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This is an announcement of Zhengxu Xia's Candidacy Exam.<br class="">
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Candidate: Zhengxu Xia<br class="">
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Date: Tuesday, June 04, 2024<br class="">
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Time: 3:30 pm CT<br class="">
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Remote Location: <a href="https://uchicago.zoom.us/j/91658660927?pwd=bXJ3NUNseEU2cTB2SWFYQlBEQ21yZz09" class="">https://uchicago.zoom.us/j/91658660927?pwd=bXJ3NUNseEU2cTB2SWFYQlBEQ21yZz09</a><br class="">
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Location: JCL 298<br class="">
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Title: Guided optimization for learning-based solutions for networking<br class="">
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Abstract: Unlike machine learning (ML) models trained for applications like object detection and classification which consume data and yield the output directly to the end users, many ML models trained for networking problems are often embedded between the
application layer and the network due to the layered characteristics in computer networks. For example, reinforcement learning (RL) models trained for congestion control and adaptive bitrate streaming sit between the network and the video codec. Therefore,
the diversity and dynamics of the application layer and the network can both affect the performance and the behavior of a ML model trained for networking problems. This work introduces how to guide the ML model training for networking to adapt to diverse network
environments and application layer changes by leveraging heuristic based solutions or application layer entities like video codecs.<br class="">
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Advisors: Junchen Jiang<br class="">
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Committee Members: Junchen Jiang, Nick Feamster, and Francis Y. Yan<br class="">
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