The ME Algorithm for Maximizinga Conditional Likelihood Fun tion

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Abstra t: Au tion me hanism design has traditionally been a largely analyti pro ess, relying on assumptions su h as fully rational bidders. In pra ti e, however, bidders behave unpredi tably, making them dif ult to model and ompli ating the design pro ess. To address this hallenge, we present an adaptive au tion me hanism: one that learns to adjust its parameters in response to past empiri al b...

The Generalized CEM Algorithm

We propose a general approach for estimating the parameters of latent variable probability models to maximize conditional likelihood and discriminant criteria. Unlike joint likelihood, these objectives are better suited for classiication and regression. The approach utilizes and extends the previously introduced CEM framework (Conditional Expectation Maximization), which reformulates EM to hand...

lambda-RBAC: Programming with Role-Based Access Control

b Bell Labs e-mail address: aje rey bell-labs. om Abstra t. We study me hanisms that permit program omponents to express role onstraints on lients, fo using on programmati se urity me hanisms, whi h permit a ess ontrols to be expressed, in situ, as part of the ode realizing basi fun tionality. In this setting, two questions immediately arise. (1) The user of a omponent fa es the issue of safety...

Conditional Maximum Likelihood Estimation of the First-Order Spatial Integer-Valued Autoregressive (SINAR(1,1)) Model

‎Recently a first-order Spatial Integer-valued Autoregressive‎ ‎SINAR(1,1) model was introduced to model spatial data that comes‎ ‎in counts citep{ghodsi2012}‎. ‎Some properties of this model‎ ‎have been established and the Yule-Walker estimator has been‎ ‎proposed for this model‎. ‎In this paper‎, ‎we introduce the...

Non-stationary Fun tion Optimization using Evolutionary Algorithms with a Case-based Memory