Additive Gaussian processes

Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

Volumn , Issue , 2011, Pages

  Duvenaud, David ^a   Nickisch, Hannes ^b   Rasmussen, Carl Edward ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; FUNCTIONS; GAUSSIAN NOISE (ELECTRONIC);

ADDITIVE FUNCTION; BAYESIAN; DIMENSIONAL FUNCTIONS; EXPONENTIAL KERNELS; GAUSSIAN PROCESS MODELS; GAUSSIAN PROCESSES; GENERALIZED ADDITIVE MODEL; HYPER-PARAMETER; INPUT VARIABLES; LOW DIMENSIONAL;

GAUSSIAN DISTRIBUTION;

EID: 85162391976     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. J.A. Nelder and R.W.M. Wedderburn. Generalized linear models. Journal of the Royal Statistical Society. Series A (General), 135(3):370-384, 1972.
2. T.J. Hastie and R.J. Tibshirani. Generalized additive models. Chapman & Hall/CRC, 1990.
3. G. Wahba. Spline models for observational data. Society for Industrial Mathematics, 1990.
4. Francis Bach. High-dimensional non-linear variable selection through hierarchical kernel learning. CoRR, abs/0909.0844, 2009.
5. C.E. Rasmussen and CKI Williams. Gaussian Processes for Machine Learning. The MIT Press, Cambridge, MA, USA, 2006.
6. T.A. Plate. Accuracy versus interpretability in flexible modeling: Implementing a tradeoff using Gaussian process models. Behaviormetrika, 26:29-50, 1999.
7. I.G. Macdonald. Symmetric functions and Hall polynomials. Oxford University Press, USA, 1998.
8. R.P. Stanley. Enumerative combinatorics. Cambridge University Press, 2001.
9. C.G. Kaufman and S.R. Sain. Bayesian functional anova modeling using Gaussian process prior distributions. Bayesian Analysis, 5(1):123-150, 2010.
10. M. Christoudias, R. Urtasun, and T. Darrell. Bayesian localized multiple kernel learning. 2009.
11. V.N. Vapnik. Statistical learning theory, volume 2. Wiley New York, 1998.
12. M. Stitson, A. Gammerman, V. Vapnik, V. Vovk, C. Watkins, and J. Weston. Support vector regression with ANOVA decomposition kernels. Advances in kernel methods: Support vector learning, pages 285-292, 1999.
13. Y. Bengio, O. Delalleau, and N. Le Roux. The curse of highly variable functions for local kernel machines. Advances in neural information processing systems, 18, 2006.
14. J. Nocedal. Updating quasi-newton matrices with limited storage. Mathematics of computation, 35(151):773-782, 1980.
15. T.P. Minka. Expectation propagation for approximate Bayesian inference. In Uncertainty in Artificial Intelligence, volume 17, pages 362-369, 2001.