Multi-task ML

July 13, 2021 — July 13, 2021

edge computing
machine learning
neural nets
regression
sparser than thou
spatial
stochastic processes
time series

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Figure 1

On training a model which predicts several things at once.

This is a very ML way of phrasing things. In classical statistics, if we have fit some multivariate regressions by a likelihood-based procedure, then it produces multivariate output. No problem. However, in machine learning, we frequently fit based on univariate predictive loss, and it is not clear, or at least affordable, to translate these univariate predictions to multivariate ones without starting over and simply training lots of univariate prediction models. In that context, it is not foolish to ask about multivariate predictions and think of them as the task of developing a “multi-task model” as some kind of new thing.

1 Weighted sum of multiple objectives

See multi-objective optimization.

2 Multi-task GPs

It is fairly natural to make a Gaussian process into a multivariate method; See Vector GP regression.

3 Incoming

4 References

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