A description of perturbation based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees in nearly all machine learning decisions must be made given current knowledge surprisingly making what is believed to be the best decision is not always the best strategy even when learning in a supervised learning setting. Perturbations in machine learning snapshot of current state of the art nips workshops on perturbations optimization and statistics in 2012 2013 and 2014 perturbations optimization and statistics tamir hazan george papandreou and daniel tarlow eds mit press 2016 8 32. Perturbations optimization and statistics editors tamir hazan tamirhazantechnionacil technion israel institute of technology technion city haifa 32000 israel george papandreou gpapangooglecom google inc 340 main st los angeles ca 90291 usa daniel tarlow dtarlowmicrosoftcom microsoft research cambridge cb1 2fb united kingdom. Perturbation optimization and statistics editors tamir hazan tamirhazantechnionacil technion israel institute of technology sensitivity to small perturbations has been found to exist not only in neural networks but also in more traditional machine learning methods such as. On the expected value of random maximum a posteriori perturbations tamir hazan tommi jaakkola 7 a poisson process model for monte carlo chris j maddison 8 perturbation techniques in online learning and optimization jacob abernethy chansoo lee ambuj tewari 9 probabilistic inference by hashing and optimization stefano ermon 10
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