A theoretical framework for learning from a pool of disparate data sources

By Shai Ben-David, Johannes Gehrke, Reba Schuller Borbely. 2002

75 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

A Survey on Multi-Task Learning (2017, background): “[196], [197], [198] study the generalization bound of MTL based on the VC dimension by assuming that data distributions of different tasks can be transformed via some known family of functions.”
Exploiting Task Relatedness for Mulitple Task Learning (2003, background): “[3] provide the following further results on dH∼F (n).”
Transitive Transfer Learning (2015, background): “Multi-task Learning algorithms simultaneously learn several tasks together and mutually enhance the classiﬁcation results of each task [2, 4, 5].”
A notion of task relatedness yielding provable multiple-task learning guarantees (2008, background): “Previously, ( Ben-David et al. 2002 ) considered the PAC setting, that is, the setting in which the learner is guaranteed that there exists a hypothesis h ∈ H that achieves zero error under the data generating distribution (in our case, P1).”
Multitask Learning without Label Correspondences (2010, background): “There is also work on data integration via multitask learning where each data source has the same binary label space, whereas the attributes of the inputs can admit different orderings as well as be linearly transformed [11].”
Learning Multiple Tasks with Kernel Methods (2005, background): “Lemma 2 also allows us to generalize multi-task learning to the case that each task function f has a different input domain X , a situation which is important in applications, see, for example, (Ben-David, Gehrke, and Schuller, 2002) for a discussion.”
Regularized multi--task learning (2004, background): “There have also been various attempts to theoretically study multi–task learning, see [4, 5, 6, 7, 8, 15, 23].”
A Framework for Probability Density Estimation (2007, background): “The question of learning a density with limited resources in such a way that the result will be useful for a range of potential application scenarios can also play an important role in sensor networks.”
Large Margin Multi-Task Metric Learning (2010, background): “This phenomenon has been examined further by recent papers which have started to build a theoretical foundation that underpins these initial empirical findings [1, 2, 3].”