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My research interests lie in the general areas of machine learning, Bayesian statistics and computational statistics. Although my group works on a variety of topics ranging from theoretical, through to methodological and applications, I am personally particularly interested in three (overlapping) themes: Bayesian nonparametrics and probabilistic learning, large scale machine learning, and deep learning.
These themes are motivated by the phenomenal growth in the quantity, diversity and heterogeneity of data now available. The analysis of such data is crucial to opening doors to new scientific frontiers and future economic growth. In the longer term, the development of general methods that can deal with such data are important testing grounds for artificial general intelligence systems.
These themes are motivated by the phenomenal growth in the quantity, diversity and heterogeneity of data now available. The analysis of such data is crucial to opening doors to new scientific frontiers and future economic growth. In the longer term, the development of general methods that can deal with such data are important testing grounds for artificial general intelligence systems.
研究兴趣
论文共 401 篇作者统计合作学者相似作者
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ICLR 2024 (2024)
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Amal Rannen-Triki,Jorg Bornschein,Razvan Pascanu,Marcus Hutter,Andras György,Alexandre Galashov,Yee Whye Teh, Michalis K. Titsias
arxiv(2024)
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arxiv(2024)
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arxiv(2024)
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CoRR (2024)
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Soham De,Samuel L. Smith, Anushan Fernando,Aleksandar Botev, George Cristian-Muraru,Albert Gu, Ruba Haroun,Leonard Berrada,Yutian Chen, Srivatsan Srinivasan,Guillaume Desjardins,Arnaud Doucet,
arxiv(2024)
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arxiv(2024)
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NeurIPS (2023)
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arXiv (Cornell University) (2023)
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Leo Klarner,Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler,Garrett Morris,Charlotte Deane,Yee-Whye Teh
ICML 2023 (2023): 17176-17197
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