Non-Compact Domain/Range Machine Learning
Notice that the limitation of finite domains and ranges stems from the finite nature of hardware and thus finite representation of numbers. Therefore, a more promising approach is remodeling the domain, rather than using an "infinite" algorithm.

You can apply logarithmic scaling to your domain and range. This will get you off from the open bounds while still preserving order and continuity, which is important fur numerical ML algorithms.

The log function is the basic building block that you can use for your transformation. You can scale it to fit your ranges and this would solve your positive part of the range. You can do the same for the negative, even using  different coefficients if this suits you.