Learning To Learn Around A Common Mean

By Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil. 2018

85 citations recorded by the source corpus.

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Citations in the corpus

Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent (2020, background): “egret bounds for lifelong learning (Alquier et al.,2017), or study the sample complexity and convergence of meta-learning algorithms (Denevi et al.2018a,Du et al.2020,Ji et al.2020,Saunshi et al.2020,Denevi et al. 2018b). 8. Discussion We discuss some assumptions and limits of our analysis. Lineariz”
Meta-Learning in Neural Networks: A Survey (2020, background): “This can lead to a learned loss that is easier to optimize (less local minima) than standard alternatives [22], [25], [117], provides faster learning with improved generalization [43], [118], [119], [120], robustness to label noise [121], or whose minima corresponds to a model more robust to domain ”
Generalizing from a Few Examples (2019, methodology): “sumptions are bounded. Approximation errorE app(H)is bounded in [33] for meta-learning using MAML. Estimation error E est(H,I)is bounded for meta-learning of ridge regression model with a common mean [23], and fine-tuning a given parameterθ0 learned by feedforward neural networks [73]. Apart from th”
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning (2022, background): “Many recent works focus on analyzing the generalization ability of MAML (Farid & Majumdar, 2021; Denevi et al., 2018; Rothfuss et al., 2021; Chen & Chen, 2022) and improving the generalization performance of MAML (Finn & Levine, 2018; Gonzalez & Miikkulainen, 2020; Park & Oliva, 2019).”
Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach (2019, background): “A meta-model characterizes the task commonality and generalizes its prediction to those unseen tasks from a related environment (Maurer 2009; Maurer et al. 2016; Denevi et al. 2018).”
Efficient Meta Learning via Minibatch Proximal Update (2019, background): “The most closely relevant work to ours are [22, 27] and [21] which also consider the prior hypothesis biased regularized empirical risk minimization for intra-task learning.”
Exploiting Shared Representations for Personalized Federated Learning (2021, methodology): “…of tasks is a classical approach in multi-task learning [Ando et al., 2005, Balcan et al., 2015, Baxter, 2000, Bengio et al., 2013, Bullins et al., 2019, Denevi et al., 2018, Kong et al., 2020, LeCun et al., 2015, Pontil and Maurer, 2013, Rish et al., 2008, Tripuraneni et al., 2020b].”
Multi-Step Model-Agnostic Meta-Learning: Convergence and Improved Algorithms (2020, methodology): “…meta-initialization algorithms such as MAML, meta-regularization approaches aim to learn a good bias for a regularized empirical risk minimization problem for intra-task learning (Alquier et al., 2016; Denevi et al., 2018b,a, 2019; Rajeswaran et al., 2019; Balcan et al., 2019; Zhou et al., 2019).”
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization (2019, methodology): “The problem of learning-to-learn (LTL) [4, 30] is receiving increasing attention in recent years, due to its practical importance [11, 26] and the theoretical challenge of statistically principled and efficient solutions [1, 2, 21, 23, 9, 10, 12].”
Provable Guarantees for Gradient-Based Meta-Learning (2019, background): “Denevi et al. (2018b) is especially similar in spirit to our work in that it focuses on the usefulness of meta-learning compared to single-task learning, showing that their method is better than the (cid:96) 2 -regularized ERM baseline.”
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms (2019, background): “In a similar line of work, [13, 14] consider the case where each task is a regression problem where its underlying distribution of data points itself comes from a general distribution.”
How to Train Your MAML to Excel in Few-Shot Classification (2021, background): “A “meta-model” summarizes the common characteristic of tasks and generalizes them to those novel but related tasks [19, 58, 59].”