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<div><span style="font-family: Calibri; font-size: 14.6667px; color: rgb(33, 33, 33); background-color: rgb(255, 255, 255);">This is an announcement of
</span><span style="font-family: Calibri; font-size: 14.6667px; color: rgb(7, 7, 6); background-color: rgb(255, 255, 255);">Yibo Jiang</span><span style="font-family: Calibri; font-size: 14.6667px; color: rgb(33, 33, 33); background-color: rgb(255, 255, 255);">'s MS Presentation</span><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);"><br>
</span><span style="font-family: Calibri; font-size: 14.6667px; color: rgb(33, 33, 33); background-color: rgb(255, 255, 255);">===============================================</span></div>
<div class="elementToProof"><span style="font-family: Calibri; font-size: 14.6667px; color: rgb(33, 33, 33); background-color: rgb(255, 255, 255);">Candidate:
</span><span style="font-family: Calibri; font-size: 14.6667px; color: rgb(7, 7, 6); background-color: rgb(255, 255, 255);">Yibo Jiang</span></div>
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<div class="elementToProof"><span style="font-family: Calibri; font-size: 14.6667px; color: rgb(33, 33, 33); background-color: rgb(255, 255, 255);">Date: Monday, March 25, 2024</span></div>
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<div class="elementToProof"><span style="font-family: Calibri; font-size: 14.6667px; color: rgb(33, 33, 33); background-color: rgb(255, 255, 255);">Time: 11 am CST</span></div>
<div><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);"><br>
</span><span style="font-family: Calibri; font-size: 14.6667px; color: rgb(33, 33, 33); background-color: rgb(255, 255, 255);">Location: JCL 298</span><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);"><br>
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</span><span style="font-family: Calibri; font-size: 14.6667px; color: rgb(33, 33, 33); background-color: rgb(255, 255, 255);">Title: Uncovering Meanings of Embeddings via Partial Orthogonality</span><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);"><br>
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</span><span style="font-family: Calibri; font-size: 14.6667px; color: rgb(33, 33, 33); background-color: rgb(255, 255, 255);">Abstract: Machine learning tools often rely on embedding text as vectors of real numbers. In this paper, we study how the semantic
structure of language is encoded in the algebraic structure of such embeddings. Specifically, we look at a notion of" semantic independence" capturing the idea that, eg," eggplant" and" tomato" are independent given" vegetable". Although such examples are
intuitive, it is difficult to formalize such a notion of semantic independence. The key observation here is that any sensible formalization should obey a set of so-called independence axioms, and thus any algebraic encoding of this structure should also obey
these axioms. This leads us naturally to use partial orthogonality as the relevant algebraic structure. We develop theory and methods that allow us to demonstrate that partial orthogonality does indeed capture semantic independence. Complementary to this,
we also introduce the concept of independence preserving embeddings where embeddings preserve the conditional independence structures of a distribution, and we prove the existence of such embeddings and approximations to them.</span><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);"><br>
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<div class="elementToProof"><span style="font-family: Calibri; font-size: 14.6667px; color: rgb(33, 33, 33); background-color: rgb(255, 255, 255);">Advisors: Victor Veitch</span></div>
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<div class="elementToProof"><span style="font-family: Calibri; font-size: 14.6667px; color: rgb(33, 33, 33); background-color: rgb(255, 255, 255);">Committee Members: Victor Veitch, Haifeng Xu, Bryon Aragam, and Yuxin Chen</span></div>
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