Multi-Task Learning using Generalized t Process

By Yu Zhang, Dit-Yan Yeung. 2010

71 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

How researchers see this paper

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

In Representation & Self-Supervised Learning

1. Qiang Yang
2. Yu Zhang
3. Yu Zhang
4. Jieping Ye
5. Jiayu Zhou
6. Dit-Yan Yeung
7. Changshui Zhang
8. Ying Wei

Citations in the corpus

• An Overview of Multi-task Learning (2018, methodology): “In order to utilize Bayesian averaging to achieve better performance, a multi-task generalized t process is proposed in [43] by placing an inverse-Wishart prior on (cid:6) .”
• A Survey on Multi-Task Learning (2017, methodology): “(20) implies that WTW follows a Wishart distribution asW(·|0,Ω), Zhang and Yeung [91] generalize it as (WTW)t ∼ W(·|0,Ω), (22) where t is a positive integer to model high-order task relationships.”
• Learning Task Grouping and Overlap in Multi-task Learning (2012, background): “There also exist probabilistic models which attempt to learn full task covariance matrix and use it in learning of predictor functions (Zhang & Yeung, 2010b;a; Zhang & Schneider, 2010; Archambeau et al., 2011).”
• A Convex Formulation for Learning Task Relationships in Multi-Task Learning (2010, background): “Moreover, some Bayesian models have been proposed for multi-task learning [7, 15, 27].”
• Multi-Stage Multi-Task Feature Learning (2012, neutral): “We evaluate the six algorithms in terms of the normalized mean squared error (nMSE) and the averaged means squared error (aMSE), which are commonly used in multi-task learning problems (Zhang and Yeung, 2010; Zhou et al., 2011; Gong et al., 2012).”
• 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.”
• Probabilistic Multi-Task Feature Selection (2010, methodology): “The task relationships learned here can be seen as an extension of the task covariance used in [4, 32, 31].”
• Student-t Processes as Alternatives to Gaussian Processes (2014, methodology): “Zhang and Yeung [2010] propose a similar method for handling noise; however, they in-correctly assume that the latent function and noise are independent under this model.”
• Multi-Task Boosting by Exploiting Task Relationships (2012, methodology): “Our point of departure is a regularized method in [12] which learns the task relationships in the form of a task covariance matrix under a regularization framework and is related to maximum a posteriori (MAP) estimation of the weight-space interpretation of the multi-task GP model [10] presented in ”
• Integrating low-rank and group-sparse structures for robust multi-task learning (2011, methodology): “In [36, 38], multivariate student t-processes and their generalization are proposed for distinguishing good tasks from noisy or outlier tasks.”
• 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].”
• Robust multi-task feature learning (2012, methodology): “We evaluate eight multi-task regression algorithms on the test data set, using normalized mean squared error (nMSE) and averaged means squared error (aMSE) as the regression performance measures [39, 10, 42].”
• Parallel Multi-task Learning (2015, methodology): “Besides the regularized methods proposed for multi-task learning, some multi-task Bayesian models are devised based on some Bayesian models such as Gaussian process [23], t process [24], Dirichlet process [25] and so on.”
• A Regularization Approach to Learning Task Relationships in Multitask Learning (2014, methodology): “…have been proposed for multitask learning by using Gaussian process (GP) [Yu et al. 2005; Bonilla et al. 2007], t process [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…”