References of "Wehenkel, Louis"
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See detailLearning inclusion-optimal chordal graphs
Auvray, Vincent; Wehenkel, Louis ULg

in Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI-08) (2008, July 09)

Chordal graphs can be used to encode dependency models that are representable by both directed acyclic and undirected graphs. This paper discusses a very simple and efficient algorithm to learn the ... [more ▼]

Chordal graphs can be used to encode dependency models that are representable by both directed acyclic and undirected graphs. This paper discusses a very simple and efficient algorithm to learn the chordal structure of a probabilistic model from data. The algorithm is a greedy hill-climbing search algorithm that uses the inclusion boundary neighborhood over chordal graphs. In the limit of a large sample size and under appropriate hypotheses on the scoring criterion, we prove that the algorithm will find a structure that is inclusion-optimal when the dependency model of the data-generating distribution can be represented exactly by an undirected graph. The algorithm is evaluated on simulated datasets. [less ▲]

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See detailA hybrid optimization technique coupling evolutionary and local search algorithms
Kelner, Vincent ULg; Capitanescu, Florin ULg; Léonard, Olivier ULg et al

in Journal of Computational & Applied Mathematics (2008), 215(2), 448-456

Evolutionary Algorithms are robust and powerful global optimization techniques for solving large scale problems that have many local optima. However, they require high CPU times, and they are very poor in ... [more ▼]

Evolutionary Algorithms are robust and powerful global optimization techniques for solving large scale problems that have many local optima. However, they require high CPU times, and they are very poor in terms of convergence performance. On the other hand, local search algorithms can converge in a few iterations but lack a global perspective. The combination of global and local search procedures should offer the advantages of both optimization methods while offsetting their disadvantages. This paper proposes a new hybrid optimization technique that merges a Genetic Algorithm with a local search strategy based on the Interior Point method. The efficiency of this hybrid approach is demonstrated by solving a constrained multi-objective mathematical test-case. [less ▲]

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See detailVariable selection for dynamic treatment regimes: a reinforcement learning approach
Fonteneau, Raphaël ULg; Wehenkel, Louis ULg; Ernst, Damien ULg

in The annual machine learning conference of Belgium and the Netherlands (BeNeLearn 2008) (2008, May)

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See detailPrediction of genetic risk of complex diseases by supervised learning
Botta, Vincent ULg; Geurts, Pierre ULg; Hansoul, Sarah et al

Scientific conference (2008, May)

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See detailVariable selection for dynamic treatment regimes
Fonteneau, Raphaël ULg; Wehenkel, Louis ULg; Ernst, Damien ULg

in 27th Benelux Meeting on Systems and Control (2008)

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See detailProteomics for prediction and characterization of response to infliximab in Crohn's disease: a pilot study.
Meuwis, Marie-Alice ULg; Fillet, Marianne ULg; Lutteri, Laurence ULg et al

in Clinical Biochemistry (2008), 41(12), 960-7

OBJECTIVES: Infliximab is the first anti-TNFalpha accepted by the Food and Drug Administration for use in inflammatory bowel disease treatment. Few clinical, biological and genetic factors tend to predict ... [more ▼]

OBJECTIVES: Infliximab is the first anti-TNFalpha accepted by the Food and Drug Administration for use in inflammatory bowel disease treatment. Few clinical, biological and genetic factors tend to predict response in Crohn's disease (CD) patient subcategories, none widely predicting response to infliximab. DESIGN AND METHODS: Twenty CD patients showing clinical response or non response to infliximab were used for serum proteomic profiling on Surface Enhanced Lazer Desorption Ionisation-Time of Flight-Mass Spectrometry (SELDI-TOF-MS), each before and after treatment. Univariate and multivariate data analysis were performed for prediction and characterization of response to infliximab. RESULTS: We obtained a model of classification predicting response to treatment and selected relevant potential biomarkers, among which platelet aggregation factor 4 (PF4). We quantified PF4, sCD40L and IL-6 by ELISA for correlation studies. CONCLUSIONS: This first proteomic pilot study on response to infliximab in CD suggests association between platelet metabolism and response to infliximab and requires validation studies on a larger cohort of patients. [less ▲]

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See detailExploiting tree-based variable importances to selectively identify relevant variables
Huynh-Thu, Vân Anh ULg; Wehenkel, Louis ULg; Geurts, Pierre ULg

in JMLR: Workshop and Conference Proceedings (2008), 4

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See detailMonomeric calgranulins measured by SELDI-TOF mass spectrometry and calprotectin measured by ELISA as biomarkers in arthritis
De Seny, Dominique ULg; Fillet, Marianne ULg; Ribbens, Clio ULg et al

in Clinical Chemistry (2008), 54

BACKGROUND: SELDI-TOF mass spectrometry (MS) is a high-throughput proteomic approach with potential for identifying novel forms of serum biomarkers of arthritis. METHODS: We used SELDI-TOF MS to analyze ... [more ▼]

BACKGROUND: SELDI-TOF mass spectrometry (MS) is a high-throughput proteomic approach with potential for identifying novel forms of serum biomarkers of arthritis. METHODS: We used SELDI-TOF MS to analyze serum samples from patients with various forms of inflammatory arthritis. Several protein profiles were collected on different Bio-Rad Laboratories ProteinChip arrays (CM10 and IMAC-Cu(2+)) and were evaluated statistically to select potential biomarkers. RESULTS: SELDI-TOF MS analyses identified several calgranulin proteins [S100A8 (calgranulin A), S100A9 (calgranulin B), S100A9*, and S100A12 (calgranulin C)], serum amyloid A (SAA), SAA des-Arg (SAA-R), and SAA des-Arg/des-Ser (SAA-RS) as biomarkers and confirmed the results with other techniques, such as western blotting, immunoprecipitation, and nano-LC-MS/MS. The S100 proteins were all able to significantly differentiate samples from patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS) from those of patients with inflammatory bowel diseases used as an inflammatory control (IC) group, whereas the SAA, SAA-R, and SAA-RS proteins were not, with the exception of AS. The 4 S100 proteins were coproduced in all of the pathologies and were significantly correlated with the plasma calprotectin concentration; however, these S100 proteins were correlated with the SAA peak intensities only in the RA and IC patient groups. In RA, these S100 proteins (except for S100A12) were significantly correlated with the serum concentrations of C-reactive protein, matrix metalloproteinase 3, and anti-cyclic citrullinated peptide and with the Disease Activity Score (DAS(28)). CONCLUSIONS: The SELDI-TOF MS technology is a powerful approach for analyzing the status of monomeric, truncated, or posttranslationally modified forms of arthritis biomarkers, such as the S100A8, S100A9, S100A12, and SAA proteins. The fact that the SELDI-TOF MS data were correlated with results obtained with the classic calprotectin ELISA test supports the reliability of this new proteomic technique. [less ▲]

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See detailRisk-aware decision making and dynamic programming
Defourny, Boris ULg; Ernst, Damien ULg; Wehenkel, Louis ULg

Conference (2008)

This paper considers sequential decision making problems under uncertainty, the tradeoff between the expected return and the risk of high loss, and methods that use dynamic programming to find optimal ... [more ▼]

This paper considers sequential decision making problems under uncertainty, the tradeoff between the expected return and the risk of high loss, and methods that use dynamic programming to find optimal policies. It is argued that using Bellman's Principle determines how risk considerations on the return can be incorporated. The discussion centers around returns generated by Markov Decision Processes and conclusions concern a large class of methods in Reinforcement Learning. [less ▲]

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See detailVariable selection for dynamic treatment regimes: a reinforcement learning approach
Fonteneau, Raphaël ULg; Wehenkel, Louis ULg; Ernst, Damien ULg

Conference (2008)

Dynamic treatment regimes (DTRs) can be inferred from data collected through some randomized clinical trials by using reinforcement learning algorithms. During these clinical trials, a large set of ... [more ▼]

Dynamic treatment regimes (DTRs) can be inferred from data collected through some randomized clinical trials by using reinforcement learning algorithms. During these clinical trials, a large set of clinical indicators are usually monitored. However, it is often more convenient for clinicians to have DTRs which are only defined on a small set of indicators rather than on the original full set. To address this problem, we analyse the approximation architecture of the state-action value functions computed by the fitted Q iteration algorithm - a RL algorithm - using tree-based regressors in order to identify a small subset of relevant ones. The RL algorithm is then rerun by considering only as state variables these most relevant indicators to have DTRs defined on a small set of indicators. The approach is validated on benchmark problems inspired from the classical ‘car on the hill’ problem and the results obtained are positive. [less ▲]

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See detailCross-entropy based rare-event simulation for the identification of dangerous events in power systems
Belmudes, Florence ULg; Ernst, Damien ULg; Wehenkel, Louis ULg

in Proceedings of the 10th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS-08) (2008)

We propose in this paper a novel approach for identifying rare events that may endanger power system integrity. This approach is inspired by the rare-event simulation literature and, in particular, by the ... [more ▼]

We propose in this paper a novel approach for identifying rare events that may endanger power system integrity. This approach is inspired by the rare-event simulation literature and, in particular, by the cross-entropy (CE) method for rare event simulation. We propose a general framework for exploiting the CE method in the context of power system reliability evaluation, when a severity function defined on the set of possible events is available. The approach is illustrated on the IEEE 30 bus test system when instability mechanisms related to static voltage security are considered. [less ▲]

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See detailAnalyzing transient instability phenomena beyond the classical stability boundary
Ali, Mahmoud; Glavic, Mevludin; Buisson, Jean et al

in Proceedings of the 40th North American Power Symposium (NAPS 2008) (2008)

We consider power systems for which the amount of power produced by their individual power plants is small with respect to the total generation of the system, and analyze how the transient instability ... [more ▼]

We consider power systems for which the amount of power produced by their individual power plants is small with respect to the total generation of the system, and analyze how the transient instability mechanisms of these systems change qualitatively when their size or the dispersion of their generators increases. Simulation results show that loss of synchronism will propagate more slowly and even stop propagating. Given the evolution of power systems towards more dispersed generation and geographically larger interconnections, we conclude that research in transient stability should focus more on the propagation of the loss of synchronism over longer time periods, so as to assess what happens to the overall system subsequently to the loss of synchronism of the first generators. We also argue that such studies might be very useful in order to provide guidelines for setting up power system control schemes to contain the propagation of instabilities, and we discuss some ideas for designing islanding based emergency control schemes for this. [less ▲]

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See detailLazy planning under uncertainty by optimizing decisions on an ensemble of incomplete disturbance trees
Defourny, Boris ULg; Ernst, Damien ULg; Wehenkel, Louis ULg

in Defourny, Boris; Ernst, Damien; Wehenkel, Louis (Eds.) Recent Advances in Reinforcement Learning (2008)

This paper addresses the problem of solving discrete-time optimal sequential decision making problems having a disturbance space W composed of a finite number of elements. In this context, the problem of ... [more ▼]

This paper addresses the problem of solving discrete-time optimal sequential decision making problems having a disturbance space W composed of a finite number of elements. In this context, the problem of finding from an initial state x0 an optimal decision strategy can be stated as an optimization problem which aims at finding an optimal combination of decisions attached to the nodes of a disturbance tree modeling all possible sequences of disturbances w0, w1, . . ., w(T−1) in W^T over the optimization horizon T. A significant drawback of this approach is that the resulting optimization problem has a search space which is the Cartesian product of O(|W|^(T−1)) decision spaces U, which makes the approach computationally impractical as soon as the optimization horizon grows, even if W has just a handful of elements. To circumvent this difficulty, we propose to exploit an ensemble of randomly generated incomplete disturbance trees of controlled complexity, to solve their induced optimization problems in parallel, and to combine their predictions at time t = 0 to obtain a (near-)optimal first-stage decision. Because this approach postpones the determination of the decisions for subsequent stages until additional information about the realization of the uncertain process becomes available, we call it lazy. Simulations carried out on a robot corridor navigation problem show that even for small incomplete trees, this approach can lead to near-optimal decisions. [less ▲]

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See detailExploiting tree-based variable importances to selectively identify relevant variables
Huynh-Thu, Vân Anh; Wehenkel, Louis ULg; Geurts, Pierre ULg

in Proc. of FSDM08, ECML/PKDD Workshop on New challenges for feature selection in data mining and knowledge discovery (2008)

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See detailNew challenges for feature selection in data mining and knowledge discovery
Saeys, Yvan; Liu, Huan; Inza, Inaki et al

in Journal of Machine Learning Research (2008), 4

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See detailEstimation de densité par ensemble aléatoire de poly-arbres
Ammar, Sourour; Leray, Philippe; Wehenkel, Louis ULg

(2008)

La notion de mélange de modèles simples aléatoires est de plus en plus utilisée et avec succès dans la littérature de l’apprentissage supervisé ces dernières années. Parmi les avantages de ces méthodes ... [more ▼]

La notion de mélange de modèles simples aléatoires est de plus en plus utilisée et avec succès dans la littérature de l’apprentissage supervisé ces dernières années. Parmi les avantages de ces méthodes, citons l’amélioration du passage à l’échelle des algorithmes d’apprentissage grâce à leur aspect aléatoire et l’amélioration de l’exactitude de la prédiction des modèles induits grâce à une flexibilité plus élevée en ce qui concerne le compromis biais/variance. Dans le présent travail, nous proposons d’explorer cette idée dans le contexte de l’estimation de la densité. Nous proposons une nouvelle famille de méthodes d’apprentissage non-supervisé à base de mélange de grands ensembles aléatoires de poly-arbres. La caractéristique spécifique de ces méthodes est leur passage à l’échelle, aussi bien en terme de nombre de variables que de données à traiter. Cette étude, exploratoire, compare empiriquement ces méthodes sur un ensemble de problèmes de test discrets de taille et de complexité croissantes et ouvre de nombreuses perspectives auxquelles nous prévoyons de nous intéresser. [less ▲]

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See detailPredicting gene essentiality from expression patterns in Escherichia coli
Irrthum, Alexandre ULg; Wehenkel, Louis ULg

(2008)

Essential genes are genes whose loss of function causes lethal- ity. In the case of pathogen organisms, the identification of these genes is of considerable interest, as they provide targets for the ... [more ▼]

Essential genes are genes whose loss of function causes lethal- ity. In the case of pathogen organisms, the identification of these genes is of considerable interest, as they provide targets for the development of novel antibiotics. Computational analyses have revealed that the posi- tions of the encoded proteins in the protein-protein interaction network can help predict essentiality, but this type of data is not always avail- able. In this work, we investigate prediction of gene essentiality from expression data only, using a genome-wide compendium of expression patterns in the bacterium Escherichia coli, by using single decision trees and random forests. We first show that, based on the original expression measurements, it is possible to identify essential genes with good accu- racy. Next, we derive, for each gene, higher level features such as average, standard deviation and entropy of its expression pattern, as well as fea- tures related to the correlation of expression patterns between genes. We find that essentiality may actually be predicted based only on the two most relevant ones among these latter.We discuss the biological meaning of these observations. [less ▲]

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See detailCompositional protein analysis of HDL by SELDI-TOF MS during experimental endotoxemia
Levels, Johannes HM; Marée, Raphaël ULg; Geurts, Pierre ULg et al

Poster (2008)

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See detailHigh-dimensional probability density estimation with randomized ensembles of tree structured bayesian networks
Ammar, Sourour; Leray, Philippe; Defourny, Boris ULg et al

in Proc. of the 4th European Workshop on Probabilistic Graphical Models (PGM08) (2008)

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