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My research works towards developing systems which learn to accomplish tasks through experience from interacting with the environment, with as little experience as possible. I aim to let the underlying principles of inference and learning guide my work, both from the point of view of developing practical methods from first principles, and from finding underlying principles in existing methods.
My research is motivated by reinforcement learning methods which use explicit predictive models of the world to plan behaviour. This approach improves data efficiency, as knowledge about the world generalises strongly to new situations. Learning good models of the world, with a reliable estimate of their own uncertainty, is crucial to the success of these methods. In addition, they need to be automatic, in the sense that they should not rely on human design or intervention as they learn.
Currently, the main component of my research is building better predictive models. In reinforcement learning / decision making applications, we require a) uncertainty estimates, for avoiding or taking calculated risks, and b) automatic adaptation with increasing data, as more experience is gained. Bayesian inference provides an elegant framework for representing uncertainty, and automating many aspects of the modelling process. Currently, I am interested in bringing the benefits of Bayesian inference to deep learning models, using Gaussian processes as a building block.
My work has been presented at the leading machine learning conferences (NeurIPS and ICML), including an oral presentation and a best paper award. Personally, I’m currently enthusiastic about our paper on learning what invariance should be used as an inductive bias for a particular dataset.
My research is motivated by reinforcement learning methods which use explicit predictive models of the world to plan behaviour. This approach improves data efficiency, as knowledge about the world generalises strongly to new situations. Learning good models of the world, with a reliable estimate of their own uncertainty, is crucial to the success of these methods. In addition, they need to be automatic, in the sense that they should not rely on human design or intervention as they learn.
Currently, the main component of my research is building better predictive models. In reinforcement learning / decision making applications, we require a) uncertainty estimates, for avoiding or taking calculated risks, and b) automatic adaptation with increasing data, as more experience is gained. Bayesian inference provides an elegant framework for representing uncertainty, and automating many aspects of the modelling process. Currently, I am interested in bringing the benefits of Bayesian inference to deep learning models, using Gaussian processes as a building block.
My work has been presented at the leading machine learning conferences (NeurIPS and ICML), including an oral presentation and a best paper award. Personally, I’m currently enthusiastic about our paper on learning what invariance should be used as an inductive bias for a particular dataset.
研究兴趣
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Jose Pablo Folch,Calvin Tsay,Robert M Lee, Behrang Shafei, Weronika Ormaniec,Andreas Krause,Mark van der Wilk,Ruth Misener,Mojmír Mutný
CoRR (2024)
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ArXiv (2024)
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bioRxiv (Cold Spring Harbor Laboratory) (2024)
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CoRR (2023)
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CoRR (2023)
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