantifying Local Model Validity using Active Learning"/>
<meta property="og:description" content="Presenting a framework to validate machine learning models using active learning and gaussian processes."/>
<meta property="og:url" content="https://trustinai.github.io/localmodelvalidity/"/>
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<meta name="twitter:title" content="Quantifying Local Model Validity using Active Learning" />
<meta name="twitter:description" content="Presenting a framework to validate machine learning models using active learning and gaussian processes." />
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<meta name="keywords" content="#UAI2024 #AI #UncertaintyQuantification #ModelValidation #MachineLearning #LimitState"/>
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<title>Quantifying Local Model Validity using Active Learning</title>
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<h1 class="title is-1 publication-title">Quantifying Local Model Validity using Active Learning</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://www.linkedin.com/in/sven-l-693392175/" target="_blank">Sven Lämmle</a><sup>1,2</sup>,</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/can-bogoclu/" target="_blank">Can Bogoclu</a><sup>3</sup>,</span>
<span class="author-block">
<a href="" target="_blank">Robert Vosshall</a><sup>4</sup>,
</span>
<span class="author-block">
<a href="https://trustin.ai" target="_blank">Anselm Haselhoff</a><sup>5</sup>,
</span>
<span class="author-block">
<a href="" target="_blank">Dirk Roos</a><sup>2</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><small><sup>1</sup>ZF Friedrichshafen AG, Germany;<sup>2</sup>Niederrhein University of Applied Sciences, Germany;<sup>3</sup>Zalando SE, Germany;<sup>4</sup>auxmoney GmbH, Germany;<sup>5</sup>Ruhr West University of Applied Sciences, Germany</small></span>
<span class="author-block">Conference on Uncertainty in Artificial Intelligence (UAI), 2024</span>
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class="external-link button is-normal is-rounded is-dark">
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<span>Paper</span>
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<span>Supplementary</span>
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</span>
-->
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<a href="https://github.com/SvenL13/LocalValidity" target="_blank"
class="external-link button is-normal is-rounded is-dark">
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<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
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class="external-link button is-normal is-rounded is-dark">
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<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
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</div>
</section>
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</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
Real-world applications of machine learning models are often subject to legal or policy-based regulations. Some of these regulations require ensuring the validity of the model, i.e., the approximation error being smaller than a threshold. A global metric is generally too insensitive to determine the validity of a specific prediction, whereas evaluating local validity is costly since it requires gathering additional data. We propose learning the model error to acquire a local validity estimate while reducing the amount of required data through active learning. Using model validation benchmarks, we provide empirical evidence that the proposed method can lead to an error model with sufficient discriminative properties using a relatively small amount of data. Furthermore, an increased sensitivity to local changes of the validity bounds compared to alternative approaches is demonstrated.
</p>
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<!-- End paper abstract -->
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<div class="item">
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<img src="static/videos/limit_anim_.gif" alt="Example 1: Limit State"/>
<h2 class="subtitle has-text-centered">
Illustration of a locally valid model: The trained model is marginally valid with 80% probability in certain regions of the input space.
Shows the marginal distribution of the true absolute error, with the 80% quantile corresponding to the tolerance level.
Depicts the learned error model and 90% confidence interval, along with the predicted local valid set.
Samples are sequentially placed to reduce misclassification probability, with most samples near the limit state. </h2> </div>
<div class="item">
<!-- Your image here -->
<img src="static/images/carousel1.png" alt="Example 2: Limit State"/>
<h2 class="subtitle has-text-centered">
Illustration of a locally valid model: The trained model is marginally valid with 80% probability in certain regions of the input space.
Shows the marginal distribution of the true absolute error, with the 80% quantile corresponding to the tolerance level.
Depicts the learned error model and 90% confidence interval, along with the predicted local valid set.
Samples are sequentially placed to reduce misclassification probability, with most samples near the limit state. </div>
</h2>
<div class="item">
<!-- Your image here -->
<img src="static/images/carousel2.png" alt="Example 3: Limit State"/>
<h2 class="subtitle has-text-centered">
Prediction for the modified Rastrigin function after 20 initial and 70 adaptive observations, with specified parameters. The true limit state is shown by the black line.
</h2>
</div>
</div>
</div>
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</section>
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</section>
-->
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<h2 class="title">Poster</h2>
<iframe src="static/pdfs/UAI2024_Poster_Laemmle.pdf" width="100%" height="550">
</iframe>
</div>
</div>
</section>
<!--End paper poster -->
<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{
laemmle2024quantifying,
title={Quantifying Local Model Validity using Active Learning},
author={Sven L{\"a}mmle and Can Bogoclu and Robert Vosshall and Anselm Haselhoff and Dirk Roos},
booktitle={The 40th Conference on Uncertainty in Artificial Intelligence},
year={2024},
url={https://openreview.net/forum?id=muDcwOKf50}
}</code></pre>
</div>
</section>
<!--End BibTex citation -->
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