Sparse Bayesian Multi-Task Learning

By Cédric Archambeau, Shengbo Guo, Onno Zoeter. 2011

52 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. Abhishek Kumar
3. Qiang Yang
4. Yoshua Bengio
5. Percy Liang
6. Dacheng Tao
7. Elad Hazan
8. Sergey Levine

Citations in the corpus

• A Survey on Multi-Task Learning (2017, neutral): “ large. A model similar to problem (21) is proposed in [90] via a matrix-variate normal prior on W: ˘MN(0; 1; 2), where the inverses of 1 and 2 are assumed to be sparse. The MTRL model is extended in [91] to use the symmetric matrixvariate generalized hyperbolic distribution to learn block sparse st”
• 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 Survey of Multi-View Representation Learning (2016, unknown): “Using the natural parameter formulation of exponential family distributions, certain common choices can be incorporated as special cases, which leading to an efficient way of exploiting the robustness of exponential family distribution in practical models [61, 62].”
• 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., 2010; Guo et al., 2011).”
• Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model (2013, methodology): “, 2010), sparse Bayesian multi-task learning (SBMTL) (Archambeau et al., 2011), and multi-task learning with GIW model (MTL-GIW).”
• Variational inference in nonconjugate models (2012, unknown): “Xue et al. (2007) and Archambeau et al. (2011) treat Yeast and Scene as multi-task problems.”
• On Multiplicative Multitask Feature Learning (2014, background): “, 2008), or by examining the tasks’ covariance matrix (Bonilla et al., 2007; Zhang and Yeung, 2010; Guo et al., 2011; Yang et al., 2013).”