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| author | Yuchen Pei <me@ypei.me> | 2019-01-27 19:30:27 +0100 |
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| committer | Yuchen Pei <me@ypei.me> | 2019-01-27 19:30:27 +0100 |
| commit | 3dcc6d779e7e03d060548f16b3c96fe90a9cef88 (patch) | |
| tree | e6a4404b76e8cc3d678a8c5c30b1ee6a5b957fc9 /microposts | |
| parent | 6cf0d12eafcb7432db80656dfb60dc009bb2b00d (diff) | |
minor
Diffstat (limited to 'microposts')
| -rw-r--r-- | microposts/learning-undecidable.md | 2 |
1 files changed, 2 insertions, 0 deletions
diff --git a/microposts/learning-undecidable.md b/microposts/learning-undecidable.md index 4507b0d..34e5e27 100644 --- a/microposts/learning-undecidable.md +++ b/microposts/learning-undecidable.md @@ -25,3 +25,5 @@ The undecidability rests on X being the continuous [0, 1] interval, and from the In the ad problem, the domain X is all potential visitors, which is finite because there are finite number of people in the world. In this case P is a categorical distribution over the 1..n where n is the population of the world. One can have a good estimate of the parameters of a categorical distribution by asking for sufficiently large number of samples and computing the empirical distribution. Let's call the estimated distribution Q. One can choose the from F (also finite) the set that maximises Q(A) which will be a solution to EMX. In other words, the theorem states: EMX is undecidable because not all EMX instances are decidable, because there are some nasty ones due to infinities. That does not mean no EMX instance is decidable. And I think the ad instance is decidable. Is there a learning task that actually corresponds to an undecidable EMX instance? I don't know, but I will not believe the result of this paper is useful until I see one. + +h/t Reynaldo Boulogne |