Inductive Transfer for Bayesian Network Structure Learning

By Alexandru Niculescu-Mizil, Rich Caruana. 2007

122 citations recorded by the source corpus.

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Citations in the corpus

A Survey on Multi-Task Learning (2017, methodology): “In [147], an algorithm is proposed to learn Bayes network structures by assuming that different networks/tasks share similar structures via a common prior and then”
Taskonomy: Disentangling Task Transfer Learning (2018, background): “compositional modeling [33, 8, 11, 21, 53, 89, 87], homomorphic cryptography [40], lifelong learning [90, 13, 82, 81], functional maps [68], certain aspects of Bayesian inference and Dirichlet processes [52, 88, 87, 86, 35, 37], few-shot learning [78, 23, 22, 67, 83], transfer learning [72, 81, 27, ”
Which Tasks Should Be Learned Together in Multi-task Learning? (2019, background): “Transfer learning, [34, 16, 40, 15, 25, 28, 21, 35, 31], is similar to multi-task learning in that solutions are learned for multiple tasks.”
Leveraging Domain Knowledge in Multitask Bayesian Network Structure Learning (2012, methodology): “Multitask learning algorithms generally represent the similarity among tasks in one of three ways: all tasks are assumed to be equally similar (Caruana 1997; Niculescu-Mizil and Caruana 2007); the similarity among tasks is estimated from the same data that is used to train the model (Thrun and O’Sul”
Generalization Bounds for Transfer Learning under Model Shift (2015, background): “Niculescu-Mizil & Caruana (2007) learns Bayes Net structures by biasing learning toward similar structures for each task.”
Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Active Tasks (2018, unknown): “[48, 36, 44, 11, 27, 31, 26, 37, 53, 38].”
Structure learning in action (2010, methodology): “It is also straightforward to use structure learning in Bayesian nets as a modelling tool to understand transfer between different tasks [100].”
Multi-Task Learning of Gaussian Graphical Models (2010, unknown): “…learning has been applied to very diverse problems, such as linear regression (Liu et al., 2009), classification (Jebara, 2004), compressive sensing (Qi et al., 2008), reinforcement learning (Wilson et al., 2007) and structure learning of Bayesian networks (Niculescu-Mizil & Caruana, 2007).”
Transfer learning using computational intelligence: A survey (2015, background): “In [75], the authors extended the Bayesian network learning from a single domain (task) to multiple domains (tasks).”
Flexible Transfer Learning under Support and Model Shift (2014, methodology): “Traditional methods for transfer learning use Markov logic networks [4], parameter learning [5, 6], and Bayesian Network structure learning [7], where specific parts of the model are considered to be carried over between tasks.”