Bayesian Kernel Methods

Slides in Keynote and PDF.

Content

• Gaussians

  • Estimating a Normal Distribution
  • Conditioning
  • Principal Component Analysis

• Gaussian Processes

  • Correlation and inference
  • Gaussian Process Regression
  • Gaussian Process Classification

Supplementary material

• Girosi, 1998; An equivalence between sparse approximation and Support Vector Machines ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-1606.pdf
• Smola, Schoelkopf and Mueller, 1998; The connection between regularization operators and Support Vector Kernels http://alex.smola.org/teaching/berkeley2012/slides/Smola1998connection.pdf
• Schoelkopf, Smola, Mueller, 1998; Nonlinear Component Analysis as Kernel Eigenvalue Problem http://www.mitpressjournals.org/doi/abs/10.1162/089976698300017467
• Scholkopf, Smola, Herbrich, 2001; A Generalized Representer Theorem http://alex.smola.org/papers/2001/SchHerSmo01.pdf
• Teo, Globerson, Roweis, Smola, 2008; Convex Learning with Invariances https://proceedings.neurips.cc/paper/2007/file/013a006f03dbc5392effeb8f18fda755-Paper.pdf
• Rasmussen, 2006; Gaussian Processes for Machine Learning http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.86.3414

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