On Learning Mixture of Linear Regressions in the Non-Realizable Setting. (arXiv:2205.13166v1 [stat.ML])

While mixture of linear regressions (MLR) is a well-studied topic, prior
works usually do not analyze such models for prediction error. In fact, {\em
prediction} and {\em loss} are not well-defined in the context of mixtures. In
this paper, first we show that MLR can be used for prediction where instead of
predicting a label, the model predicts a list of values (also known as {\em
list-decoding}). The list size is equal to the number of components in the
mixture, …

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