References of "Wehenkel, Louis"
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See detailProbability Density Estimation by Perturbing and Combining Tree Structured Markov Networks
Ammar, Sourour; Leray, Philippe; Defourny, Boris ULg et al

in Proc. of ECSQARU '09: 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (2009)

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See detailReinforcement learning versus model predictive control: a comparison on a power system problem
Ernst, Damien ULg; Glavic, Mevludin; Capitanescu, Florin ULg et al

in IEEE Transactions on Systems, Man & Cybernetics : Part B (2009), 33(2), 517-519

This paper compares reinforcement learning (RL) with model predictive control (MPC) in a unified framework and reports experimental results of their application to the synthesis of a controller for a ... [more ▼]

This paper compares reinforcement learning (RL) with model predictive control (MPC) in a unified framework and reports experimental results of their application to the synthesis of a controller for a nonlinear and deterministic electrical power oscillations damping problem. Both families of methods are based on the formulation of the control problem as a discrete-time optimal control problem. The considered MPC approach exploits an analytical model of the system dynamics and cost function and computes open-loop policies by applying an interior-point solver to a minimization problem in which the system dynamics are represented by equality constraints. The considered RL approach infers in a model-free way closed-loop policies from a set of system trajectories and instantaneous cost values by solving a sequence of batch-mode supervised learning problems. The results obtained provide insight into the pros and cons of the two approaches and show that RL may certainly be competitive with MPC even in contexts where a good deterministic system model is available. [less ▲]

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See detailConstraint Based Learning of Mixtures of Trees
Schnitzler, François ULg; Wehenkel, Louis ULg

Conference (2009)

Mixtures of trees can be used to model any multivariate distributions. In this work the possibility to learn these models from data by causal learning is explored. The algorithm developed aims at ... [more ▼]

Mixtures of trees can be used to model any multivariate distributions. In this work the possibility to learn these models from data by causal learning is explored. The algorithm developed aims at approximating all first order relationships between pairs of variables by a mixture of a given size. This approach is evaluated based on synthetic data, and seems promising. [less ▲]

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See detailPseudo-geographical representations of power system buses by multidimensional scaling
Belmudes, Florence ULg; Ernst, Damien ULg; Wehenkel, Louis ULg

in Proceedings of the 15th International Conference on Intelligent System Applications to Power Systems (ISAP 2009) (2009)

Graphical representations of power systems are systematically used for planning and operation. The coordinate systems commonly used by Transmission System Operators are static and reflect the geographical ... [more ▼]

Graphical representations of power systems are systematically used for planning and operation. The coordinate systems commonly used by Transmission System Operators are static and reflect the geographical positions of each equipment of the system. We propose in this work to position on a twodimensional map the different buses of a power system in a way such that their coordinates also highlight some other physical information related to them. These pseudo-geographical representations are computed by formulating multidimensional scaling problems which aim at mapping a distance matrix combining both geographical and physical information into a vector of two-dimensional bus coordinates. We illustrate through examples that these pseudo-geographical representations can help to gain insights into the power system physical properties. [less ▲]

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See detailLearning parameters in discrete naive Bayes models by computing fibers of the parametrization map
Auvray, Vincent; Wehenkel, Louis ULg

in NIPS ´08 Workshop: Algebraic and combinatorial methods in machine learning (2008, December 20)

Discrete Naive Bayes models are usually defined parametrically with a map from a parameter space to a probability distribution space. First, we present two families of algorithms that compute the set of ... [more ▼]

Discrete Naive Bayes models are usually defined parametrically with a map from a parameter space to a probability distribution space. First, we present two families of algorithms that compute the set of parameters mapped to a given discrete Naive Bayes distribution satisfying certain technical assumptions. Using these results, we then present two families of parameter learning algorithms that operate by projecting the distribution of observed relative frequencies in a dataset onto the discrete Naive Bayes model considered. They have nice convergence properties, but their computational complexity grows very quickly with the number of hidden classes of the model. [less ▲]

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See detailLe projet PEGASE
Stubbe, Marc; Karoui, Karim; Van Cutsem, Thierry ULg et al

in Revue E Tijdschrift (2008), (4), 37-41

A group of Transmission System Operators (TSO’s), expert companies and leading research centers in power system analysis and applied mathematics, under the coordination of Tractebel Engineering, has ... [more ▼]

A group of Transmission System Operators (TSO’s), expert companies and leading research centers in power system analysis and applied mathematics, under the coordination of Tractebel Engineering, has joined to develop methodologies and software tools able to monitor, simulate and analyze the European Transmission Network (ETN). This project called PEGASE is part of the 7th Framework Programme of the European Commission. Its budget is about 13 MEUR. It started in September 2008 and will last for 4 years. It will define the architecture, data flows and algorithms of an ETN state estimator making use of emerging technologies like the GPS-synchronized Phasor Measurement Units (PMUs). Giving access to the state of the ETN to each TSO would improve dramatically their coordination provided that new ideas to display huge amounts of ETN data are proposed. This is also part of the research. The static simulation of the ETN requires to take into account the various operating rules and control practices of each national grid. New algorithms will be developed, based on optimization techniques and sound engineering judgment. The dynamic simulation of the ETN is of paramount importance for better control and security assessment of this large-scale system. PEGASE will build a prototype of a simulation engine capable of reproducing all kinds of behavior of the ETN. Such an engine will be designed to play extreme scenarios up to the complete black-out of Europe and its subsequent restoration. It will require algorithmic breakthroughs and advanced computer architecture. It will be embedded in a mock-up of a real time dispatcher training simulator. Simplified dynamic simulation tools, able to run much faster than real time, will be developed for on-line security assessment. If the main challenge of the project remains the size and the heterogeneity of the ETN, special attention will be paid to modeling methodology. Component models have to face the complexity introduced by IT and power electronic technologies presently used in power systems. Standard model libraries reach some limits and a greater modeling flexibility is needed to introduce new devices in software tools or exchange models between operators. [less ▲]

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See detailResearch and Education Activities in Electric Power Systems at the University of Liège
Wehenkel, Louis ULg; Ernst, Damien ULg; Rousseaux, Patricia ULg et al

in Revue E Tijdschrift (2008), (4), 54-59

This paper presents research and education activities of the power systems group of the Department of Electrical Engineering and Computer Science of the University of Liège. These activities cover power ... [more ▼]

This paper presents research and education activities of the power systems group of the Department of Electrical Engineering and Computer Science of the University of Liège. These activities cover power systems stability, security, reliability, and markets, within the contexts of expansion planning, operation planning and real-time operation and automatic control. The paper also reviews the international collaborations of the team. [less ▲]

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See detailA new iterative approach to the corrective security-constrained optimal power flow problem
Capitanescu, Florin ULg; Wehenkel, Louis ULg

in IEEE Transactions on Power Systems (2008), 23(4), 1342-1351

This paper deals with techniques to solve the corrective security-constrained optimal power flow (CSCOPF) problem. To this end, we propose a new iterative approach that comprises four modules: a CSCOPF ... [more ▼]

This paper deals with techniques to solve the corrective security-constrained optimal power flow (CSCOPF) problem. To this end, we propose a new iterative approach that comprises four modules: a CSCOPF which considers only a subset of potentially binding contingencies among the postulated ones, a (steady-state) security analysis (SSSA), a contingency filtering (CF) technique, and an OPF variant to check post-contingency state feasibility when taking into account post-contingency corrective actions. We compare performances of our approach and its possible variants with classical CSCOPF approaches such as the direct approach and Benders decomposition (BD), on three systems of 60, 118, and 1203 buses. [less ▲]

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See detailRaw genotypes vs haplotype blocks for genome wide association studies by random forests
Botta, Vincent ULg; Hansoul, Sarah ULg; Geurts, Pierre ULg et al

in Proc. of MLSB 2008, second workshop on Machine Learning in Systems Biology (2008, September)

We consider two different representations of the input data for genome-wide association studies using random forests, namely raw genotypes described by a few thousand to a few hundred thousand discrete ... [more ▼]

We consider two different representations of the input data for genome-wide association studies using random forests, namely raw genotypes described by a few thousand to a few hundred thousand discrete variables each one describing a single nucleotide polymorphism, and haplotype block contents, represented by the combinations of about 10 to 100 adjacent and correlated genotypes. We adapt random forests to exploit haplotype blocks, and compare this with the use of raw genotypes, in terms of predictive power and localization of causal mutations, by using simulated datasets with one or two interacting effects. [less ▲]

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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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