Learning Multiple Tasks with Kernel Methods

By Theodoros Evgeniou, Charles A. Micchelli, Massimiliano Pontil. 2005

995 citations recorded by the source corpus.

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In Statistical Learning Theory

Citations in the corpus

An Overview of Multi-task Learning (2018, methodology): “In earlier studies on this approach, task relations are either defined by model assumptions [36,37] or given by a priori information [38–41].”
Semi-Cyclic Stochastic Gradient Descent (2019, background): “…structure (Ando & Zhang, 2005), shared deep features (e.g. Caruana, 1997), shared kernel or low nuclearnorm structure (e.g. Argyriou et al., 2008; Amit et al., 2007), low-norm additive structure (e.g. Evgeniou & Pontil, 2004), or graph-based relatedness (e.g. Evgeniou et al., 2005; Maurer, 2006).”
A Survey on Multi-Task Learning (2017, methodology): “In [144], both a regularized MTL method [75] and the MTRL method are applied to each view of different tasks and different views in a task are expected to achieve an agreement on unlabeled data.”
Forward and Reverse Gradient-Based Hyperparameter Optimization (2017, methodology): “In order to leverage information among the different classes, we employed a multitask learning (MTL) regularizer (Evgeniou et al., 2005)”
Learning Theory for Distribution Regression (2014, methodology): “On the machine learning side, multi-task learning (Evgeniou et al., 2005), functional response regression (Kadri et al.”
Learngene: From Open-World to Your Learning Task (2021, background): “Transfer learning: Methods such as [26, 59, 60, 53, 36, 71, 15, 74, 16], which focus on transferring the knowledge across domains, aim at improving the performance of target learners in target domains.”
Model Zoo: A Growing Brain That Learns Continually (2021, background): “There are a number of algorithmic tools to estimate task relatedness, e.g., (Evgeniou et al., 2005; Cavallanti et al., 2010; Kumar and Daume III, 2012), and although such methods are popular in transfer learning (Pentina and Lampert, 2015; Jaakkola and Haussler, 1999), one cannot apply them in…”
Meta-Learning: A Survey (2018, neutral): “This is a generally applicable idea, and transfer learning approaches have been proposed for kernel methods (Evgeniou et al., 2005; Evgeniou and Pontil, 2004), parametric Bayesian models (Rosenstein et al.”
Clustered Multi-Task Learning: A Convex Formulation (2008, background): “For examp le, such constraints are typically that the weight vectors of the different tasks belong (a) to a Euclide an ball centered at the origin [5], which implies no sharing of information between tasks apart from t he size of the different vectors, i.”
Optimal Rates for the Regularized Least-Squares Algorithm (2007, background): “This observation was our original motivation for taking into account the effective dimension: a spectral theoretical parameter which quantifies some capacity properties of ρX by means of the kernel.”
A Convex Formulation for Learning Task Relationships in Multi-Task Learning (2010, methodology): “Then ( W ) can be simpliﬁed as 𝑓 ( W ) = tr ( WLW 𝑇 ) where L is the Laplacian matrix of 𝐺 , which is similar to [11].”
A Comprehensive Survey on Transfer Learning (2019, background): “In light of the manifold assumption [30] and the graph-based regularizer [117], [118], fast-DAM designs a domain-dependent regularizer.”
Sparse coding for multitask and transfer learning (2012, neutral): “…sparse coding (Olshausen & Field, 1996), multitask learning (Ando & Zhang, 2005; Argyriou, Evgeniou, Pontil, 2008; Argyriou, Maurer, Pontil, 2008; Ben-David & Schuller, 2003; Caruana, 1997; Evgeniou, Micchelli, Pontil, 2005; Maurer, 2009) and learning to learn (Baxter, 2000; Thrun & Pratt, 1998).”