The current approximation models use inducing points to reduce the number of data points. Do there exist approximation models specifically for multi-output models so that the number of output channels is more scalable (ie. doesn't go exponential with the number of output channels).
The current approximation models use inducing points to reduce the number of data points. Do there exist approximation models specifically for multi-output models so that the number of output channels is more scalable (ie. doesn't go exponential with the number of output channels).
One example is for the CONV kernel: https://proceedings.neurips.cc/paper_files/paper/2008/file/149e9677a5989fd342ae44213df68868-Paper.pdf