We present a very simple, randomized approximation algorithm for determining the number of cliques in a random graph. Supported in part by NSF Grant CCR-9505448 and a UC Berkeley Faculty Research Grant

We propose and investigate from the algorithmic standpoint a novel form of fuzzy query called approximately dominating representatives or ADRs. The ADRs of a multidimensional point set consist of a few points guaranteed to contain an approximate optimum of any monotone Lipschitz continuous combining function of the dimensions. ADRs can be computed by appropriately post-processing Pareto, or “sk...

Informative Bayesian priors are often difficult to elicit, and when this is the case, modelers usually turn to noninformative or objective priors. However, objective priors such as the Jeffreys and reference priors are not tractable to derive for many models of interest. We address this issue by proposing techniques for learning reference prior approximations: we select a parametric family and ...

Data as a commodity has always been purchased and sold. Recently, web services that are data marketplaces have emerged that match data buyers with data sellers. So far there are no guidelines how to price queries against a database. We consider the recently proposed query-based pricing framework of Koutris et al. [13] and ask the question of computing optimal input prices in this framework by f...

(www.aaai.org). All rights reserved. Creating strategies for different games forces us to grapple with different types of decision-making challenges. Poker is a stochastic game of imperfect information; unlike games of complete information, game-theoretic optimal strategies for poker can be randomized. Koller and Pfeffer [1] argue that two-player poker can be solved efficiently in the size of t...

To what extent is learnability impeded when information is missing in learning instances? We present relevant known results and concrete open problems, in the context of a natural extension of the PAC learning model that accounts for arbitrarily missing information.

Previous results on nonlearnability of visual concepts relied on the assumption that such concepts are represented as sets of pixels [l]. This correspondence uses an approach developed by Haussler [2] to show that under an alternative, feature-based representation, recognition is PAC learnable from a feasible number of examples in a distribution-free manner.

We introduce and study the notions of a PAC substructure of a stable structure, and a bounded substructure of an arbitrary substructure, generalizing [8]. We give precise definitions and equivalences, saying what it means for properties such as PAC to be first order, study some examples (such as differentially closed fields) in detail, relate the material to generic automorphisms, and generaliz...