Collaborative information retrieval involves retrieval settings in which a group of users collaborates to satisfy the same underlying need. One core issue of collaborative IR models involves either supporting collaboration with adapted tools or developing IR models for a multiple-user context and providing a ranked list of documents adapted for each collaborator. In this paper, we introduce the...

We describe a novel training criterion for probabilistic grammar induction models, contrastive estimation [Smith and Eisner, 2005], which can be interpreted as exploiting implicit negative evidence and includes a wide class of likelihood-based objective functions. This criterion is a generalization of the function maximized by the ExpectationMaximization algorithm [Dempster et al., 1977]. CE is...

Computingmaximum a posteriori (MAP) estimation in graphical models is an important inference problem with many applications. We present message-passing algorithms for quadratic programming (QP) formulations of MAP estimation for pairwise Markov random fields. In particular, we use the concaveconvex procedure (CCCP) to obtain a locally optimal algorithm for the non-convex QP formulation. A simil...

A latent variable generative model with finite noise is used to describe several different algorithms for Independent Components Analysis (ICA). In particular, the Fixed Point ICA algorithm is shown to be equivalent to the ExpectationMaximization algorithm for maximum likelihood under certain constraints, allowing the conditions for global convergence to be elucidated. The algorithms can also b...

With the success of modern internet based platform, such as Amazon Mechanical Turk, it is now normal to collect a large number of hand labeled samples from non-experts. The DawidSkene algorithm, which is based on ExpectationMaximization update, has been widely used for inferring the true labels from noisy crowdsourced labels. However, Dawid-Skene scheme requires all the data to perform each EM ...

The ability to delineate geologic facies and to estimate their properties from sparse data is essential for modeling physical and biochemical processes occurring in the subsurface. If such data are poorly differentiated, this challenging task is complicated further by the absence of a clear distinction between different hydrofacies at locations where data are available. We consider three altern...

Relevance judgments are often the most expensive part of information retrieval evaluation, and techniques for comparing retrieval systems using fewer relevance judgments have received significant attention in recent years. This paper proposes a novel system comparison method using an expectationmaximization algorithm. In the expectation step, real-valued pseudo-judgments are estimated from a se...

Intrusion detection in the internet is an active area of research. Intruders can be classified into two types, namely; external intruders who are unauthorized users of the computers they attack, and internal intruders, who have permission to access the system but with some restrictions. The aim of this paper is to present a methodology to recognize attacks during the normal activities in a syst...

A low-complexity recursive procedure is presented for model selection and minimum mean squared error (MMSE) estimation in linear regression. Emphasis is given to the case of a sparse parameter vector and fewer observations than unknown parameters. A Gaussian mixture is chosen as the prior on the unknown parameter vector. The algorithm returns both a set of high posterior probability models and ...

In this paper, we deal with the so-called Markovian Arrival process (MAP). An MAP is thought of as a partially observed Markov process, so that the ExpectationMaximization (EM) algorithm is a natural way to estimate its parameters. Then, non-linear filters of basic statistics related to the MAP must be computed. The forward-backward principle is the basic way to do it. Here, bearing in mind a f...