Machine Learning
Regression with Gaussian processes
MANNA '95 Proceedings of the first international conference on Mathematics of neural networks : models, algorithms and applications: models, algorithms and applications
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Sparse on-line Gaussian processes
Neural Computation
Gaussian Processes for Classification: Mean-Field Algorithms
Neural Computation
Expectation Consistent Approximate Inference
The Journal of Machine Learning Research
| Hi-index | 0.00 |
Using a novel reformulation, we develop a framework to compute approximate resampling data averages analytically. The method avoids multiple retraining of statistical models on the samples. Our approach uses a combination of the replica "trick" of statistical physics and the TAP approach for approximate Bayesian inference. We demonstrate our approach on regression with Gaussian processes. A comparison with averages obtained by Monte-Carlo sampling shows that our method achieves good accuracy.