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WigglyHypersurface t1_iw5c2u1 wrote

Thankfully I'm doing niche enough projects I still get to. Last one was a multi-modal iwae for imputing missing data.

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MontanaBananaJCabana t1_iw5kxzt wrote

What's iwae?

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WigglyHypersurface t1_iw5lak8 wrote

Importance weighted autoencoder.

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schwagggg t1_iw5vivc wrote

were you able to use the measure valued derivative for poisson? you posted a thread couple months ago

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WigglyHypersurface t1_iw5xnxa wrote

It's possible I'll use it down the line, but it's not currently in the model.

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Used-Routine-4461 t1_iw7lr3v wrote

Was this for one feature or multiple features and if so did it require chained runs/predictions/equations? I’m currently using a MICE solution but have been looking for something better and this sounds interesting, any relevant papers or material you’d recommend?

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WigglyHypersurface t1_iw7qykq wrote

Search for MIWAE and notMIWAE to find the papers on the technique.

If your data is small and tabular than you can't really beat bayes. If your data is too big for bayes but just tabular than random forest imputation is pretty good. Or if you have specific hypotheses you know you will test you can do mice with SMCFCS.

The real utility of the (M)IWAE I think is when you have non-tabular data with missings. This is my use case. I have to impute a mixture of audio, string, and tabular data.

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