Related papers: Random DFAs are Efficiently PAC Learnable

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This paper has been withdrawn by the author, due an error in the proof of Proposion 2.13.

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This paper has been withdrawn by the author due to essential mistakes in some previous versions.

This paper has been withdrawn by the authors due to an error first noted by M. Lukin.

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We study the problem of PAC learning halfspaces in the reliable agnostic model of Kalai et al. (2012). The reliable PAC model captures learning scenarios where one type of error is costlier than the others. Our main positive result is a new…

This paper offers a new hybrid probably approximately correct (PAC) reinforcement learning (RL) algorithm for Markov decision processes (MDPs) that intelligently maintains favorable features of its parents. The designed algorithm, referred…

This paper has been withdrawn by the author because overcame by arXiv:0910.4694

This paper has been withdrawn by the authors due to an error.

This paper has been withdrawn by the author due to a crucial error.

This paper has been withdrawn by the authors.

This paper has been withdrawn by the author due to a crucial sign error.

This paper has been withdrawn.

This paper has been withdrawn by the author due a crucial error.

This paper has been withdrawn as we discovered a bug in our tensorflow implementation that involved accidental mixing of vectors across batches. This lead to different inference results given different batch sizes which is completely…