Learning to Search Better Than Your Teacher · HackerPapers discussions

Learning to Search Better Than Your Teacher — mentioned by supercarrot

Wed, 15 Jun 2016 21:22:26 GMT

> IMO RNNs do need some kind of structured loss (more than per step likelihood) to be competitive with HMM approaches using Viterbi decoding This is exactly what they do with the dependency parser I've cited, so your opinion is definitely valid. Although their approach is not general, given the fact that they approximate hamming loss with log-loss and again make it work only on sequences. http://arxiv.org/abs/1502.02206 paper above also has a very good analysis on how to remove the search component of the inference and allow linear time complexity with competitive results. consistent (as it…

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Learning to Search Better Than Your Teacher — mentioned by vagabondvector

Mon, 16 May 2016 19:38:35 GMT

http://arxiv.org/abs/1603.06042 this is the paper. the idea is based on the incredible incremental perceptron coupled with beam search for having different hypotheses at the same time trying to find a policy which will build them and select the best one http://dl.acm.org/citation.cfm?id=1218970 The approach to structured prediction that was the fastest at the time was that incremental perceptron and the implementation extremely simple. Some other approaches includes conditional random fields, hidden markov models, maximum margin markov networks, maximum entropy markov model and the approach…

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Learning to Search Better Than Your Teacher — mentioned by kotach

Thu, 17 Mar 2016 13:08:14 GMT

Checkout Dagger [2], SEARN [3] and LOLS [1] (LOLS is available in vowpal wabbit search capabilities). A lot of interesting stuff on mimicking optimal policies, local optimality, joint learning and similar stuff :D The whole point of playing is doing your decisions jointly, dependent on the previous decisions. If you learn your model that way it'll make its decisions trying to minimize future regret. Local optimality is a very nice property. It means that if you play out a game, not a single change of any of the previous moves could lead you to a better result. Of course, local optimality is…

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Learning to Search Better Than Your Teacher — mentioned by kotach

Sun, 13 Mar 2016 09:15:42 GMT

What you are talking about here is called "label bias". [2] It is present only if training is done badly. When you have a game of Go, or Super Mario level. You don't want to make your decisions by just checking the local features and doing them, because it can be the case that by compounding errors you end up in a state you never saw, and all of the future decisions won't be good. One can avoid these situations by training jointly over the whole game. For example, maximum entropy models can work for decision making problems but their training leaves them in a "label bias" state because the…

Paper and discussion history. A mention is not an endorsement.