References of "Wehenkel, Louis"
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See detailExtremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval
Marée, Raphaël ULg; Wehenkel, Louis ULg; Geurts, Pierre ULg

in Criminisi, A; Shotton, J (Eds.) Decision Forests in Computer Vision and Medical Image Analysis, Advances in Computer Vision and Pattern Recognition (2013)

We present a unified framework involving the extraction of random subwindows within images and the induction of ensembles of extremely randomized trees. We discuss the specialization of this framework for ... [more ▼]

We present a unified framework involving the extraction of random subwindows within images and the induction of ensembles of extremely randomized trees. We discuss the specialization of this framework for solving several general problems in computer vision, ranging from image classification and segmentation to content-based image retrieval and interest point detection. The methods are illustrated on various applications and datasets from the biomedical domain [less ▲]

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See detailGene regulatory network inference from systems genetics data using tree-based methods
Huynh-Thu, Vân Anh ULg; Wehenkel, Louis ULg; Geurts, Pierre ULg

in de la Fuente, Alberto (Ed.) Gene Network Inference - Verification of Methods for Systems Genetics Data (2013)

One of the pressing open problems of computational systems biology is the elucidation of the topology of gene regulatory networks (GRNs). In an attempt to solve this problem, the idea of systems genetics ... [more ▼]

One of the pressing open problems of computational systems biology is the elucidation of the topology of gene regulatory networks (GRNs). In an attempt to solve this problem, the idea of systems genetics is to exploit the natural variations that exist between the DNA sequences of related individuals and that can represent the randomized and multifactorial perturbations necessary to recover GRNs. In this chapter, we present new methods, called GENIE3-SG-joint and GENIE3- SG-sep, for the inference of GRNs from systems genetics data. Experiments on the artificial data of the StatSeq benchmark and of the DREAM5 Systems Genetics challenge show that exploiting jointly expression and genetic data is very helpful for recovering GRNs, and one of our methods outperforms by a large extent the official best performing method of the DREAM5 challenge. [less ▲]

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See detailWhither probabilistic security management for real-time operation of power systems ?
Karangelos, Efthymios ULg; Panciatici, Patrick; Wehenkel, Louis ULg

in Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid (IREP), 2013 IREP Symposium (2013)

This paper investigates the stakes of introducing probabilistic approaches for the management of power system’s security. In real-time operation, the aim is to arbitrate in a rational way between ... [more ▼]

This paper investigates the stakes of introducing probabilistic approaches for the management of power system’s security. In real-time operation, the aim is to arbitrate in a rational way between preventive and corrective control, while taking into account i) the prior probabilities of contingencies, ii) the possible failure modes of corrective control actions, iii) the socio-economic consequences of service interruptions. This work is a first step towards the construction of a globally coherent decision making framework for security management from long-term system expansion, via mid-term asset management, towards short-term operation planning and real-time operation. [less ▲]

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See detailScenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine Learning
Defourny, Boris; Ernst, Damien ULg; Wehenkel, Louis ULg

in INFORMS Journal on Computing (2013), 25(3), 488-501

In the context of multistage stochastic optimization problems, we propose a hybrid strategy for generalizing to nonlinear decision rules, using machine learning, a finite data set of constrained vector ... [more ▼]

In the context of multistage stochastic optimization problems, we propose a hybrid strategy for generalizing to nonlinear decision rules, using machine learning, a finite data set of constrained vector-valued recourse decisions optimized using scenario-tree techniques from multistage stochastic programming. The decision rules are based on a statistical model inferred from a given scenario-tree solution and are selected by out-of-sample simulation given the true problem. Because the learned rules depend on the given scenario tree, we repeat the procedure for a large number of randomly generated scenario trees and then select the best solution (policy) found for the true problem. The scheme leads to an ex post selection of the scenario tree itself. Numerical tests evaluate the dependence of the approach on the machine learning aspects and show cases where one can obtain near-optimal solutions, starting with a “weak” scenario-tree generator that randomizes the branching structure of the trees. [less ▲]

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See detailContingency ranking with respect to overloads in very large power systems taking into account uncertainty, preventive, and corrective actions
Fliscounakis, Stéphane; Panciatici, Patrick; Capitanescu, Florin et al

in IEEE Transactions on Power Systems (2013)

This paper deals with day-ahead security management with respect to a postulated set of contingencies, while taking into account uncertainties about the next day generation/load scenario. In order to help ... [more ▼]

This paper deals with day-ahead security management with respect to a postulated set of contingencies, while taking into account uncertainties about the next day generation/load scenario. In order to help the system operator in decision making under uncertainty, we aim at ranking these contingencies into four clusters according to the type of control actions needed to cover the worst uncertainty pattern of each contingency with respect to branch overload. To this end we use a fixed point algorithm that loops over two main modules: a discrete bi-level program (BLV) that computes the worst-case scenario, and a special kind of security constrained optimal power flow (SCOPF) which computes optimal preventive/corrective actions to cover the worst-case. We rely on a DC grid model, as the large number of binary variables, the large size of the problem, and the stringent computational requirements preclude the use of existing mixed integer nonlinear programming (MINLP) solvers. Consequently we solve the SCOPF using a mixed integer linear programming (MILP) solver while the BLV is decomposed into a series of MILPs. We provide numerical results with our approach on a very large European system model with 9241 buses and 5126 contingencies. [less ▲]

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See detailStratégies d'échantillonnage pour l'apprentissage par renforcement batch
Fonteneau, Raphaël ULg; Murphy, Susan A.; Wehenkel, Louis ULg et al

in Revue d'Intelligence Artificielle [=RIA] (2013), 27(2), 171-194

We propose two strategies for experiment selection in the context of batch mode reinforcement learning. The first strategy is based on the idea that the most interesting experiments to carry out at some ... [more ▼]

We propose two strategies for experiment selection in the context of batch mode reinforcement learning. The first strategy is based on the idea that the most interesting experiments to carry out at some stage are those that are the most liable to falsify the current hypothesis about the optimal control policy. We cast this idea in a context where a policy learning algorithm and a model identification method are given a priori. The second strategy exploits recently published methods for computing bounds on the return of control policies from a set of trajectories in order to sample the state-action space so as to be able to discriminate between optimal and non-optimal policies. Both strategies are experimentally validated, showing promising results. [less ▲]

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See detailMeta-learning of Exploration/Exploitation Strategies: The Multi-Armed Bandit Case
Maes, Francis; Wehenkel, Louis ULg; Ernst, Damien ULg

in Filipe, Joaquim; Fred, Ana (Eds.) Agents and Artificial Intelligence: 4th International Conference, ICAART 2012, Vilamoura, Portugal, February 6-8, 2012. Revised Selected Papers (2013)

The exploration/exploitation (E/E) dilemma arises naturally in many subfields of Science. Multi-armed bandit problems formalize this dilemma in its canonical form. Most current research in this field ... [more ▼]

The exploration/exploitation (E/E) dilemma arises naturally in many subfields of Science. Multi-armed bandit problems formalize this dilemma in its canonical form. Most current research in this field focuses on generic solutions that can be applied to a wide range of problems. However, in practice, it is often the case that a form of prior information is available about the specific class of target problems. Prior knowledge is rarely used in current solutions due to the lack of a systematic approach to incorporate it into the E/E strategy. To address a specific class of E/E problems, we propose to proceed in three steps: (i) model prior knowledge in the form of a probability distribution over the target class of E/E problems; (ii) choose a large hypothesis space of candidate E/E strategies; and (iii), solve an optimization problem to find a candidate E/E strategy of maximal average performance over a sample of problems drawn from the prior distribution. We illustrate this meta-learning approach with two different hypothesis spaces: one where E/E strategies are numerically parameterized and another where E/E strategies are represented as small symbolic formulas. We propose appropriate optimization algorithms for both cases. Our experiments, with two-armed “Bernoulli” bandit problems and various playing budgets, show that the metalearnt E/E strategies outperform generic strategies of the literature (UCB1, UCB1-T UNED, UCB-V, KL-UCB and epsilon-GREEDY); they also evaluate the robustness of the learnt E/E strategies, by tests carried out on arms whose rewards follow a truncated Gaussian distribution. [less ▲]

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See detailExperiments with the interior-point method for solving large scale Optimal Power Flow problems
Capitanescu, Florin; Wehenkel, Louis ULg

in Electric Power Systems Research (2013), 95

This paper reports extensive results obtained with the interior-point method (IPM) for nonlinear programmes (NLPs) stemming from large-scale and severely constrained classical Optimal Power Flow (OPF) and ... [more ▼]

This paper reports extensive results obtained with the interior-point method (IPM) for nonlinear programmes (NLPs) stemming from large-scale and severely constrained classical Optimal Power Flow (OPF) and Security-Constrained Optimal Power Flow (SCOPF) problems. The paper discusses transparently the problems encountered such as convergence reliability and speed issues of the method. [less ▲]

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See detailSurvival analysis: finding relevant epistatic SNP pairs using Model- Based Multifactor Dimensionality Reduction
Van Lishout, François ULg; Vens, Céline; Urrea, Victor et al

Conference (2012, December 03)

Analyzing the combined effects of genes (and/or environmental factors) on the development of complex diseases is quite challenging, both from the statistical and computational perspective, even using a ... [more ▼]

Analyzing the combined effects of genes (and/or environmental factors) on the development of complex diseases is quite challenging, both from the statistical and computational perspective, even using a relatively small number of genetic and non-genetic exposures. Several data-mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR). Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new dimensionality reduction technique, is able to unify the best of both nonparametric and parametric worlds, and has proven its utility in a variety of theoretical and practical settings. Until now, MB-MDR software has only accommodated traits that are measured on a binary or interval scale. Time-to-event data could therefore not be analyzed with the MB-MDR methodology. MB-MDR-3.0.0 overcomes this shortcoming of earlier versions. We show the added value of MB-MDR for censored traits by comparing the implemented strategies with more classical methods such as those based on a parametric regression paradigm. The simulation results are supplemented with an application to real-life data. [less ▲]

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See detailCautious operation planning under uncertainties
Capitanescu, Florin ULg; Fliscounakis, Stéphane; Panciatici, Patrick et al

in IEEE Transactions on Power Systems (2012), 27(4), 1859-1869

This paper deals with day-ahead power systems security planning under uncertainties, by posing an optimization problem over a set of power injection scenarios that could show up the next day and modeling ... [more ▼]

This paper deals with day-ahead power systems security planning under uncertainties, by posing an optimization problem over a set of power injection scenarios that could show up the next day and modeling the next day's real-time control strategies aiming at ensuring security with respect to contingencies by a combination of preventive and corrective controls. We seek to determine whether and which day-ahead decisions must be taken so that for scenarios over the next day there still exists an acceptable combination of preventive and corrective controls ensuring system security for any postulated contingency. We formulate this task as a three-stage feasibility checking problem, where the first stage corresponds to day-ahead decisions, the second stage to preventive control actions, and the third stage to corrective post-contingency controls. We propose a solution approach based on the problem decomposition into successive optimal power flow (OPF) and security-constrained optimal power flow (SCOPF) problems of a special type. Our approach is illustrated on the Nordic32 system and on a 1203-bus model of a real-life system. [less ▲]

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See detailPolicy search in a space of simple closed-form formulas: towards interpretability of reinforcement learning
Maes, Francis ULg; Fonteneau, Raphaël ULg; Wehenkel, Louis ULg et al

in Discovery Science 15th International Conference, DS 2012, Lyon, France, October 29-31, 2012. Proceedings (2012, October)

In this paper, we address the problem of computing interpretable solutions to reinforcement learning (RL) problems. To this end, we propose a search algorithm over a space of simple losed-form formulas ... [more ▼]

In this paper, we address the problem of computing interpretable solutions to reinforcement learning (RL) problems. To this end, we propose a search algorithm over a space of simple losed-form formulas that are used to rank actions. We formalize the search for a high-performance policy as a multi-armed bandit problem where each arm corresponds to a candidate policy canonically represented by its shortest formula-based representation. Experiments, conducted on standard benchmarks, show that this approach manages to determine both efficient and interpretable solutions. [less ▲]

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See detailOperating in the fog: security management under uncertainty
Panciatici, Patrick; Bareux, Gabriel; Wehenkel, Louis ULg

in IEEE Power & Energy Magazine (2012)

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See detailEmbedding Monte Carlo search of features in tree-based ensemble methods
Maes, Francis ULg; Geurts, Pierre ULg; Wehenkel, Louis ULg

in Flach, Peter; De Bie, Tijl; Cristianini, Nello (Eds.) Machine Learning and Knowledge Discovery in Data Bases (2012, September)

Feature generation is the problem of automatically constructing good features for a given target learning problem. While most feature generation algorithms belong either to the filter or to the wrapper ... [more ▼]

Feature generation is the problem of automatically constructing good features for a given target learning problem. While most feature generation algorithms belong either to the filter or to the wrapper approach, this paper focuses on embedded feature generation. We propose a general scheme to embed feature generation in a wide range of tree-based learning algorithms, including single decision trees, random forests and tree boosting. It is based on the formalization of feature construction as a sequential decision making problem addressed by a tractable Monte Carlo search algorithm coupled with node splitting. This leads to fast algorithms that are applicable to large-scale problems. We empirically analyze the performances of these tree-based learners combined or not with the feature generation capability on several standard datasets. [less ▲]

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See detailMixtures of Bagged Markov Tree Ensembles
Schnitzler, François ULg; Geurts, Pierre ULg; Wehenkel, Louis ULg

in Cano Utrera, Andrès; Gómez-Olmedo, Manuel; Nielsen, Thomas (Eds.) Proceedings of the 6th European Workshop on Probabilistic Graphical Models (2012, September)

Markov trees, a probabilistic graphical model for density estimation, can be expanded in the form of a weighted average of Markov Trees. Learning these mixtures or ensembles from observations can be ... [more ▼]

Markov trees, a probabilistic graphical model for density estimation, can be expanded in the form of a weighted average of Markov Trees. Learning these mixtures or ensembles from observations can be performed to reduce the bias or the variance of the estimated model. We propose a new combination of both, where the upper level seeks to reduce bias while the lower level seeks to reduce variance. This algorithm is evaluated empirically on datasets generated from a mixture of Markov trees and from other synthetic densities. [less ▲]

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See detailExploiting the use of DC SCOPF approximation to improve iterative AC SCOPF algorithms
Marano Marcolini, Alejandro; Capitanescu, Florin ULg; Jose Luis, Martinez Ramos et al

in IEEE Transactions on Power Systems (2012), 27(3), 1459-1466

This paper focuses on improving the solution techniques for the AC SCOPF problem of active power dispatch by using the DC SCOPF approximation within the SCOPF algorithm. Our approach brings two benefits ... [more ▼]

This paper focuses on improving the solution techniques for the AC SCOPF problem of active power dispatch by using the DC SCOPF approximation within the SCOPF algorithm. Our approach brings two benefits compared to benchmark SCOPF algorithms: it speeds-up the solution of an iterative AC SCOPF algorithm thanks to a more efficient identification of binding contingencies, and allows improving the objective by an appropriate choice of a limited number of corrective actions for each contingency. The proposed approach is illustrated on 5 test systems of 60, 118, 300, 1203, and 2746 buses. [less ▲]

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See detailComparator selection for RPC with many labels
Hiard, Samuel ULg; Geurts, Pierre ULg; Wehenkel, Louis ULg

in ECAI 2012 : 20th European Conference on Artificial Intelligence : 27-31 August 2012, Montpellier, France (2012, August)

The Ranking by Pairwise Comparison algorithm (RPC) is a well established label ranking method. However, its complexity is of O(N²) in the number N of labels. We present algorithms for selection, before ... [more ▼]

The Ranking by Pairwise Comparison algorithm (RPC) is a well established label ranking method. However, its complexity is of O(N²) in the number N of labels. We present algorithms for selection, before model construction, a subset of comparators of size O(N), to reduce the computational complexity without loss in accuracy. [less ▲]

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See detailWisdom of crowds for robust gene network inference
Marbach, Daniel; Costello, James C.; Küffner, Robert et al

in Nature Methods (2012), 9

Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a ... [more ▼]

Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~ 1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks. [less ▲]

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See detailDecoding spontaneous brain activity from fMRI using Gaussian Processes: tracking brain reactivation
Schrouff, Jessica ULg; Kussé, Caroline ULg; Wehenkel, Louis ULg et al

in 2012 Second International Workshop on Pattern Recognition in NeuroImaging (PRNI 2012): proceedings (2012, July 03)

While Multi-Variate Pattern Analysis techniques based on machine learning have now been regularly applied to neuroimaging data, decoding brain activity is usually performed in highly controlled ... [more ▼]

While Multi-Variate Pattern Analysis techniques based on machine learning have now been regularly applied to neuroimaging data, decoding brain activity is usually performed in highly controlled experimental paradigms. In more realistic conditions, the number, sequence and duration of mental states are unpredictably generated by the individual, resulting in complex and imbalanced fMRI data sets. Moreover, in the case of spontaneous brain activity, the mental states can not be linked to any external or internal stimulation, which makes it a highly difficult condition to decode. This study tests the classification of brain activity, acquired on 14 volunteers using fMRI, during mental imagery, a condition in which the number and duration of mental events were not externally imposed but self-generated. Application of the obtained model on rest sessions allowed classifying spontaneous brain activity linked to the task which, overall, correlated with their behavioural performance to the task. [less ▲]

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See detailL1-based compression of random forest models
Joly, Arnaud ULg; Schnitzler, François ULg; Geurts, Pierre ULg et al

in Proceeding of the 21st Belgian-Dutch Conference on Machine Learning (2012, May 24)

Random forests are effective supervised learning methods applicable to large-scale datasets. However, the space complexity of tree ensembles, in terms of their total number of nodes, is often prohibitive ... [more ▼]

Random forests are effective supervised learning methods applicable to large-scale datasets. However, the space complexity of tree ensembles, in terms of their total number of nodes, is often prohibitive, specially in the context of problems with very high-dimensional input spaces. We propose to study their compressibility by applying a L1-based regularization to the set of indicator functions defined by all their nodes. We show experimentally that preserving or even improving the model accuracy while significantly reducing its space complexity is indeed possible. [less ▲]

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