Efficient Meta Learning via Minibatch Proximal Update

By Pan Zhou, Xiaotong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng. 2019

98 citations recorded by the source corpus.

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In Meta & Continual Learning

Citations in the corpus

Federated Optimization in Heterogeneous Networks (2018, background): “…interesting connection between meta-learning methods and federated optimization methods (Khodak et al., 2019), and similar proximal terms have recently been investigated in the context of meta-learning for improved performance on few-shot learning tasks (Goldblum et al., 2020; Zhou et al., 2019).”
Personalized Federated Learning with Moreau Envelopes (2020, neutral): “The definition of Fi(w) is the well-known Moreau envelope, which facilitates several learning algorithm designs [40, 41].”
Personalized Federated Learning: A Meta-Learning Approach (2020, methodology): “The centralized version of model-agnostic meta-learning (MAML) problem was first proposed in [2] and followed by a number of papers studying its empirical characteristics [26, 27, 28, 29, 30, 31] as well as its convergence properties [32, 33].”
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning (2020, methodology): “…of gradient-based meta-learning algorithms have been recently studied in the non-convex setting [Fallah et al., 2019, Rajeswaran et al., 2019, Zhou et al., 2019], these results only provide stationary-point convergence guarantees and do not show a reduction in sample complexity, the primary…”
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach (2020, unknown): “The centralized version of model-agnostic meta-learning (MAML) problem was first proposed in [2] and followed by a number of papers studying its empirical characteristics [24–29] as well as its convergence properties [30, 31].”
Personalized Federated Learning using Hypernetworks (2021, background): “Many papers proposed a decentralized version of the model agnostic meta-learning (MAML) problem (Fallah et al., 2020a; Li et al., 2017; Behl et al., 2019; Zhou et al., 2019; Fallah et al., 2020b).”
Multi-Step Model-Agnostic Meta-Learning: Convergence and Improved Algorithms (2020, methodology): “Zhou et al. (2019) proposed an efficient meta-learning approach based on a minibatch proximal update.”