A “lifting” of a Markov chain is a larger chain obtained by replacing each state of the original chain by a set of states, with transition probabilities defined in such a way that the lifted chain projects down exactly to the original one. It is well known that lifting can potentially speed up the mixing time substantially. Essentially all known examples of efficiently implementable liftings ha...

The predictive coding of video typically entails a motion estimation and compensation step followed by lossy encoding of the residual between the original and predicted images. While this strategy has been shown to be effective for moderate to high rate video compression applications (e.g., the MPEG-1 video coding standard [1]), it neglects the fact that significant correlation may exist betwee...

Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This paper proposes to use the convolution kernel over parse trees together with support vector machines to model syntactic structured information for relation extraction. Compared with linear kernels, tree kernels can effe...

An important problem in fault diagnosis is how to locate faulty components by analysing performance measurements from those components. In this paper, we present an algorithm that uses the informationtheoretic Minimum Message Length (MML) principle to locate faults in tree-structured networks. Treestructured networks are an important application for fault diagnosis due to their use as distribut...

Background subtraction is a fundamental video analysis technique that consists of creation of a background model that allows distinguishing foreground pixels. We present a new method in which the image sequence is assumed to be made up of the sum of a low-rank background matrix and a dynamic tree-structured sparse matrix. The decomposition task is then solved using our approximated Robust Princ...

Bayesian optimization has been successfully used to optimize complex black-box functions whose evaluations are expensive. In many applications, like in deep learning and predictive analytics, the optimization domain is itself complex and structured. In this work, we focus on use cases where this domain exhibits a known dependency structure. The benefit of leveraging this structure is twofold: w...

Monadic query languages over trees currently receive considerable interest in the database community, as the problem of selecting nodes from a tree is the most basic and widespread database query problem in the context of XML. Partly a survey of recent work done by the authors and their group on logical query languages for this problem and their expressiveness, this paper provides a number of n...

Robust model predictive control approaches and other applications lead to nonlinear optimization problems defined on (scenario) trees. We present structure-preserving Quasi-Newton update formulas as well as structured inertia correction techniques that allow to solve these problems by interior-point methods with specialized KKT solvers for tree-structured optimization problems. The same type of...

A general approach for the generation of structured knowledge by integrating tree-structured and connec-tionist mechanisms is proposed. We present our main ideas concerning the amalgamation of symbolic tree structures and topology preserving neural networks. Goals of this research are to develop a model that is explainable in symbolic terms and supports direct interaction with the user as well ...

A signiicant limitation of neural networks is that the representations they learn are usually incomprehensible to humans. We present a novel algorithm, Trepan, for extracting comprehensible, symbolic representations from trained neural networks. Our algorithm uses queries to induce a decision tree that approximates the concept represented by a given network. Our experiments demonstrate that Tre...