References of "Wehenkel, Louis"
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See detailOptimized look-ahead tree policies
Maes, Francis ULg; Wehenkel, Louis ULg; Ernst, Damien ULg

in Proceedings of the 9th European Workshop on Reinforcement Learning (EWRL 2011) (2011)

We consider in this paper look-ahead tree techniques for the discrete-time control of a deterministic dynamical system so as to maximize a sum of discounted rewards over an in finite time horizon. Given ... [more ▼]

We consider in this paper look-ahead tree techniques for the discrete-time control of a deterministic dynamical system so as to maximize a sum of discounted rewards over an in finite time horizon. Given the current system state xt at time t, these techniques explore the look-ahead tree representing possible evolutions of the system states and rewards conditioned on subsequent actions ut, ut+1, ... . When the computing budget is exhausted, they output the action ut that led to the best found sequence of discounted rewards. In this context, we are interested in computing good strategies for exploring the look-ahead tree. We propose a generic approach that looks for such strategies by solving an optimization problem whose objective is to compute a (budget compliant) tree-exploration strategy yielding a control policy maximizing the average return over a postulated set of initial states. This generic approach is fully speci ed to the case where the space of candidate tree-exploration strategies are "best-first" strategies parameterized by a linear combination of look-ahead path features - some of them having been advocated in the literature before - and where the optimization problem is solved by using an EDA-algorithm based on Gaussian distributions. Numerical experiments carried out on a model of the treatment of the HIV infection show that the optimized tree-exploration strategy is orders of magnitudes better than the previously advocated ones. [less ▲]

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See detailPrédiction structurée multitâche itérative de propriétés structurelles de protéines
Maes, Francis ULg; Becker, Julien ULg; Wehenkel, Louis ULg

in 7e Plateforme AFIA: Association Française pour l'Intelligence Artificielle (2011)

Le développement d'outils informatiques pour prédire de l'information structurelle de protéines à partir de la séquence en acides aminés constitue un des défis majeurs de la bioinformatique. Les problèmes ... [more ▼]

Le développement d'outils informatiques pour prédire de l'information structurelle de protéines à partir de la séquence en acides aminés constitue un des défis majeurs de la bioinformatique. Les problèmes tels que la prédiction de la structure secondaire, de l'accessibilité au solvant, ou encore la prédiction des régions désordonnées, peuvent être exprimés comme des problèmes de prédiction avec sorties structurées et sont traditionnellement résolus individuellement par des méthodes d'apprentissage automatique existantes. Etant donné que ces problèmes sont fortement liés les uns aux autres, nous proposons de les traiter ensemble par une approche d'apprentissage multitâche. A cette fin, nous introduisons un nouveau cadre d'apprentissage générique pour la prédiction structurée multitâche. Nous appliquons cette stratégie pour résoudre un ensemble de cinq tâches de prédiction de propriétés structurelles des protéines. Nos résultats expérimentaux sur deux jeux de données montrent que la stratégie proposée est significativement meilleure que les approches traitant individuellement les tâches. [less ▲]

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See detailIterative multi-task sequence labeling for predicting structural properties of proteins
Maes, Francis ULg; Becker, Julien ULg; Wehenkel, Louis ULg

in ESANN 2011 (2011)

Developing computational tools for predicting protein structural information given their amino acid sequence is of primary importance in protein science. Problems, such as the prediction of secondary ... [more ▼]

Developing computational tools for predicting protein structural information given their amino acid sequence is of primary importance in protein science. Problems, such as the prediction of secondary structures, of solvent accessibility, or of disordered regions, can be expressed as sequence labeling problems and could be solved independently by existing machine learning based sequence labeling approaches. But, since these problems are closely related, we propose to rather approach them jointly in a multi-task approach. To this end, we introduce a new generic framework for iterative multi-task sequence labeling. We apply this - conceptually simple but quite effective - strategy to jointly solve a set of five protein annotation tasks. Our empirical results with two protein datasets show that the proposed strategy significantly outperforms the single-task approaches. [less ▲]

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See detailDiscovery and biochemical characterisation of four novel biomarkers for osteoarthritis.
DE SENY, Dominique ULg; Sharif, Mohammed; Fillet, Marianne ULg et al

in Annals of the Rheumatic Diseases (2011), 70(6), 1144-52

OBJECTIVE: Knee osteoarthritis (OA) is a heterogeneous, complex joint pathology of unknown aetiology. Biomarkers have been widely used to investigate OA but currently available biomarkers lack specificity ... [more ▼]

OBJECTIVE: Knee osteoarthritis (OA) is a heterogeneous, complex joint pathology of unknown aetiology. Biomarkers have been widely used to investigate OA but currently available biomarkers lack specificity and sensitivity. Therefore, novel biomarkers are needed to better understand the pathophysiological processes of OA initiation and progression. METHODS: Surface enhanced laser desorption/ionisation-time of flight-mass spectrometry proteomic technique was used to analyse protein expression levels in 284 serum samples from patients with knee OA classified according to Kellgren and Lawrence (K&L) score (0-4). OA serum samples were also compared to serum samples provided by healthy individuals (negative control subjects; NC; n=36) and rheumatoid arthritis (RA) patients (n=25). Proteins that gave similar signal in all K&L groups of OA patients were ignored, whereas proteins with increased or decreased levels of expression were selected for further studies. RESULTS: Two proteins were found to be expressed at higher levels in sera of OA patients at all four K&L scores compared to NC and RA, and were identified as V65 vitronectin fragment and C3fpeptide. Of the two remaining proteins, one showed increased expression (unknown protein at m/z of 3762) and the other (identified as connective tissue-activating peptide III protein) was decreased in K&L scores >2 subsets compared to NC, RA and K&L scores 0 or 1 subsets. CONCLUSION: The authors detected four unexpected biomarkers (V65 vitronectin fragment, C3f peptide, CTAP-III and m/z 3762 protein) that could be relevant in the pathophysiological process of OA as having significant correlation with parameters reflecting local inflammation and bone remodelling, as well as decrease in cartilage turnover. [less ▲]

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See detailOptimal sample selection for batch-mode reinforcement learning
Rachelson, Emmanuel ULg; Schnitzler, François ULg; Wehenkel, Louis ULg et al

in Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART 2011) (2011)

We introduce the Optimal Sample Selection (OSS) meta-algorithm for solving discrete-time Optimal Control problems. This meta-algorithm maps the problem of finding a near-optimal closed-loop policy to the ... [more ▼]

We introduce the Optimal Sample Selection (OSS) meta-algorithm for solving discrete-time Optimal Control problems. This meta-algorithm maps the problem of finding a near-optimal closed-loop policy to the identification of a small set of one-step system transitions, leading to high-quality policies when used as input of a batch-mode Reinforcement Learning (RL) algorithm. We detail a particular instance of this OSS metaalgorithm that uses tree-based Fitted Q-Iteration as a batch-mode RL algorithm and Cross Entropy search as a method for navigating efficiently in the space of sample sets. The results show that this particular instance of OSS algorithms is able to identify rapidly small sample sets leading to high-quality policies [less ▲]

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See detailMultistage stochastic programming: A scenario tree based approach to planning under uncertainty
Defourny, Boris ULg; Ernst, Damien ULg; Wehenkel, Louis ULg

in Sucar, L. Enrique; Morales, Eduardo F.; Hoey, Jesse (Eds.) Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions (2011)

In this chapter, we present the multistage stochastic programming framework for sequential decision making under uncertainty. We discuss its differences with Markov Decision Processes, from the point of ... [more ▼]

In this chapter, we present the multistage stochastic programming framework for sequential decision making under uncertainty. We discuss its differences with Markov Decision Processes, from the point of view of decision models and solution algorithms. We describe the standard technique for solving approximately multistage stochastic problems, which is based on a discretization of the disturbance space called scenario tree. We insist on a critical issue of the approach: the decisions can be very sensitive to the parameters of the scenario tree, whereas no efficient tool for checking the quality of approximate solutions exists. In this chapter, we show how supervised learning techniques can be used to evaluate reliably the quality of an approximation, and thus facilitate the selection of a good scenario tree. The framework and solution techniques presented in the chapter are explained and detailed on several examples. Along the way, we define notions from decision theory that can be used to quantify, for a particular problem, the advantage of switching to a more sophisticated decision model. [less ▲]

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See detailSensitivity-based approaches for handling discrete variables in optimal power flow computations
Capitanescu, Florin ULg; Wehenkel, Louis ULg

in IEEE Transactions on Power Systems (2010), 25(4), 1780-1789

This paper proposes and compares three iterative approaches for handling discrete variables in optimal power flow (OPF) computations. The first two approaches rely on the sensitivities of the objective ... [more ▼]

This paper proposes and compares three iterative approaches for handling discrete variables in optimal power flow (OPF) computations. The first two approaches rely on the sensitivities of the objective and inequality constraints with respect to discrete variables. They set the discrete variables values either by solving a mixed-integer linear programming (MILP) problem or by using a simple procedure based on a merit function. The third approach relies on the use of Lagrange multipliers corresponding to the discrete variables bound constraints at the OPF solution. The classical round-off technique and a progressive round-off approach have been also used as a basis of comparison. We provide extensive numerical results with these approaches on four test systems with up to 1203 buses, and for two OPF problems: loss minimization and generation cost minimization, respectively. These results show that the sensitivity-based approach combined with the merit function clearly outperforms the other approaches in terms of: objective function quality, reliability, and computational times. Furthermore, the objective value obtained with this approach has been very close to that provided by the continuous relaxation OPF. This approach constitutes therefore a viable alternative to other methods dealing with discrete variables in an OPF. [less ▲]

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See detailInferring Regulatory Networks from Expression Data Using Tree-Based Methods
Huynh-Thu, Vân Anh ULg; Irrthum, Alexandre ULg; Wehenkel, Louis ULg et al

in PLoS ONE (2010), 5(9), 12776

One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray ... [more ▼]

One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article, we present GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn't make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm, based on feature selection with tree-based ensemble methods, is simple and generic, making it adaptable to other types of genomic data and interactions. [less ▲]

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See detailSub-quadratic Markov tree mixture learning based on randomizations of the Chow-Liu algorithm
Ammar, Sourour; Leray, Philippe; Schnitzler, François ULg et al

in Myllymäki, Petri; Roos, Antoine; Jaakkola, Tommi (Eds.) Proceedings of the Fifth European Workshop on Probabilistic Graphical Models (PGM-2010) (2010, September)

The present work analyzes different randomized methods to learn Markov tree mixtures for density estimation in very high-dimensional discrete spaces (very large number n of discrete variables) when the ... [more ▼]

The present work analyzes different randomized methods to learn Markov tree mixtures for density estimation in very high-dimensional discrete spaces (very large number n of discrete variables) when the sample size (N ) is very small compared to n. Several sub- quadratic relaxations of the Chow-Liu algorithm are proposed, weakening its search proce- dure. We first study na¨ıve randomizations and then gradually increase the deterministic behavior of the algorithms by trying to focus on the most interesting edges, either by retaining the best edges between models, or by inferring promising relationships between variables. We compare these methods to totally random tree generation and randomiza- tion based on bootstrap-resampling (bagging), of respectively linear and quadratic com- plexity. Our results show that randomization becomes increasingly more interesting for smaller N/n ratios, and that methods based on simultaneously discovering and exploiting the problem structure are promising in this context. [less ▲]

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See detailSecurity management under uncertainty: From day-ahead planning to intraday operation
Panciatici, Patrick; Hassaine, Thierry; Fliscounakis, S. et al

in Proceedings of Bulk Power System Dynamics and Control (iREP) - VIII (iREP), 2010 iREP Symposium (2010, August)

In this paper, we propose to analyse the practical task of dealing with uncertainty for security management by Transmission System Operators in the context of day-ahead planning and intraday operation. We ... [more ▼]

In this paper, we propose to analyse the practical task of dealing with uncertainty for security management by Transmission System Operators in the context of day-ahead planning and intraday operation. We propose a general but very abstract formalization of this task in the form of a three-stage decision making problem under uncertainties in the min-max framework, where the three stages of decision making correspond respectively to operation planning, preventive control in operation, and post-contingency emergency control. We then consider algorithmic solutions for addressing this problem in the practical context of large scale power systems by proposing a bi-level linear programming formulation adapted to the case where security is constrained by power flow limits. This formulation is illustrated on two case studies corresponding respectively to a synthetic 7-bus system and the IEEE 30-bus system. [less ▲]

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See detailConsequence driven decomposition of large-scale power system security analysis
Fonteneau, Florence ULg; Ernst, Damien ULg; Druet, Christophe et al

in Proceedings of the 2010 IREP Symposium - Bulk Power Systems Dynamics and Control - VIII (2010, August)

This paper presents an approach for assessing, in operation planning studies, the security of a large-scale power system by decomposing it into elementary subproblems, each one corresponding to a ... [more ▼]

This paper presents an approach for assessing, in operation planning studies, the security of a large-scale power system by decomposing it into elementary subproblems, each one corresponding to a structural weak-point of the system. We suppose that the structural weak-points are known a priori by the system operators, and are each one described by a set of constraints that are localized in some relatively small area of the system. The security analysis with respect to a given weak-point thus reduces to the identification of the combinations of power system configurations and disturbances that could lead to the violation of some of its constraints. We propose an iterative rare-event simulation approach for identifying such combinations among the very large set of possible ones. The procedure is illustrated on a simplified version of this problem applied to the Belgian transmission system. [less ▲]

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See detailRadar Classification based on Extra-Trees
Pisane, Jonathan ULg; Marée, Raphaël ULg; Wehenkel, Louis ULg et al

(2010, May 24)

In this paper, we describe a new automatic target recognition algorithm for classifying SAR images based on the PiXiT im- age classifier. It uses randomized sub-windows extraction and extremely randomized ... [more ▼]

In this paper, we describe a new automatic target recognition algorithm for classifying SAR images based on the PiXiT im- age classifier. It uses randomized sub-windows extraction and extremely randomized trees (extra-trees). This approach re- quires very little pre-processing of the images, thereby lim- iting the computational load. It was successfully tested on an extended version of the public standard MSTAR database, that includes targets of interest, false targets, and background clutter. A misclassification rate of about three percent has been achieved. [less ▲]

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See detailVers un apprentissage subquadratique pour les mélanges d’arbres
Schnitzler, François ULg; Leray, Philippe; Wehenkel, Louis ULg

Conference (2010, May 10)

We consider randomization schemes of the Chow-Liu algorithm from weak (bagging, of quadratic complexity) to strong ones (full random sampling, of linear complexity), for learn- ing probability density ... [more ▼]

We consider randomization schemes of the Chow-Liu algorithm from weak (bagging, of quadratic complexity) to strong ones (full random sampling, of linear complexity), for learn- ing probability density models in the form of mixtures of Markov trees. Our empirical study on high-dimensional synthetic problems shows that, while bagging is the most accurate scheme on average, some of the stronger randomizations remain very competitive in terms of accuracy, specially for small sample sizes. [less ▲]

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See detailModel-free Monte Carlo–like policy evaluation
Fonteneau, Raphaël ULg; Murphy, Susan; Wehenkel, Louis ULg et al

in Proceedings of Conférence Francophone sur l'Apprentissage Automatique (CAp) 2010 (2010, May)

We propose an algorithm for estimating the finite-horizon expected return of a closed loop control policy from an a priori given (off-policy) sample of one-step transitions. It averages cumulated rewards ... [more ▼]

We propose an algorithm for estimating the finite-horizon expected return of a closed loop control policy from an a priori given (off-policy) sample of one-step transitions. It averages cumulated rewards along a set of “broken trajectories” made of one-step transitions selected from the sample on the basis of the control policy. Under some Lipschitz continuity assumptions on the system dynamics, reward function and control policy, we provide bounds on the bias and variance of the estimator that depend only on the Lipschitz constants, on the number of broken trajectories used in the estimator, and on the sparsity of the sample of one-step transitions. [less ▲]

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See detailModel-free Monte Carlo-like policy evaluation
Fonteneau, Raphaël ULg; Murphy, Susan; Wehenkel, Louis ULg et al

in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010) (2010, May)

We propose an algorithm for estimating the finite-horizon expected return of a closed loop control policy from an a priori given (off-policy) sample of one-step transitions. It averages cumulated rewards ... [more ▼]

We propose an algorithm for estimating the finite-horizon expected return of a closed loop control policy from an a priori given (off-policy) sample of one-step transitions. It averages cumulated rewards along a set of “broken trajectories” made of one-step transitions selected from the sample on the basis of the control policy. Under some Lipschitz continuity assumptions on the system dynamics, reward function and control policy, we provide bounds on the bias and variance of the estimator that depend only on the Lipschitz constants, on the number of broken trajectories used in the estimator, and on the sparsity of the sample of one-step transitions. [less ▲]

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