References of "Wehenkel, Louis"
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See detailGradient boosting for kernelized output spaces
Geurts, Pierre ULg; Wehenkel, Louis ULg; d'Alché-Buc, Florence

in Proceedings of the 24th International Conference on Machine Learning (2007)

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See detailNouvelles approches dans la prise en charge de l'infection a VIH.
Chandrika, K.; Dellot, Patricia ULg; Frippiat, Frédéric ULg et al

in Revue Médicale de Liège (2007), 62 Spec No

HIV infection remains a major problem of public health in Belgium as well as globally. The number of new diagnosies of HIV infection in Belgium remains between two and three daily. Given the dramatic ... [more ▼]

HIV infection remains a major problem of public health in Belgium as well as globally. The number of new diagnosies of HIV infection in Belgium remains between two and three daily. Given the dramatic effect of antiretroviral therapy on the mortality due to HIV infection, the number of patients is constantly increasing. The different problems related to HIV care are also changing. Aging of the patients and chronic exposure to antiretroviral medications have induced new complications. We will present in this brief article several new experimental and clinical approaches in which our centre has participated during the last two years. [less ▲]

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See detailPreDetector : Prokaryotic Regulatory Element Detector
Hiard, Samuel ULg; Rigali, Sébastien ULg; Colson, Séverine ULg et al

Poster (2006, May 17)

PreDetector is a stand-alone software, written in java. Its final aim is to predict regulatory sites for prokaryotic species. It comprises two functionalities. The first one is very similar to Target ... [more ▼]

PreDetector is a stand-alone software, written in java. Its final aim is to predict regulatory sites for prokaryotic species. It comprises two functionalities. The first one is very similar to Target Explorer1. From a set of sequences identified as potential target sites, PreDetector creates a consensus sequence and computes its scoring matrix. This sequence and matrix can be saved on a file and, then, be used to find along a selected genome the sequences that are close enough to the consensus sequence. To this end, a score is attributed to each locus in the genome according to the similarity measure defined by the matrix. The output of this functionality is filtered with a cut-off score and then directly used as input by the second one. The second functionality starts by fetching the gene positions of the selected species from the NCBI server. The loci having above cut-off score are then classified into four classes, allowing multiple classes for one element. This gives the biologists a better view of his discovered sequences. [less ▲]

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See detailExtremely randomized trees
Geurts, Pierre ULg; Ernst, Damien ULg; Wehenkel, Louis ULg

in Machine Learning (2006), 63(1), 3-42

This paper proposes anew tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute and cut-point choice while ... [more ▼]

This paper proposes anew tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute and cut-point choice while splitting a tree node. In the extreme case, it builds totally randomized trees whose structures are independent of the output values of the learning sample. The strength of the randomization can be tuned to problem specifics by the appropriate choice of a parameter. We evaluate the robustness of the default choice of this parameter, and we also provide insight on how to adjust it in particular situations. Besides accuracy, the main strength of the resulting algorithm is computational efficiency. A bias/variance analysis of the Extra-Trees algorithm is also provided as well as a geometrical and a kernel characterization of the models induced. [less ▲]

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See detailModel predictive control and reinforcement learning as two complementary frameworks
Ernst, Damien ULg; Glavic, Mevludin; Capitanescu, Florin ULg et al

in Proceedings of the 13th IFAC Workshop on Control Applications of Optimisation (2006)

Model predictive control (MPC) and reinforcement learning (RL) are two popular families of methods to control system dynamics. In their traditional setting, they formulate the control problem as a ... [more ▼]

Model predictive control (MPC) and reinforcement learning (RL) are two popular families of methods to control system dynamics. In their traditional setting, they formulate the control problem as a discrete-time optimal control problem and compute a suboptimal control policy. We present in this paper in a unified framework these two families of methods. We run for MPC and RL algorithms simulations on a benchmark control problem taken from the power system literature and discuss the results obtained. [less ▲]

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See detailReinforcement learning with raw image pixels as input state
Ernst, Damien ULg; Marée, Raphaël ULg; Wehenkel, Louis ULg

in Advances in machine vision, image processing & pattern analysis (Lecture notes in computer science, Vol. 4153) (2006)

We report in this paper some positive simulation results obtained when image pixels are directly used as input state of a reinforcement learning algorithm. The reinforcement learning algorithm chosen to ... [more ▼]

We report in this paper some positive simulation results obtained when image pixels are directly used as input state of a reinforcement learning algorithm. The reinforcement learning algorithm chosen to carry out the simulation is a batch-mode algorithm known as fitted Q iteration. [less ▲]

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See detailApplications of security-constrained optimal power flows
Capitanescu, Florin ULg; Glavic, Mevludin; Ernst, Damien ULg et al

in In Proceedings of Modern Electric Power Systems Symposium, MEPS06 (2006)

This paper proposes to formulate security control as a sequential decision making problem and presents new developments in automatic learning of sequential decision making strategies from simulations and ... [more ▼]

This paper proposes to formulate security control as a sequential decision making problem and presents new developments in automatic learning of sequential decision making strategies from simulations and/or information collected from real-life system measurements. The exploitation of these methods for the design of decision making strategies and control policies in the context of preventive and emergency mode dynamic security assessment and control is discussed and further opportunities for research in this area are highlighted. [less ▲]

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See detailClinical data based optimal STI strategies for HIV: a reinforcement learning approach
Ernst, Damien ULg; Stan, Guy-Bart; Gonçalves, Jorge et al

in Proceedings of the 45th IEEE Conference on Decision and Control (CDC 2006) (2006)

This paper addresses the problem of computing optimal structured treatment interruption strategies for HIV infected patients. We show that reinforcement learning may be useful to extract such strategies ... [more ▼]

This paper addresses the problem of computing optimal structured treatment interruption strategies for HIV infected patients. We show that reinforcement learning may be useful to extract such strategies directly from clinical data, without the need of an accurate mathematical model of HIV infection dynamics. To support our claims, we report simulation results obtained by running a recently proposed batch-mode reinforcement learning algorithm, known as fitted Q iteration, on numerically generated data. [less ▲]

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See detailClinical data based optimal STI strategies for HIV: a reinforcement learning approach
Ernst, Damien ULg; Stan, Guy-Bart; Gonçalves, Jorge et al

in Proceedings of the 15th Annual Machine Learning Conference of Belgium and The Netherlands (Benelearn 2006) (2006)

This paper addresses the problem of computing optimal structured treatment interruption strategies for HIV infected patients. We show that reinforcement learning may be useful to extract such strategies ... [more ▼]

This paper addresses the problem of computing optimal structured treatment interruption strategies for HIV infected patients. We show that reinforcement learning may be useful to extract such strategies directly from clinical data, without the need of an accurate mathematical model of HIV infection dynamics. To support our claims, we report simulation results obtained by running a recently proposed batch-mode reinforcement learning algorithm, known as tted Q iteration, on numerically generated data. [less ▲]

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See detailMulti-area security assessment: results using efficient bounding method
Wehenkel, Louis ULg; Glavic, Mevludin; Ernst, Damien ULg

in Proceedings of the 38th North American Power Symposium (NAPS 2006) (2006)

We present our recent results on using previously introduced framework for multi-area security assessment in large interconnections. The basic idea of the framework is exchanging just enough information ... [more ▼]

We present our recent results on using previously introduced framework for multi-area security assessment in large interconnections. The basic idea of the framework is exchanging just enough information so that each operator can evaluate the impact in his control area of contingencies both internal and external to his area. We provide illustrations based on a localization concept known as efficient bounding method and recently introduced approximate DC model of the European interconnected system. In this paper we focus on four transmission system operators (within the approximate DC model): Belgium, France, Germany, and Netherlands (for both Summer and Winter-peak load conditions). [less ▲]

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See detailDamping control by fusion of reinforcement learning and control Lyapunov functions
Glavic, Mevludin; Wehenkel, Louis ULg; Ernst, Damien ULg

in Proceedings of the 38th North American Power Symposium (NAPS 2006) (2006)

The main idea behind the concept, proposed in the paper, is the opportunity to make control systems with increased capabilities by synergetic fusion of the domain-specific knowledge and the methodologies ... [more ▼]

The main idea behind the concept, proposed in the paper, is the opportunity to make control systems with increased capabilities by synergetic fusion of the domain-specific knowledge and the methodologies from control theory and artificial intelligence. The particular approach considered combines Control Lyapunov Functions (CLF), a constructive control technique, an Reinforcement Learning (RL) in attempt to optimize a mix of system stability and performance. Two control schemes are proposed and the capabilities of the resulting controllers are illustrated on a control problem involving a Thyristor Controlled Series Capacitor (TCSC) for damping oscillations in a four-machine power system. [less ▲]

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See detailAutomatic learning of sequential decision strategies for dynamic security assessment and control
Wehenkel, Louis ULg; Glavic, Mevludin; Geurts, Pierre ULg et al

in Proceedings of the IEEE Power Engineering Society General Meeting 2006 (2006)

This paper proposes to formulate security control as a sequential decision making problem and presents new developments in automatic learning of sequential decision making strategies from simulations and ... [more ▼]

This paper proposes to formulate security control as a sequential decision making problem and presents new developments in automatic learning of sequential decision making strategies from simulations and/or information collected from real-life system measurements. The exploitation of these methods for the design of decision making strategies and control policies in the context of preventive and emergency mode dynamic security assessment and control is discussed and further opportunities for research in this area are highlighted. [less ▲]

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See detailEnsembles of extremely randomized trees and some generic applications
Wehenkel, Louis ULg; Ernst, Damien ULg; Geurts, Pierre ULg

in Proceedings of Robust Methods for Power System State Estimation and Load Forecasting (2006)

In this paper we present a new tree-based ensemble method called “Extra-Trees”. This algorithm averages predictions of trees obtained by partitioning the inputspace with randomly generated splits, leading ... [more ▼]

In this paper we present a new tree-based ensemble method called “Extra-Trees”. This algorithm averages predictions of trees obtained by partitioning the inputspace with randomly generated splits, leading to significant improvements of precision, and various algorithmic advantages, in particular reduced computational complexity and scalability. We also discuss two generic applications of this algorithm, namely for time-series classification and for the automatic inference of near-optimal sequential decision policies from experimental data. [less ▲]

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See detailOn multi-area security assessment of large interconnected power systems
Wehenkel, Louis ULg; Glavic, Mevludin; Ernst, Damien ULg

in Proceedings of the Second Carnegie Mellon Conference in Electric Power Systems: Monitoring, Sensing, Software and its Valuation for the Changing electric Power Industry (2006)

The paper introduces a framework for information exchange and coordination of security assessment suitable for distributed multi-area control in large interconnections operated by a team of transmission ... [more ▼]

The paper introduces a framework for information exchange and coordination of security assessment suitable for distributed multi-area control in large interconnections operated by a team of transmission system operators. The basic idea of the proposed framework consists of exchanging just enough information so that each operator can evaluate the impact in his control area of contingencies both internal and external to his area. The framework has been thought out with the European perspective in mind where it is presently not possible to set up a transnational security coordinator that would have authority to handle security control over the whole or part of the European interconnection. Nevertheless, it can also be considered as an approach to handle security control in North-American Mega-RTOs, where it could help to circumvent problems of scalability of algorithms and maintainability of data by distributing them over the TSOs under the authority of the RTO. [less ▲]

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See detailAbout automatic learning for advanced sensing, monitoring and control of electric power systems
Wehenkel, Louis ULg; Glavic, Mevludin; Geurts, Pierre ULg et al

in Proceedings of the Second Carnegie Mellon Conference in Electric Power Systems: Monitoring, Sensing, Software and its Valuation for the Changing electric Power Industry (2006)

The paper considers the possible uses of automatic learning for improving power system performance by software methodologies. Automatic learning per se is first reviewed and recent developements of the ... [more ▼]

The paper considers the possible uses of automatic learning for improving power system performance by software methodologies. Automatic learning per se is first reviewed and recent developements of the field are highlighted. Then the authors’ views of its main actual or potential applications related to power system operation and control are described, and in each application present status and needs for further developments are discussed. [less ▲]

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See detailBiological Image Classification with Random Subwindows and Extra-Trees
Marée, Raphaël ULg; Geurts, Pierre ULg; Wehenkel, Louis ULg

Conference (2006)

We illustrate the potential of our image classification method on three datasets of images at different imaging modalities/scales, from subcellular locations up to human body regions. The method is based ... [more ▼]

We illustrate the potential of our image classification method on three datasets of images at different imaging modalities/scales, from subcellular locations up to human body regions. The method is based on random subwindows extraction and the combination of their classification using ensembles of extremely randomized decision trees. [less ▲]

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See detailOK3: Méthode d’arbres à sortie noyau pour la prédiction de sorties structurées et l’apprentissage de noyau
Geurts, Pierre ULg; Wehenkel, Louis ULg; d'Alché-Buc, Florence

in Proc. of CAP (Conférence francophone d'apprentissage) (2006)

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See detailKernelizing the output of tree-based methods
Geurts, Pierre ULg; Wehenkel, Louis ULg; d Alché-Buc, Florence

in Proceedings of the 23rd International Conference on Machine Learning (2006)

We extend tree-based methods to the prediction of structured outputs using a kernelization of the algorithm that allows one to grow trees as soon as a kernel can be defined on the output space. The ... [more ▼]

We extend tree-based methods to the prediction of structured outputs using a kernelization of the algorithm that allows one to grow trees as soon as a kernel can be defined on the output space. The resulting algorithm, called output kernel trees (OK3), generalizes classification and regression trees as well as tree-based ensemble methods in a principled way. It inherits several features of these methods such as interpretability, robustness to irrelevant variables, and input scalability. When only the Gram matrix over the outputs of the learning sample is given, it learns the output kernel as a function of inputs. We show that the proposed algorithm works well on an image reconstruction task and on a biological network inference problem. [less ▲]

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

in Journal of Computational & Applied Mathematics (2006), 215(2), 281-287

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 detailSegment and combine: a generic approach for supervised learning of invariant classifiers from topologically structured data
Geurts, Pierre ULg; Marée, Raphaël ULg; Wehenkel, Louis ULg

in Proceedings of the Machine Learning Conference of Belgium and The Netherlands (Benelearn) (2006)

A generic method for supervised classification of structured objects is presented. The approach induces a classifier by (i) deriving a surrogate dataset from a pre-classified dataset of structured objects ... [more ▼]

A generic method for supervised classification of structured objects is presented. The approach induces a classifier by (i) deriving a surrogate dataset from a pre-classified dataset of structured objects, by segmenting them into pieces, (ii) learning a model relating pieces to object-classes, (iii) classifying structured objects by combining predictions made for their pieces. The segmentation allows to exploit local information and can be adapted to inject invariances into the resulting classifier. The framework is illustrated on practical sequence, time-series and image classification problems. [less ▲]

Detailed reference viewed: 135 (14 ULg)