Probably Approximately Correct Nash Equilibrium Learning

Probably Approximately Correct Learning

This paper surveys some recent theoretical results on the efficiency of machine learning algorithms. The main tool described is the notion of Probably Approximately Correct (PAC) 1 earning, introduced by Valiant. We define this learning model and then look at sorne of the results obtained in it. We then consider some criticisms of the PAC model and the extensions proposed to address these criti...

Probably Approximately Correct Learning

Learning quickly when irrelevant attributes abound: a new linear-threshold algorithm. of empirical and explanation-based learning algo

Refutably Probably Approximately Correct Learning

Probably Approximately Correct Search

We consider the problem of searching a document collection using a set of independent computers. That is, the computers do not cooperate with one another either (i) to acquire their local index of documents or (ii) during the retrieval of a document. During the acquisition phase, each computer is assumed to randomly sample a subset of the entire collection. During retrieval, the query is issued...

Probably Approximately Correct Greedy Maximization

Submodular function maximization finds application in a variety of real-world decision-making problems. However, most existing methods, based on greedy maximization, assume it is computationally feasible to evaluate F , the function being maximized. Unfortunately, in many realistic settings F is too expensive to evaluate exactly even once. We present probably approximately correct greedy maximi...