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职业迁徙
个人简介
I have a broad range of interests in statistical machine learning. A large part of my work has been on Bayesian experimental design: how do we design experiments that will be most informative about the process being investigated? One approach is to optimize the Expected Information Gain (EIG), which can be seen as a mutual information, over the space of possible designs. In my work, I have developed variational methods to estimate the EIG, stochastic gradient methods to optimize over designs, and how to obtain unbiased gradient estimators of EIG. In more recent work, we have studied policies that can choose a sequence of designs automatically. This talk offers a 30 minute introduction to experimental design and my research in this area. To use Bayesian experimental design in practice, we have developed a range of tools in deep probabilistic programming language Pyro: our aim is to allow automatic experimental design for any Pyro model.
研究兴趣
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arxiv(2023)
arxiv(2023)
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ICML 2023pp.24720-24736, (2023)
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CoRR (2023): 7331-7348
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D-Core
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