We consider risk minimization problems for Markov decision processes. From a standpoint of making the risk of random reward variable at each time as small as possible, a risk measure is introduced using conditional value-at-risk for random immediate reward variables in Markov decision processes, under whose risk measure criteria the risk-optimal policies are characterized by the optimality equa...

The computational complexity of linearchain Conditional Random Fields (CRFs) makes it difficult to deal with very large label sets and long range dependencies. Such situations are not rare and arise when dealing with morphologically rich languages or joint labelling tasks. We extend here recent proposals to consider variable order CRFs. Using an effective finitestate representation of variable-...

This paper presents a novel sequence labeling model based on the latent-variable semiMarkov conditional random fields for jointly extracting argument roles of events from texts. The model takes in coarse mention and type information and predicts argument roles for a given event template. This paper addresses the event extraction problem in a primarily unsupervised setting, where no labeled trai...

This paper specifies a new deep architecture, called Recurrent Temporal Deep Field (RTDF), for semantic video labeling. RTDF is a conditional random field (CRF) that combines a deconvolution neural network (DeconvNet) and a recurrent temporal restricted Boltzmann machine (RTRBM). DeconvNet is grounded onto pixels of a new frame for estimating the unary potential of the CRF. RTRBM estimates a hi...

In this paper we present a discriminative framework based on conditional random fields for stochastic modeling of images in a hierarchical fashion. The main advantage of the proposed framework is its ability to incorporate a rich set of interactions among the image sites. We achieve this by inducing a hierarchy of hidden variables over the given label field. The proposed tree like structure of ...

In this paper we propose a unified framework for structured prediction with latent variables which includes hidden conditional random fields and latent structured support vector machines as special cases. We describe a local entropy approximation for this general formulation using duality, and derive an efficient message passing algorithm that is guaranteed to converge. We demonstrate its effec...

As it is of great importance to extract useful information from heterogeneous Web data, in this paper, we propose a novel heterogeneous Web data extraction algorithm using a modified hidden conditional random fields model. Considering the traditional linear chain based conditional random fields can not effectively solve the problem of complex and heterogeneous Web data extraction, we modify the...

Abstract. In this paper, we obtain some stable Poisson Convergence Theorems for arrays of integer-valued dependent random variables. We prove that the limiting distribution is a mixture of Poisson distribution when the conditional second moments on a given σ-algebra of the sequence converge to some positive random variable. Moreover, we apply the main results to the indicator functions of rowis...

As was noted already by A. N. Kolmogorov, any random variable has a Bernoulli component. This observation provides a tool for the extension of results which are known for Bernoulli random variables to arbitrary distributions. Two applications are provided here: i. an anti-concentration bound for a class of functions of independent random variables, where probabilistic bounds are extracted from ...