Multi-Task Learning for Classification with Dirichlet Process Priors

By Ya Xue, Xuejun Liao, Lawrence Carin, Balaji Krishnapuram. 2007

621 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods

How researchers see this paper

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

In Probabilistic & Bayesian Methods

Citations in the corpus

An Overview of Multi-task Learning (2018, methodology): “Unlike[29,30],whichareBayesianmodels,there are several regularized methods [31–35] to do task clustering.”
LEAF: A Benchmark for Federated Settings (2018, background): “…contrary to realistic federated networks, is usually explored in regimes with small numbers of tasks and samples, e.g., the popular Landmine Detection [38, 26, 36, 30], Computer Survey [2, 1, 14] and Inner London Education Authority School [26, 17, 1, 2, 14] datasets have at most 200 tasks each.”
A Survey on Multi-Task Learning (2017, methodology): “In [3], [4], [5], a new MTL setting called asymmetric multitask learning is investigated and this setting considers a different scenario where a new task is arrived when multiple tasks have been learned jointly via some MTL method.”
A PAC-Bayesian bound for Lifelong Learning (2013, methodology): “We optimize (17), and due to the squared loss, the problem has a closed form solution: To make results comparable with the baseline algorithms, we report the squared error multiplied by the squared value of the maximum examination score.”
Learning Task Grouping and Overlap in Multi-task Learning (2012, methodology): “Examples of such approaches are (Jacob et al., 2008; Xue et al., 2007) where tasks are assumed to be clustered and parameters of tasks within a cluster lie close to each other in l2 norm sense.”
A Convex Formulation for Learning Task Relationships in Multi-Task Learning (2010, methodology): “Another widely studied approach for multi-task learning is the task clustering approach [25, 3, 26].”
Bayesian Multitask Learning with Latent Hierarchies (2009, methodology): “The second domain adaptation task we attempt is landmine detection [15].”
Deep Multi-task Representation Learning: A Tensor Factorisation Approach (2016, methodology): “E.g., [Xue et al., 2007] assumes S · ,i (the i th column of S ) is a unit vector generated by a Dirichlet Process and [Passos et al., 2012] models W using linear factor analysis with Indian Buﬀet Process [Griﬃths and Ghahramani, 2011] prior on S .”
Multi-Task Learning and Algorithmic Stability (2015, methodology): “As well as the aforementioned multi-task models, some Bayesian models have been extended to multi-task setting by using Gaussian process (Bonilla, Chai, and Williams 2007), Dirichlet process (Xue et al. 2007), t process (Zhang and Yeung 2010b) and so on.”
Multi-Task Learning as Multi-Objective Optimization (2018, methodology): “In soft parameter sharing, all parameters are task-speciﬁc but they are jointly constrained via Bayesian priors (Xue et al., 2007; Bakker and Heskes, 2003) or a joint dictionary (Argyriou et al., 2007; Long and Wang, 2015; Yang and Hospedales, 2016; Ruder, 2017).”
Multi-Task Boosting by Exploiting Task Relationships (2012, methodology): “Moreover, one widely used approach for multi-task learning is the task clustering approach [7,8,9] in which the main idea is to group the tasks into several clusters and then learn identical or similar data features or model parameters for the tasks within each cluster.”
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation (2019, background): “Our work can be seen as an asymmetric multi-task learning framework [37, 43, 44], in which we aim to utilize the connection between RS and KG to help improve their performance, and the two tasks are trained with different frequencies.”