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Dear all,</div>
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Please see the information of our next VUML seminar (<a href="https://sites.google.com/view/vumls/home">https://sites.google.com/view/vumls/home</a><span style="font-size: 12pt;">) below:</span></div>
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Speaker: <b>Lincen Yang </b>(<a href="https://www.lincen.nl">https://www.lincen.nl</a><span style="font-size: 12pt;">)</span></div>
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Title: MDL-based Interpretable Machine Learning for High-stake Applications (please see the bio of Lincen and the abstract below the email)</div>
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<div class="elementToProof" style="font-size: 12pt;">Time: Monday, <span style="font-size: 12pt;">27, Oct. 10:00</span></div>
<div class="elementToProof" style="font-size: 12pt;">Room: NU-4A67</div>
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<div class="elementToProof" style="font-size: 12pt;">Lincen is visiting from Leiden, and he will stay around for the rest of the day. If you would like to have a chat with him, please let us know!</div>
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Looking forward to seeing all of you on Monday! </div>
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Best from the organizers,</div>
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Zhao & Daniel</div>
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<b>Bio</b>: Lincen is a postdoctoral researcher at Leiden Institute of Advanced Computer Science (LIACS) and a guest researcher at Leiden University Medical Center (LUMC). His research lies at the intersection of interpretable machine learning and information-theoretic
data mining. Specifically, he studies the Minimum Description Length (MDL) principle and develops human-centered algorithms that enable trustworthy knowledge discovery and high-stakes decision-making. Lincen also organized the inaugural Human-Centered Data
Mining workshop at ECML-PKDD 2025.</div>
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<b>Abstract</b>: Applying machine learning to high-stakes domains such as healthcare and manufacturing requires models that are both interpretable and capable of quantifying uncertainty. This talk presents two related lines of research: (1) combining interpretability
with uncertainty quantification through a tree-based conditional density estimation method, and (2) enhancing model comprehensibility via probabilistic rule sets that reduce the cognitive workload of understanding model behavior. Two real-world use cases—processor
microarchitecture design in computer architecture and ICU readmission analysis in clinical informatics—illustrate their effectiveness in supporting data-driven decision-making. Built upon the Minimum Description Length (MDL) framework, these works leverage
its hyperparameter-free formulation to enable interpretable, reliable, and easy-to-use methods for domain experts.</div>
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