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<!DOCTYPE html>
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<title>Knowledge and Logical Reasoning in the Era of Data-driven Learning | Workshop at ICML 2023</title>
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<h1 class="project-name">Knowledge and Logical Reasoning in the Era of Data-driven Learning</h1>
<h2 class="project-tagline">Workshop at ICML 2023</h2>
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<a href="/cfp.html" class="btn">Call for Papers</a>
<a href="/papers.html" class="btn">Accepted Papers</a>
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<h1 id="call-for-papers">Call for Papers</h1>
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<td width="30%"><b>Submission Deadline</b></td>
<td width="70%"><s>May 26, 2023</s> June 2, 2023 (AOE)</td>
<!-- <td width="70%"><strike>March 24</strike> March 31, 2023 Anywhere on Earth (AoE)</td> -->
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<td><b>Author notification</b></td>
<td><strike>June 16, 2023</strike></td>
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<td><b>Camera ready deadline</b></td>
<td><strike>July 3, 2023</strike></td>
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<td><b>Submission server</b></td>
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<a href="https://cmt3.research.microsoft.com/KLR2023/">https://cmt3.research.microsoft.com/KLR2023/</a>
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<td><b>Submission format</b></td>
<td>Submissions need to be anonymized and should include at most <b>8</b> pages, excluding the references and appendices. Please adhere to the <a href="https://icml.cc/Conferences/2023/StyleAuthorInstructions">ICML 2023 guidelines and style templates</a>.</td>
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<p>We invite submissions that jointly focus on data-driven learning and knowledge, logical, rule-based and commonsense reasoning. They may describe completed research or work-in-progress. Topics of interest include, but are not limited to, the following:</p>
<ul>
<li>Trustworthy machine learning using knowledge reasoning, extraction and integration</li>
<li>Knowledge and logic reasoning for counteracting bias, variance, and uncertainty of data</li>
<li>Knowledge utilization for efficient data generation and labeling</li>
<li>Knowledge graphs combined with language and vision models for better generalization</li>
<li>Natural language reasoning for large language models</li>
<li>Knowledge graphs for verification of generative models</li>
<li>Factual grounding and attacking hallucination</li>
<li>Scaling task and instruction-based fine tuning of LLMs</li>
<li>Knowledge retrieval, knowledge extraction and knowledge graph construction</li>
<li>Relational and graph based reasoning</li>
<li>Contrastive learning for reasoning</li>
<li>Semantic search and hypothesis discovery</li>
<li>Automated theorem proving and deductive, abductive and inductive reasoning</li>
<li>Physical laws and neural networks - geometric deep learning</li>
<li>Neural algorithmic and logic reasoning</li>
</ul>
<p>Submissions will be reviewed in a double-blind setting. The workshop is non-archival and will not have any official proceedings. Based on the PC’s recommendation, the accepted papers will be allocated either a contributed talk or a poster presentation. Both in-person and virtual formats will be accommodated for the poster sessions.</p>
<p><b>We offer a Best Paper Award ($1,000) and several travel grants (complimentary ICML conference registrations).</b></p>
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<span class="site-footer-credits">Please contact <a href="mailto:nezihe.guerel@inf.ethz.ch">Nezihe Merve Gürel</a> or <a href="mailto:lbo@illinois.edu">Bo Li</a> if you have any questions.<br> The webpage template is by the courtesy of <a href="https://tda-in-ml.github.io/">NeurIPS 2020 Workshop on Topological Data Analysis and Beyond</a>. <br>
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