Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms

By Yu Zhang. 2013

28 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistical Learning Theory

Citations in the corpus

An Overview of Multi-task Learning (2018, methodology): “Unlike the aforementioned methods, which rely on global learning models, local learning methods such as the k -nearest-neighbor (kNN) classifier are extended in [52] to the multi-task setting and the learning function is defined as f ( x ij ) = (cid”
A Survey on Multi-Task Learning (2017, methodology): “While the aforementioned methods whose task relations are symmetric except [98], Lee et al.”
Multi-Task Learning and Algorithmic Stability (2015, methodology): “2010; Lozano and Swirszcz 2012; Zweig and Weinshall 2013) which decomposes the model parameters into two or more parts with each part modeling one type of sparsity, and constructing multi-task local learners (Zhang 2013).”
Learning Multi-Level Task Groups in Multi-Task Learning (2015, background): “All rights reserved. and Xu 2010; Zhang and Schneider 2010; Zhang and Yeung 2010; Zhang 2013; Zhang and Yeung 2014), and some assume the task structure is hierarchical (Daumé III 2009; Jalali et al. 2010; Görnitz et al. 2011; Lozano and Swirszcz 2012; Zweig and Weinshall 2013).”
Learning Tree Structure in Multi-Task Learning (2015, unknown): “For example, low-rank structure based methods in [1, 3] can learn model parameters sharing a low dimensional subspace for multiple tasks, probabilistic MTL models [27, 25] place probabilistic priors on multiple tasks to learn the model parameters as well as identifying the task relations, regularize”
A Regularization Approach to Learning Task Relationships in Multitask Learning (2014, methodology): “…[Yu et al. 2007; Zhang and Yeung 2010b], Dirichlet process [Xue et al. 2007], Indian buffet process [Rai and Daum´e III 2010; Zhu et al. L´azaro-Gredilla multitask local learning algorithms are proposed in Zhang [2013] to extend the k - Nearest-Neighbor algorithm and the kernel regression method.”
Learning Sparse Task Relations in Multi-Task Learning (2017, methodology): “Moreover, several methods directly learn pairwise relations among tasks, where the pairwise relations are represented by a covariance matrix (Bonilla, Chai, and Williams 2007; Zhang and Yeung 2010a; 2010b; Zhang and Schneider 2010) or just a square matrix (Zhang 2013).”
Learning to Multitask (2018, background): “This approach can be further classified into four categories: low-rank approach [1, 28, 17], task clustering approach [18, 20, 15], task relation learning approach [39, 40, 36, 35, 41, 42, 21, 37], and decomposition approach [10, 19, 44, 16].”
Multi-Stage Multi-Task Learning with Reduced Rank (2016, background): “…and Pontil 2008; Liu, Ji, and Ye 2009; Zhang, Yeung, and Xu 2010), learning task groups (Han and Zhang 2015a), learning task relations (Zhang and Yeung 2010a; Zhang 2013; Zhang and Yeung 2014), utilizing or learning tree structure among tasks (Han et al. 2014; Han and Zhang 2015b), and so on.”
Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation (2014, background): “Multi-task learning has been gathering a great deal of attention, and extensive studies have been conducted both theoretically and experimentally (Thrun, 1996; Evgeniou and Pontil, 2004; Ando and Zhang, 2005; Zhang, 2013; Baxter, 2000).”