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

Article history: Received 8 July 2013 Received in revised form 25 May 2014 Accepted 30 May 2014 Available online 4 June 2014 Communicated by Amit Singer

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...

We show that auctions can generate near optimal total expected revenue by not using optimal reserve prices. Suppose we have n = 1 bidders in a posted-price, sealed-bid auction. This bidder has type v i drawn from distribution F. We can describe what the expected revenue is with a functionˆR : T → R: ˆ R(p) = p(1 − F(p)). (1) Figures 1 and 2 show expected revenue functions for the exponential di...

Unification with constants modulo the theory ACUI of an associative (A), commutative (C) and idempotent (I) binary function symbol with a unit (U) corresponds to solving a very simple type of set equations. It is well-known that solvability of systems of such equations can be decided in polynomial time by reducing it to satisfiability of propositional Horn formulae. Here we introduce a modified...

Given a string w and a pattern p, approximate pattern matching merges traditional sequence comparison and pattern matching by asking for the minimum diierence between w and a string exactly matched by p. We give an O(PN 2 (N + logP)) algorithm for approximately matching a string of length N and a context-free language speciied by a grammar of size P. The algorithm generalizes the Cocke-Younger-...

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...