Someday I'll Be Free

Theory: It looks like this proof is going to be a classic Horrible Analysis Proof. Fuck.

Theory: I have one major element of the proof left: showing that if two probability distributions are a certain distance apart, they must have [desired property it’s complicated]. I accidentally already showed the reverse direction (for distributions with bounded support, at least), because I confused myself.

Applied: Hmm. I still need to implement a gumbel softmax VAE, where by “implement” I mean “copy from github and get it to run”. I implemented a trivial environment and I’m not really seeing learning there, so clearly something is going wrong in the training alg.

... and now I’ve done most of the direct proof, realized the general version, proved that and rewritten my proofs using the general thing.

Math: I’m closing in on the final result, very excited about it. I need to convert an existing result to my paradigm, prove an additional novel result, and then I think I should have everything modulo some cleanup

Theory: There’s one or two major steps left in the proof. One of them should be easy, I’m just modifying an existing proof, but no one has written that proof down so instead I’m looking at a much more complicated proof and trying to adapt that.

Applied: Well, I am at least safe from being scooped because absolutely everyone else who’s looked at this problem has failed. Working with [redacted] is helpful, she’s much better at neural net engineering than I am

Theory: Still haven’t figured out the blocked part of the proof. Specifically there’s a claim that a particular estimator produces a hypothesis test for conditional independence with particular power, but I honestly can’t see it (it’s certainly in the realm, you’re estimating a thing that is 0 if conditional independence holds, but it’s a continuous variable so I don’t see how we can get non-zero measure on it actually being zero)

Code: Doing some devops, mostly have things set up nicely. Still need to clean the actual code up and refactor it to fit into my overall framework.

Learning: I’m going to start recording some of the things I’m focusing on learning. Mostly this is stuff that sits in between direct research relevance (which would go under theory) and just for fun (which I might post about anyway)

Overall I’m v happy with progress on theory and code

Theory: Have most of the main proof done. Need to send an email to [the scary prof] with a question about his field. Need to write up the last bit of the proof for the noise terms. Need to formalize everything.

Code: I picked up a collaborator (my girlfriend who wants to publish RL papers). She’s way more motivated about writing ML code than I am, which is extremely helpful because motivation is my biggest block by far. Also helpful because it forces me to be more organized since now I have another coder to direct. We’re working through some reimplementations of relevant work. Also she has a 3090* which is extremely useful for experiments.

Theory: Need to prove bounds on the error terms