Information-theoretic measures are suitable to characterize datasets with discrete attributes (or continuous which can be transformed).
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Nov 21, 2024 · Information-theoretic measures are suitable to characterize datasets with discrete attributes (or continuous which can be transformed).
May 9, 2020 · This paper presents novel information-theoretic upper bounds on the meta-generalization gap. Two broad classes of meta-learning algorithms are considered.
Information-theoretic measures are suitable to characterize datasets with discrete attributes (or continuous which can be transformed). They can find ...
We derive a novel information-theoretic analysis of the generalization property of meta-learning algorithms. Concretely, our analysis proposes a generic ...
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We formulate meta learning using information the- oretic concepts; namely, mutual information and the information bottleneck. The idea is to learn a.
This paper presents novel information-theoretic upper bounds on the meta-generalization gap. Two broad classes of meta-learning algorithms are considered.
We propose a task selection algorithm, Information-Theoretic Task Selection (ITTS), based on information theory, which optimizes the set of tasks used for ...
Dec 31, 2021 · In this paper, we introduce the problem of transfer meta-learning, in which tasks are drawn from a target task environment during meta-testing ...
Feb 4, 2024 · Abstract page for arXiv paper 2402.02429: Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning.