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Yeah. The hyper parameter story that comes with Gaussian processeses is a big drawback. The choice of kernel has a massive impact. In practice, I've found GPs
by siddboots 9y ago
Yeah. The hyper parameter story that comes with Gaussian processeses is a big drawback. The choice of kernel has a massive impact.
In practice, I've found GPs to be great for getting actual insight into an unknown function, but much less useful as a black-box learner.
- nonbel 9y agoI guess at its root the problem may just be how much compute is available to throw at the optimization. Alternatively there could be more efficient algos... I looked into but never fully tested this, it seemed promising: https://news.ycombinator.com/item?id=16241659 https://news.ycombinator.com/item?id=16241659
- elcritch 9y agoWhat kernels would you recommend trying initially? I’m still unclear if the Gaussian processes require normal distribution (e.g. would they work on log-log / binomial based functions). I’ve wanted to apply the approach you mention a few times, but documentation seems to go from “Wiki” level to novel research articles. Are there any good introductory books / resources that aren’t beginner level? That scikit library looks handy!
- kiliantics 8y agoGaussianprocess.org