Sparse Bayesian multi-task learning for predicting cognitive outcomes from neuroimaging measures in Alzheimer's disease

By Jing Wan, Zhilin Zhang, Jingwen Yan, Taiyong Li, Bhaskar D. Rao, S. Fang, Sungeun Kim, Shannon L. Risacher, Andrew J. Saykin, Li Shen. 2012

77 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Statistics and Probability, Probabilistic & Bayesian Methods, Medical Imaging, Neuroscience

How researchers see this paper

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

In Cognitive Neuroscience

1. Chelsea Finn
2. Sergey Levine
3. Trevor Darrell
4. David Krueger
5. Erin Grant
6. Qiang Yang
7. T. Griffiths
8. Boris N. Oreshkin

In Radiology, Nuclear Medicine and Imaging

1. Sergey Levine
2. Chelsea Finn
3. Kelvin Xu
4. Qiang Yang
5. Trevor Darrell
6. Boris N. Oreshkin
7. Pieter Abbeel
8. Igor Mordatch

In Statistics and Probability

1. David Krueger
2. Erin Grant
3. Katherine A. Heller
4. Qiang Yang
5. Sergey Levine
6. Trevor Darrell
7. T. Griffiths
8. Boris N. Oreshkin

In Probabilistic & Bayesian Methods

1. Chelsea Finn
2. Sergey Levine
3. David Krueger
4. Erin Grant
5. Katherine A. Heller
6. Qiang Yang
7. Trevor Darrell
8. T. Griffiths

In Neuroscience

1. Qiang Yang
2. Sergey Levine
3. Trevor Darrell
4. T. Griffiths
5. Yu Zhang
6. Chelsea Finn

Citations in the corpus

• An Overview of Multi-task Learning (2018, background): “…Alzheimer’s disease assessment scale cognitive subscale [108], prediction of cognitive outcomes from neuroimaging measures in Alzheimer’s disease [109], identification of longitudinal phenotypic markers for Alzheimer’s disease progression prediction [110], prioritization of disease genes [111],…”
• A Survey on Multi-Task Learning (2017, methodology): “In [190], a sparse Bayesian model, which learns correlations between features for all the tasks based on the automatic relevance determination, is proposed to predict cognitive outcomes from neuroimaging measures for the Alzheimers disease.”
• Recasting Gradient-Based Meta-Learning as Hierarchical Bayes (2018, background): “Hierarchical Bayesian models have a long history of use in both transfer learning and domain adaptation (e.g., Lawrence & Platt, 2004; Yu et al., 2005; Gao et al., 2008; Daumé III, 2009; Wan et al., 2012).”
• Probabilistic Model-Agnostic Meta-Learning (2018, background): “Hierarchical Bayesian models are a long-standing approach for few-shot learning that naturally allow for the ability to reason about uncertainty over functions [39, 7, 25, 43, 12, 4, 41].”
• Uncertainty in Multitask Transfer Learning (2018, background): “an efficient algorithm with the same complexity as the original one and a good proxy for the marginal likelihood. 4 Related Work Hierarchical Bayes algorithms for multitask learning has a long history [8, 32, 2]. However most of the literature focus on simple statistical models and do not consider tr”
• Reconciling meta-learning and continual learning with online mixtures of tasks (2018, background): “[44] demonstrated that negative transfer can worsen generalization performance, and avoidance of negative transfer has motivated much work on hierarchical Bayes in transfer learning and domain adaptation [ e.g., 29, 57, 16, 11, 54].”
• Online gradient-based mixtures for transfer modulation in meta-learning (2018, background): “This has motivated much work on HB in transfer learning and domain adaptation (e.g., Lawrence and Platt, 2004; Yu et al., 2005; Gao et al., 2008; Daumé III, 2009; Wan et al., 2012).”