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Uncertainty-Challenge-Workshop.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta content="width=device-width, initial-scale=1.0" name="viewport">
<title>Uncertainty Challenge Workshop | FAIR Universe</title>
<meta content="Uncertainty aware large-compute-scale AI platform for high energy physics and cosmology" name="description">
<meta content="FAIR Universe, High Energy Physics, Cosmology, Uncertainty aware" name="keywords">
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<body>
<!-- ======= Hero Section ======= -->
<section class="hero">
<h1>FAIR Universe: Uncertainty Challenge Workshop</h1>
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<!-- ======= NeurIPS Section ======= -->
<section id="about" class="about">
<div class="container">
<div class="section-title">
<p>The workshop will be held on December 14, 2024 at the Vancouver Convention Center in Vancouver, BC, Canada as a part of the <a href="https://neurips.cc/" target="_blank">38th annual conference on Neural Information Processing Systems (NeurIPS)</a>.</p>
<a class="btn btn-lg bg-primary text-light" target="_blank" href="https://www.codabench.org/competitions/2977/"><i class="bi bi-globe2"></i> Join the Challenge here</a>
<a class="btn btn-lg btn-secondary" href="#workshop-schedule"><i class="bi bi-calendar3"></i> Workshop Schedule</a>
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<!-- ======= Workshop Section ======= -->
<section>
<div class="container" id="workshop-schedule">
<div class="section-title">
<h2>Workshop Schedule</h2>
<p>Join us for the Higgs Uncertainty Challenge Workshop at NeurIPS 2024</p>
<p><strong>Date:</strong> Saturday, December 14</p>
<p><strong>Time:</strong> 9:00 AM - 12:00 PM</p>
<p><strong>Location:</strong> West Meeting Room 215, 216</p>
</div>
<div class="workshop-schedule section-body">
<div class="schedule-item">
<span class="time color1">9:00 AM - 9:05 AM</span>
<div class="details-speaker">
<span class="details">Opening</span>
<span class="speaker">Ragansu Chakkappai · Wahid Bhimji · Jordan Dudley · Sascha Diefenbacher</span>
</div>
</div>
<div class="schedule-item">
<span class="time color2">9:05 AM - 9:45 AM</span>
<div class="details-speaker">
<span class="details">Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference</span>
<span class="speaker">Ann Lee</span>
</div>
</div>
<div class="schedule-item">
<span class="time color3">9:45 AM - 10:00 AM</span>
<div class="details-speaker">
<span class="details">DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods</span>
<span class="speaker">Becky Nevin</span>
</div>
</div>
<div class="schedule-item">
<span class="time color4">10:00 AM - 10:15 AM</span>
<div class="details-speaker">
<span class="details">Neural network prediction of strong lensing systems with domain adaptation and uncertainty quantification</span>
<span class="speaker">Shrihan Agarwal</span>
</div>
</div>
<div class="schedule-item">
<span class="time color5">10:15 AM - 10:30 AM</span>
<div class="details-speaker">
<span class="details">Break</span>
<span class="speaker">Networking</span>
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</div>
<div class="schedule-item">
<span class="time color6">10:30 AM - 11:00 AM</span>
<div class="details-speaker">
<span class="details">HiggsML Uncertainty Challenge Introduction</span>
<span class="speaker">Sascha Diefenbacher · Jordan Dudley</span>
</div>
</div>
<div class="schedule-item">
<span class="time color8">11:00 AM - 11:15 AM</span>
<div class="details-speaker">
<span class="details">2nd Place Competition Milestone</span>
<span class="speaker">Yota Hashizume</span>
</div>
</div>
<div class="schedule-item">
<span class="time color9">11:15 AM - 11:45 AM</span>
<div class="details-speaker">
<span class="details">1st Place Competition Milestone (Ensembles and Uncertainty Quantification)</span>
<span class="speaker">Ibrahim Elsharkawy</span>
</div>
</div>
<div class="schedule-item">
<span class="time color10">11:45 AM - 12:00 PM</span>
<div class="details-speaker">
<span class="details">Closeout</span>
<span class="speaker">Sascha Diefenbacher · David Rousseau · Shih-Chieh Hsu</span>
</div>
</div>
</div>
<div class="section-title">
<p>For more details and uptodate schedule please check the <a href="https://neurips.cc/virtual/2024/competition/84792" target="_blank">NeurIPS Schedule</a>.</p>
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</div>
</section>
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<div class="container">
<div class="section-title">
<h2>About the Challenge</h2>
<p>This NeurIPS 2024 Machine Learning competition is one of the first to strongly emphasise mastering uncertainties in the input training dataset and outputting credible confidence intervals. This challenge explores uncertainty-aware AI techniques for High Energy Physics (HEP).</p>
<p>The context is the measurement of the Higgs Boson signal like in HiggsML challenge on Kaggle in 2014. Participants should design an advanced analysis technique that can not only measure the signal strength but also provide a confidence interval</p>
<p>The confidence interval should include statistical and systematic uncertainties (concerning detector calibration, background levels, etc…). It is expected that advanced analysis techniques that can control the impact of systematics will perform best. This challenge presents an opportunity to push the boundaries of machine learning applications within physics while still focusing on essential ML skills like robust model development and uncertainty quantification.</p>
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</section>
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<section>
<div class="container">
<div class="section-title">
<h2>Organizers</h2>
<p>We gratefully acknowledge the efforts of the FAIR Universe Team, composed of researchers dedicated to advancing high energy physics, cosmology, and machine learning for the benefit of the scientific community.</p>
<p>For inquiries, please contact us at: <a href="mailto:[email protected]">[email protected]</a></p>
</div>
<div class="row section-body">
<ul>
<li>Wahid Bhimji</li>
<li>Benjamin Nachman</li>
<li>Paolo Calafiura</li>
<li>Peter Nugent</li>
<li>Benjamin Thorne</li>
<li>Chris Harris</li>
<li>Sascha Diefenbacher</li>
<li>Steven Farrell</li>
<li>David Rousseau</li>
<li>Ragansu Chakkapai</li>
<li>Mathis Reymond</li>
<li>Shih-Chieh Hsu</li>
<li>Elham Khoda</li>
<li>Yuan-Tang Chou</li>
<li>Yulei Zhang</li>
<li>Isabelle Guyon</li>
<li>Ihsan Ullah</li>
<li>Daniel Whiteson</li>
<li>Aishik Ghosh</li>
</ul>
</div>
</div>
</section>
<!-- End Organizers Section -->
<!-- ======= Competition Section ======= -->
<section>
<div class="container">
<div class="section-title">
<h2>Competition Material</h2>
<p>Find all the competition resources below:</p>
<div class="row section-body">
<ul>
<li><strong><a href="https://www.codabench.org/competitions/2977/" target="_blank">Codabench Challenge Page:</a></strong> This serves as the platform to submit entries to the competition.</li>
<li><strong><a href="https://fair-universe.lbl.gov/tutorials/Higgs_Uncertainty_Challenge-Codabench_Tutorial.pdf" target="_blank">Tutorial Slides:</a></strong> These slides will help you register and submit a sample dummy submission.</li>
<li><strong><a href="https://www.codabench.org/datasets/download/b9e59d0a-4db3-4da4-b1f8-3f609d1835b2/" target="_blank">Training Data (6.5 GB):</a></strong> Download the training data if you you want to experiment with the data on your local machines.</li>
<li><strong><a href="https://fair-universe.lbl.gov/docs/" target="_blank">Documentation:</a></strong> This contains detailed information about the <a href="https://fair-universe.lbl.gov/docs/pages/overview.html#problem-setting" target="_blank">science behind the challenge</a>, the <a href="https://fair-universe.lbl.gov/docs/pages/data.html" target="_blank">specifics of the data</a>, and <a href="https://fair-universe.lbl.gov/docs/rst_source/modules.html" target="_blank">documents the code</a> used to facilitate the evaluation of the competition. It also describes the <a href="https://fair-universe.lbl.gov/docs/pages/evaluation.html" target="_blank">evaluation metric</a>.</li>
<li><strong><a href="https://github.com/FAIR-Universe/HEP-Challenge/tree/master/" target="_blank">GitHub Repository:</a></strong> This hosts the code for testing submissions, as well as the <a href="https://github.com/FAIR-Universe/HEP-Challenge/blob/master/StartingKit_HiggsML_Uncertainty_Challenge.ipynb" target="_blank">starting kit notebook</a>. The starting kit is also available on <a href="https://colab.research.google.com/github/FAIR-Universe/HEP-Challenge/blob/master/StartingKit_HiggsML_Uncertainty_Challenge.ipynb" target="_blank">Google Colab</a>.</li>
<li><strong><a href="https://arxiv.org/abs/2410.02867" target="_blank">White Paper:</a></strong> This serves as a full breakdown of the competition in detail.</li>
</ul>
</div>
</div>
</div>
</section>
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</body>
</html>