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My research is focused on combining learning and control theory with large communication networks such as the Internet. For me, this means: (i) how can we learn the interactions between traffic and networks; and (ii) how can ensure that the data-driven decisions are optimal, in particular at the tail?
In my most recent project, I am tackling the issue of keeping ML models up to date with a focus on tail performance. (Re-training) ML models over time is known as continual learning (CL), and common CL systems are mostly focused on delivering good performance on average. Yet in networking, tail performance is very important, and thus I set out to develop a system that reliably identifies and remembers rare samples to improve model performance at the tail.
In my most recent project, I am tackling the issue of keeping ML models up to date with a focus on tail performance. (Re-training) ML models over time is known as continual learning (CL), and common CL systems are mostly focused on delivering good performance on average. Yet in networking, tail performance is very important, and thus I set out to develop a system that reliably identifies and remembers rare samples to improve model performance at the tail.
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arxiv(2024)
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ACM SIGCOMM '23: Proceedings of the ACM SIGCOMM 2023 Conferencepp.1108-1110, (2023)
arxiv(2022)
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