GP Bibliography

A list of Gaussian process publications, with an emphasis on the machine learning literature sorted by topics and keywords. There is a landing page for the various topics with a brief description and subtopics as well as review papers.

A .bib file corresponding to the references in this bibliography is provided for download.

Please contact me via email with any comments or suggestions (especially if you spot typos in the .bib file!).

Top level topics

  • Scalability
    • Low-rank approximation
    • Iterative methods
  • Kernel design
    • Additive modelling
    • Non-Euclidean domains
  • Model Selection
    • Maximum marginal likelihood (theory)
    • Maximum marginal likelihood (practice)
    • Cross validation
  • Non-conjugate likelihoods
    • Markov Chain Monte Carlo
    • Variational Inference
    • Laplace Approximation
    • Expectation Propagation
  • Climate
  • Robotics
  • Bandits and Bayesian Optimization

A bit more about this bibliography

These topics should have something like the structure of a rooted tree, where the root is “Gaussian processes” and the leaves are individual papers, although since some papers inevitably contain several topics, the structure is perhaps more of a directed acyclic graph. My hope is this will be a useful resource for those researching Gaussian process methods in finding related work, as well as for those looking to apply Gaussian processes to real-world problems. This list will of course be non-exhaustive, and is in the process of being expanded. Eventually, I hope to provide summaries of many of the included papers as well as how they relate to other papers in the literature, though this will likely take some time. Inevitably, the topics and papers contained in this bibliography will reflect my own biases and knowledge of the literature, and so for the moment there will be more about scalable approximate Gaussian process regression than other topics, though I hope to improve the balance of the topics moving forward.