Reference papers

A non-exhausted list of papers related to operator-valued kernels. Any dead links? Open a new issue here.

• Reviews:

  • Wikipedia entry.
  • Kernels for Vector-Valued Functions: a Review, Alvarez et al. 2011

• On operator-valued kernels:

  • On learning vector-valued function, Micchelli et al. 2005.
  • Kernels for Vector-Valued Functions: a Review, Alvarez et al. 2011
  • Vector valued reproducing kernel Hilbert spaces of integrable functions and Mercer theorem, Carmeli et al., 2006.

• Universal operator-valued kernels:

  • Vector valued reproducing kernel Hilbert spaces and universality, Carmeli et al., 2008
  • Universal Kernels for Multi-Task learning, Caponnetto et al., 2006.

• Classification:

  • Multiclass Learning with Simplex Coding, Youssef Mroueh et al., 2012.

• Vector-field learning:

  • Modeling magnetic fields using Gaussian processes, Wahlstrom et al., 2013.
  • Learning divergence-free and curl-free vector fields with matrix-valued kernels, Macedo et al., 2010.

• Structured output prediction, link prediction:

  • Input Output Kernel Regression, Brouard et al., 2015.
  • A Generalized Kernel Approach to Structured Output Learning, Kadri et al., 2015.

• Multiple output kernels:

  • Multiple Operator-valued Kernel Learning, Kadri et al., 2011.

• Multiview/multitask learning:

  • Learning Multiple Tasks with Kernel Methods, Evgeniou et al., 2005.
  • A Unifying Framework for Vector-valued Manifold Regularization and Multi-view Learning, Quang Minh et al., 2013.

• Functional data:

  • Operator-valued Kernels for Learning from Functional Response Data, Kadri et al., 2015.
  • Nonlinear functional regression: a functional RKHS approach, Kadri et al., 2010.
  • Operator-valued kernel recursive least squares algoritm, Amblard et al., 2015.

• Learning kernels:

  • Learning low-rank output kernels, Dinuzzo et al., 2011.
  • Learning Output Kernels with Block Coordinate Descent, Dinuzzo et al., 2011.
  • Operator-valued Kernel-based Vector Autoregressive Models for Network Inference, Lim et al., 2014.

• Refinement of kernels:

  • Refinement of Operator-valued Reproducing Kernels, Zhang et al., 2012.

• Differential equations:

  • Learning nonparametric differential equations with operator-valued kernels and gradient matching, Heinonen et al., 2014.

• Large scale-learning with operator-valued kernels:

  • Online Learning with Multiple Operator-valued Kernels, Audiffren et al., 2013.