The Rademacher Complexity of Linear Transformation Classes

By Andreas Maurer. 2006

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In Statistical Learning Theory

Citations in the corpus

An Overview of Multi-task Learning (2018, neutral): “Thenthereare manystudiestoanalyzegeneralizationboundsofdif-ferentMTLapproaches,includinge.g. [7,134]for thefeaturetransformapproach,[135]forthefea-tureselectionapproach,[24,135–138]forthelow-rankapproach,[136]forthetask-relationlearning approach,and[138]forthedirtyapproach.”
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs (2019, methodology): “This principle, known as graph regularization, is well-established in the multi-task learning literature (Evgeniou and Pontil, 2004; Maurer, 2006; Dhillon et al., 2011).”
A Survey on Multi-Task Learning (2017, methodology): “In [222], Maurer analyzes the generalization bound of a MTL method based on the Rademacher complexity, which learns a common feature transformation for all the tasks and is a special case of problem (11) without U, and then in [223] further analyzes generalization bounds of regularized MTL models ba”
Personalized and Private Peer-to-Peer Machine Learning (2017, methodology): “Following a well-established principle in the multi-task learning literature (Evgeniou and Pontil, 2004; Maurer, 2006; Dhillon et al., 2011), we use graph regularization to favor models that vary smoothly on the graph.”
Excess risk bounds for multitask learning with trace norm regularization (2012, methodology): “In [22] the general form of the bound is very similar to Theorem 1.”
Towards a Learning Theory of Cause-Effect Inference (2015, background): “For a particular examples of classes of functionals with small Rademacher complexity we refer to Maurer (2006).”
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning (2016, methodology): “In what follows, we demonstrate the power of Theorem 8 by applying it to derive the LRC bounds for some popular MTL models, including group norm, Schatten norm and graph regularized MTL models extensively studied in the literature of MTL [46, 23, 6, 4, 38, 3].”
Convex multi-task feature learning (2008, background): “Results in [12, 25 ] may be useful for this purpose.”
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality (2012, background): “The notion of Rademacher complexity is readily generalizable to vector-valued hypothesis spaces [23].”
From Dependence to Causation (2016, background): “One natural example of such a class is the set of linear functionals with uniformly bounded operator norm (Maurer, 2006).”