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Single label has been the applicable one for most of the applications we've tackled to date, but agreed that multi-label is also very important! More coming here soon...

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Thanks Alex. I'm sure you can relate that when you have a an unbounded input distribution (like w/ user-interaction systems), defining that other class w/ current snorkel is difficult/impossible.

Yeah definitely- and would love to chat sometime, as this is a space I've at least had less direct hands-on interaction with. There's a line of work in the ML literature on "Positive unlabeled (PU) learning"--basically, setting where there are only positive labels or abstains--with a lot of theoretical ties to what our stuff rests on, I think a tie in here is interesting. Of course, most of these approaches rely on some (to varying degrees) hidden and very strong distributional assumption... anyway looking forward to a chat!

Thanks for the lead on PU Learning.

I signed up for a demo of the new platform, looking forward to chatting. Me and a colleague from work spoke w/ Henry last year about a potential partnership but I guess it got lost in the mix...

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