基本信息
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Bio
Most of my work centers on information-theoretic and statistical mechanical approaches to learning and control, leading to contributions in bounded rationality models and recasting adaptive control as a causal inference problem. I have also worked on causal induction, and on game- and decision-theoretic models in computational neuroscience.
My research focuses on artificial general intelligence and the formal principles of intelligence, covering aspects such as learning, planning and decision making in both machines and biological organisms. My approach lies at the intersection between machine learning, computational neuroscience, theoretical economics, and physics.
Specifically, I made pioneering contributions to very large-scale planning, causality in reinforcement learning, the connection between meta-learning and Bayesian statistics, and technical AI safety.
My research focuses on artificial general intelligence and the formal principles of intelligence, covering aspects such as learning, planning and decision making in both machines and biological organisms. My approach lies at the intersection between machine learning, computational neuroscience, theoretical economics, and physics.
Specifically, I made pioneering contributions to very large-scale planning, causality in reinforcement learning, the connection between meta-learning and Bayesian statistics, and technical AI safety.
Research Interests
Papers共 73 篇Author StatisticsCo-AuthorSimilar Experts
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CoRR (2022)
ICLR 2023 (2022)
CoRR (2022)
arXiv (Cornell University) (2021)
arXiv (Cornell University) (2020)
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