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Search of Robust and Stable Solutions for Constraints Satisfaction Problems
Date & Time: May 16, 2012, 15:00:00.0
Location: Room G17 WGB
Speaker: Laura Climent
Many real life problems come from uncertain and dynamic environments, where the original problem may change during its execution. Thus, the solution found for the problem may become invalid. For this reason, the search of robust solutions for Constraint Satisfaction Problems (CSPs) has become an important issue in the field of constraint programming. In some cases, there exists knowledge about the uncertain and dynamic environment. However most of our work is focused in problems where this information is unknown. In addition, we consider CSPs with ordered domains. Given this context, even if this does not cover all the possible changes, it is reasonable to assume that the original bounds of the solution space can undergo changes. For this reason, when the information about the possible future changes is unknown, the main objective is to find the solution located as far as possible from the bounds of the solution space. To this end, we propose several modeling techniques and search algorithm. Furtheremore, we extend the concepts of stability and robustness in this framework.

Declarative Pattern Mining using Constraint Programming
Date & Time: March 28, 2012, 15:00:00.0
Location: Room 2.16 WGB
Speaker: Tias Guns
Declarative Pattern Mining using Constraint Programming It involves the use of constraint programming to solve data mining problems, more specifically the standard itemset mining problem and its many 'constraint-based' variants. The big benefit of CP in this context is that it is general and can solve many itemset mining related problems in a single framework. This flexibility was previously unknown to the itemset mining field, as most of the work focused on creating highly efficient (imperative) algorithms. Of course generality often comes at the cost of effiency, but I will demonstrate how the advanced propagation of CP can (and has) improved on existing state-of-the-art mining algorithms. I will also offer a glimpse of what declarative constraint languages can offer to data mining.

Robust Recommender Systems
Date & Time: June 29, 2009, 15:00:00.0
Location: 4C Meeting Room -14 Washington St. West
Speaker: Robin Burke
The openness and anonymity of the Internet environment create many hazards for e-commerce. For collaborative recommender systems, it raises the possibility of that attackers will seek to bias the output recommendations through manipulation of the public inputs that the system permits. Fighting such manipulation is a constant battle for the owners and maintainers of such systems. In this talk, I will describe the known vulnerabilities of collaborative algorithms and examine a range of possible attack types that could be deployed against them. With these vulnerabilities in mind, I will discuss possible responses, including the deployment of alternate recommendation algorithms and the use of supervised and unsupervised techniques to detect attacks. Building on this research, I will examine what it might mean to build a robust collaborative recommender and consider the implications for other machine learning techniques deployed in public on-line environments.

Analyzing Randomized Search Heuristics: An Example
Date & Time: April 7, 2009, 15:00:00.0
Location: 4C Meeting Room -14 Washington St. West
Speaker: Thomas Jansen
The last 15 years have seen a steady development of methods and tools for the analysis of evolutionary algorithms. These algorithms are just one example for randomized search heuristics, a broad class of algorithms applied for many different kind of search problems, among those optimization. Considering evolutionary algorithms, an overview of methods and tools for obtaining rigorously proven results on the expected optimization time, the most important notion of efficiency in the context of optimization, is presented. Building on these methods we consider a concrete example for the transfer of these methods to another kind of randomized search heuristics, namely artificial immune systems. Concentrating on pure static aging as one of many pertinent concepts we prove that artificial immune systems are very sensitive to the maximal age that is set in pure static aging. In this analysis we consider a new analytical tool that has the potential to prove itself useful beyond this single concrete application.

Context-Free Grammar Constraints
Date & Time: February 25, 2009, 15:00:00.0
Location: 4C Meeting Room -14 Washington St. West
Speaker: Prof. Meinolf Sellmann
When dealing with real-world optimization problems, we frequently face complicated side constraints which are hard to formulate in integer programming and constraint programming. To facilitate the modeling process, we introduce the context-free grammar constraint that requires that an assignment of values to an ordered set of variables must form a word in a given context-free language. For this constraint, we devise an efficient, complete, and incremental filtering algorithm that has the same asymptotic complexity as the Cocke-Younger-Kasami algorithm for parsing context-free languages. Moreover, we show how the constraint can be linearized automatically whereby the resulting polytope has only integer extreme points. Joint work with Serdar Kadioglu, Louis-Martin Rousseau, Claude-Guy Quimper, and Gilles Pesant.

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