(This is a post-hoc writeup).

Apparently today I prepared a talk to give at NLP lunch. This was, I believe, a pretty busy week, as I gave 4 talks in total (one for Vannevar, one for NLP lunch, one for ML Reading Group, and one for 229T section). The NLP lunch talk was to about my research so far, and included a presentation on weighted KL divergence (there was also transformed KL divergence but I cut it from the talk), as well as an attempted definition of probabilistic abstractions in terms of piecewise uniform approximations, although I will later find out that this definition doesn’t work.

In the talk I go over abstract beam search (it’s actually relatively similar to the thing that I have right now for my write-up, minus the dynamic programming insight + punting on conditioning at the end of the day). I also didn’t yet have a way to subsample a Pi-system (that gets resolved over the next week and a half, though, at least for trees).


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