Publications


Please refer to my Google Scholar profile for citation information.

Conference papers


  1. S Jiang, G Malkomes, G Converse, A Shofner, B Moseley, and R Garnett. Efficient nonmyopic active search. International Conference on Machine Learning (ICML 2017).

  2. G Malkomes, K Lu, B Hoffman, R Garnett, B Moseley, and R Mann. Cooperative Set Function Optimization Without Communication or Coordination. International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017).

  3. JR Gardner, C Guo, KQ Weinberger, R Garnett, and R Grosse. Discovering and Exploiting Additive Structure for Bayesian Optimization. International Conference on Artificial Intelligence and Statistics (AISTATS 2017). pdf

  4. Y Ma, R Garnett, and J Schneider. Active Search for Sparse Signals with Region Sensing. AAAI Conference on Artificial Intelligence (AAAI 2017). pdf

  5. D Oglic, R Garnett, and T Gärtner. Active Search in Intensionally Specified Structured Spaces. AAAI Conference on Artificial Intelligence (AAAI 2017). pdf

  6. G Malkomes, C Schaff, and R Garnett. Bayesian Optimization for Automated Model Selection. Conference on Neural Information Processing Systems (NIPS 2016). pdf

  7. SF Carr, R Garnett, and CS Lo. BASC: Applying Bayesian Optimization to the Search for Global Minima on Potential Energy Surfaces. International Conference on Machine Learning (ICML 2016). pdf

  8. JR Gardner, G Malkomes, R Garnett, KQ Weinberger, D Barbour, and JP Cunningham. Bayesian Active Model Selection with an Application to Automated Audiometry. Conference on Neural Information Processing Systems (NIPS 2015). pdf

  9. R Garnett, S Ho, and J Schneider. Finding Galaxies in the Shadows of Quasars with Gaussian Processes. International Conference on Machine Learning (ICML 2015). pdf

  10. MJ Kusner, JR Gardner, R Garnett, and KQ Weinberger. Differentially Private Bayesian Optimization. International Conference on Machine Learning (ICML 2015). pdf

  11. Y Ma, D Sutherland, R Garnett, and J Schneider. Active Pointillistic Pattern Search. International Conference on Artificial Intelligence and Statistics (AISTATS 2015). pdf

  12. T Gunter, MA Osborne, R Garnett, P Hennig, and SJ Roberts. Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature. Conference on Neural Information Processing Systems (NIPS 2014). pdf

  13. R Garnett, T Gärtner, T Ellersiek, E Guðmondsson, and P Óskarsson. Predicting Unexpected Influxes of Players in EVE Online. IEEE Conference on Computational Intelligence and Games (CIG 2014). pdf

  14. R Garnett, MA Osborne, and P Hennig. Active Learning of Linear Embeddings for Gaussian Processes. Conference on Uncertainty in Artificial Intelligence (UAI 2014). pdf

  15. K Kersting, M Mladenov, R Garnett, and M Grohe. Power Iterated Color Refinement. AAAI Conference on Artificial Intelligence (AAAI 2014). pdf

  16. Y Ma, R. Garnett, and J Schneider. Active Area Search via Bayesian Quadrature. International Conference on Artificial Intelligence and Statistics (AISTATS 2014). pdf

  17. Y Ma, R Garnett, and J Schneider. Σ-Optimality for Active Learning on Gaussian Random Fields. Conference on Neural Information Processing Systems (NIPS 2013). pdf

  18. M Neumann, R Garnett, and K Kersting. Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning With Graphs and Few Labels. Asian Conference on Machine Learning (ACML 2013). pdf

  19. X Wang, R Garnett, and J Schneider. Active Search on Graphs. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2013). pdf

  20. MA Osborne, D Duvenaud, R Garnett, CE Rasmussen, SJ Roberts, and Z Ghahramani. Active Learning of Model Evidence Using Bayesian Quadrature. Conference on Neural Information Processing Systems (NIPS 2012). pdf

  21. M Neumann, N Patricia R Garnett, and K Kersting. Efficient Graph Kernels by Randomization. European Conference on Machine Learning (ECML 2012). pdf

  22. R Garnett, Y Krishnamurthy, X Xiong, J Schneider, and RP Mann. Bayesian Optimal Active Search and Surveying. International Conference on Machine Learning (ICML 2012). pdf

  23. MA Osborne, R Garnett, K Swersky, and N de Freitas. Prediction and Fault Detection of Environmental Signals with Uncharacterized Faults. AAAI Conference on Artificial Intelligence (AAAI 2012). pdf

  24. MA Osborne, R Garnett, SJ Roberts, C Hart, S Aigrain, and N Gibson. Bayesian Quadrature for Ratios. International Conference on Artificial Intelligence and Statistics (AISTATS 2012). pdf

  25. MA Osborne, R Garnett, and SJ Roberts. Active Data Selection for Sensor Networks with Faults and Changepoints. IEEE International Conference on Information Networking and Applications (AINA 2010). pdf

  26. R Garnett, MA Osborne, and SJ Roberts. Bayesian Optimization for Sensor Set Selection. ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2010). pdf

  27. R Garnett, MA Osborne, and SJ Roberts. Sequential Bayesian Prediction in the Presence of Changepoints. International Conference on Machine Learning (ICML 2009). pdf

  28. MA Osborne, R Garnett, and SJ Roberts. Gaussian Processes for Global Optimization. International Conference on Learning and Intelligent Optimization (LION 2009). pdf

Journal articles


  1. X Song, R Garnett, and D Barbour. (2017). Psychometric function estimation by probabilistic classification. Journal of the Acoustical Society of America, 141(4): 2513–2525. pdf doi

  2. S Bird, R Garnett, and S Ho. (2016). Statistical properties of damped Lyman-alpha systems from Sloan Digital Sky Survey DR12. Monthly Notices of the Royal Astronomical Society, 466(2): 2111–2122. pdf doi

  3. SF Carr, R Garnett, and CS Lo. (2016). Accelerating the Search for Global Minima on Potential Energy Surfaces using Machine Learning. Journal of Chemical Physics, 145(15): 154106. pdf doi

  4. M Neumann, R Garnett, K Kersting, and C Bauckhage. (2016). Propagation Kernels: Efficient Graph Kernels from Propagated Information. Machine Learning, 102(2): 209–245. pdf doi

  5. RP Mann and R Garnett. (2015). The Entropic Basis of Collective Behaviour. Journal of the Royal Society Interface, 12(106): 20150037. pdf doi

  6. R Garnett, T Gärtner, M Vogt, and J Bajorath. (2015). Introducing the ‘Active Search’ Method for Iterative Virtual Screening. Journal of Computer-Aided Molecular Design, 29(4): 305–314. pdf doi

  7. RP Mann, A Perna, D Strömbom, R Garnett, JE Herbert–Read, DJT Sumpter, and AJW Ward. (2012). Multi-scale Inference on Interaction Rules in Animal Groups using Bayesian Model Selection. PLoS Computational Biology, 9(3): e1002961. pdf doi

  8. RP Mann, R Freeman, MA Osborne, R Garnett, C Armstrong, J Meade, D Biro, T Guilford, and SJ Roberts. (2011). Objectively Identifying Landmark Use and Predicting Flight Trajectories of the Homing Pigeon using Gaussian Processes. Journal of the Royal Society Interface, 8(55): 210–219. pdf doi

  9. D Lowne, R Garnett, and SJ Roberts. (2010). Sequential Nonstationary Dynamic Classification with Sparse Feedback. Pattern Recognition, 43(3): 897–905. pdf doi

  10. R Garnett, MA Osborne, S Reece, A Rogers, and SJ Roberts. (2010). Sequential Bayesian Prediction in the Presence of Changepoints and Faults. The Computer Journal, 43(9): 1430–1446. pdf doi

  11. R Garnett, T Heugerich, W He, and C Chui. (2005). A Universal Noise Removal Algorithm with an Impulse Detector. IEEEE Transactions on Image Processing, 14(11): 1747–1754. pdf doi

Preprints


  1. R Garnett, S Ho, S Bird, and J Schneider. Detecting Damped Lyman-α Absorbers with Gaussian Processes. arXiv preprint arXiv:1605.04460 [astro-ph.CO]

Thesis


  1. R Garnett. Learning from Data Streams with Concept Drift. DPhil thesis, University of Oxford, 2010. pdf