In many real-world auctions, a bidder does not know her exact value for an item, but can perform costly deliberation to reduce uncertainty. Relatively little is known about such deliberative environments, which are fundamentally different from classical auction environments. this paper, we propose new approach that allows us leverage revenue-maximization results in particular, use Myerson (1981...

We survey the fastest known algorithms for learning various expressive classes of Boolean functions in the Probably Approximately Correct (PAC) learning model.

We consider PAC learning of probability distributions (a.k.a. density estimation), where we are given an i.i.d. sample generated from an unknown target distribution, and want to output a distribution that is close to the target in total variation distance. Let F be an arbitrary class of probability distributions, and let F denote the class of k-mixtures of elements of F . Assuming the existence...

This paper applies the theory of Probably Approximately Correct (PAC) learning to multiple output feedforward threshold networks in which the weights conform to certain equivalences. It is shown that the sample size for reliable learning can be bounded above by a formula similar to that required for single output networks with no equivalences. The best previously obtained bounds are improved fo...

A satisscing search problem consists of a set of probabilistic experiments to be performed in some order, seeking a satisfying connguration of successes and failures. The expected cost of the search depends both on the success probabilities of the individual experiments, and on the search strategy, which speciies the order in which the experiments are to be performed. A strategy that minimizes ...

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...

To facilitate the testing of models for the evolution of languages, the present note offers a set of linguistic features that are approximately independent of each other. To find these features, the adjusted Rand index (R) is used to estimate the degree of pairwise relationship among 130 linguistic features in a large published database. Many of the R values prove to be near 0, as predicted for...

A* is a best-first search algorithm that returns an optimal solution. w-admissible algorithms guarantee that the returned solution is no larger than w times the optimal solution. In this paper we introduce a generalization of the w-admissibility concept that we call PAC search, which is inspired by the PAC learning framework in Machine Learning. The task of a PAC search algorithm is to find a s...

We study cost-sharing mechanisms for several fundamental NP-hard combinatorial optimization problems. A cost-sharing mechanism is a protocol that, given bids for a service, determines which bidders to serve and what prices to charge. The mechanism incurs a subset-dependent cost that is implicitly defined by an instance of a combinatorial optimization problem. Three desirable but mutually incomp...

In the recent development ofwrious models of learning inspired by the PAC learning model (introduced by Valiant) there has been a trend towards models which are as representation independent as possible: We review this development and discuss the advantages of representation independence. Motivated by the research in learning, we propose a framework for studying the combinatorial properties of ...