Lifelong Learning with Weighted Majority Votes

By Anastasia Pentina, Ruth Urner. 2016

29 citations recorded by the source corpus.

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Citations in the corpus

Gradient Episodic Memory for Continual Learning (2017, background): “This learning setting led to implementations [Carlson et al., 2010, Ruvolo and Eaton, 2013, Ring, 1997], and theoretical investigations [Baxter, 2000, Balcan et al., 2015, Pentina and Urner, 2016], although the latter ones have been restricted to linear models.”
Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions (2022, background): “The works [33, 25, 1, 4] provide theoretical guarantees on lifelong learning.”
Distribution-Free One-Pass Learning (2017, background): “Lifelong learning is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in the past, and uses/adapts it to help future learning and problem solving [23], [24], [25].”
Regret Bounds for Lifelong Learning (2016, background): “, 2016, and references therein), we are aware of only few papers which address the statistical analysis of lifelong learning (Balcan et al., 2015; Herbster et al., 2016; Pentina and Urner, 2016).”
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization (2019, methodology): “The LTL literature in the online setting [1, 9, 10, 24] has received limited attention and is less developed than standard LTL approaches, in which the data are processed in one batch as opposed to incrementally, see for instance [4, 19, 20, 21, 23].”
ORACLE: Order Robust Adaptive Continual LEarning (2019, background): “We follow a common problem setting in continual learning with network expansion (Pentina & Urner, 2016; Yoon et al., 2018; Xu & Zhu, 2018).”
Learning To Learn Around A Common Mean (2018, background): “Although LTL is naturally suited for the incremental setting, we are only aware of few theoretical investigations about it [1, 3, 12, 17, 25].”