Learning Gaussian Process Kernels via Hierarchical Bayes

By Anton Schwaighofer, Volker Tresp, Kai Yu. 2004

197 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.

1. Yu Zhang
2. Qiang Yang
3. Yu Zhang
4. Dit-Yan Yeung
5. Jieping Ye
6. Dacheng Tao
7. Jiayu Zhou
8. Mingsheng Long

Citations in the corpus

• Learning Inverse Dynamics by Gaussian process Begrression under the Multi-Task Learning Framework (2009, background): “proach and hence our focus here will be on multi-task learning based on the GP approach [10, 9, 16, 25, 5, 6, 24].”
• Interactive Multi-task Relationship Learning (2016, background): “An example is the low-rank MTL [12, 21, 25], which seeks a shared low-dimensional subspace in task models, and the tasks are related through the shared subspace.”
• Transfer Learning by Distribution Matching for Targeted Advertising (2008, background): “Hierarchical Bayesian models are a standard statistical approach to multi-task learning [5, 6, 7].”
• Integrating low-rank and group-sparse structures for robust multi-task learning (2011, background): “One key assumption in most of the existing MTL algorithms is that all tasks are correlated via a certain structure, which, for example, includes hidden units in neural networks [7], a common prior in a hierarchical Bayesian model [3, 29, 35, 37], parameters in Gaussian process covariance [17], kerne”
• Bayesian Online Multitask Learning of Gaussian Processes (2010, methodology): “Then, the availability of multiple training sets is exploited to learn a better kernel, e.g., using the EM algorithm [23], [24], [25].”
• Recent Advances in Bayesian Optimization (2022, methodology): “As a treatment to the outlier tasks, Yu et al. [275] presented a robust extension of the previous studies [218, 274] by using heavier tailed t-Processes.”
• A Survey on Transfer Learning (2010, methodology): “Schwaighofer et al. [47] proposed to use a hierarchical Bayesian framework (HB) together with GP for multitask learning.”
• Multi-Task Learning using Generalized t Process (2010, methodology): “As such, it inherits the same assumption of (Schwaighofer et al., 2005) that all tasks share the same representation.”
• Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning (2019, background): “Several papers analyze multi-task learning in the context of Gaussian processes (Schwaighofer et al., 2004; Yu et al., 2005; Bonilla et al., 2007), as well as other generative assumptions (HernándezLobato et al.”
• Robust multi-task feature learning (2012, unknown): “Under explicit parameter sharing, all the tasks explicitly share some common parameters; examples include hidden units in neural networks [8, 5], prior in hierarchical Bayesian models [4, 31, 36, 38], parameters of Gaussian process [18], feature mapping matrix [1], classification weight [11] and sim”
• Semi-Supervised Multi-Task Regression (2009, methodology): “This problem also exists in the models of [16] and [17], which later motivated the development of a robust model using t-processes [23].”
• Multi-task Gaussian Process Prediction (2007, background): “Within the GP literature, [14, 15, 16, 17, 18] give models where the covariance matrix of the full (noiseless) system is block diagonal, and each of the M blocks is induced from the same kernel function.”
• A convex formulation for learning shared structures from multiple tasks (2009, methodology): “Bakker, B., & Heskes, T. (2003).”
• Feature-based transfer learning with real-world applications (2010, methodology): “[165] proposed to use a hierarchical Bayesian framework (HB) together with GP for multi-task learning.”
• Flexible Clustered Multi-Task Learning by Learning Representative Tasks (2016, background): “Extensive experimental results are presented in Section 4, followed by conclusions and future works in Section 5.”