References of "Wehenkel, Louis"
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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 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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See detailGenerating informative trajectories by using bounds on the return of control policies
Fonteneau, Raphaël ULg; Murphy, Susan; Wehenkel, Louis ULg et al

in Proceedings of the Workshop on Active Learning and Experimental Design 2010 (in conjunction with AISTATS 2010) (2010, May)

We propose new methods for guiding the generation of informative trajectories when solving discrete-time optimal control problems. These methods exploit recently published results that provide ways for ... [more ▼]

We propose new methods for guiding the generation of informative trajectories when solving discrete-time optimal control problems. These methods exploit recently published results that provide ways for computing bounds on the return of control policies from a set of trajectories. [less ▲]

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See detailTowards sub-quadratic learning of probability density models in the form of mixtures of trees
Schnitzler, François ULg; Leray, Philippe; Wehenkel, Louis ULg

(2010, April)

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 learning 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 learning 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 detailZebrafish as model in toxicology/pharmacology.
Voncken, Audrey ULg; Piot, Amandine ULg; Stern, Olivier ULg et al

Poster (2010, March 17)

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See detailIncremental Indexing and Distributed Image Search using Shared Randomized Vocabularies
Marée, Raphaël ULg; Denis, Philippe; Wehenkel, Louis ULg et al

in ACM Proceedings MIR 2010 (2010, March)

We present a cooperative framework for content-based image retrieval for the realistic setting where images are distributed across multiple cooperating servers. The proposed method is in line with bag-of ... [more ▼]

We present a cooperative framework for content-based image retrieval for the realistic setting where images are distributed across multiple cooperating servers. The proposed method is in line with bag-of-features approaches but uses fully data-independent, randomized structures, shared by the cooperating servers, to map image features to common visual words. A coherent, global image similarity measure (which is a kernel) is computed in a distributed fashion over visual words, by only requiring a small amount of data transfers between nodes. Our experiments on various image types show that this framework is a very promising step towards large-scale, distributed content-based image retrieval. [less ▲]

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See detailOptimal power flow computations with a limited number of controls allowed to move
Capitanescu, Florin ULg; Wehenkel, Louis ULg

in IEEE Transactions on Power Systems (2010), 25(1), 586-587

This letter focuses on optimal power flow (OPF) computations in which no more than a pre-specified number of controls are allowed to move. To determine an efficient subset of controls satisfying this ... [more ▼]

This letter focuses on optimal power flow (OPF) computations in which no more than a pre-specified number of controls are allowed to move. To determine an efficient subset of controls satisfying this constraint we rely on the solution of a mixed integer linear programming (MILP) problem fed with sensitivity information of controls' impact on the objective and constraints. We illustrate this approach on a 60-bus system and for the OPF problem of minimum load curtailment cost to remove thermal congestion. [less ▲]

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See detailA cautious approach to generalization in reinforcement learning
Fonteneau, Raphaël ULg; Murphy, Susan; Wehenkel, Louis ULg et al

in Proceedings of the 2nd International Conference on Agents and Artificial Intelligence (2010, January)

In the context of a deterministic Lipschitz continuous environment over continuous state spaces, finite action spaces, and a finite optimization horizon, we propose an algorithm of polynomial complexity ... [more ▼]

In the context of a deterministic Lipschitz continuous environment over continuous state spaces, finite action spaces, and a finite optimization horizon, we propose an algorithm of polynomial complexity which exploits weak prior knowledge about its environment for computing from a given sample of trajectories and for a given initial state a sequence of actions. The proposed Viterbi-like algorithm maximizes a recently proposed lower bound on the return depending on the initial state, and uses to this end prior knowledge about the environment provided in the form of upper bounds on its Lipschitz constants. It thereby avoids, in way depending on the initial state and on the prior knowledge, those regions of the state space where the sample is too sparse to make safe generalizations. Our experiments show that it can lead to more cautious policies than algorithms combining dynamic programming with function approximators. We give also a condition on the sample sparsity ensuring that, for a given initial state, the proposed algorithm produces an optimal sequence of actions in open-loop. [less ▲]

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

in 29th Benelux Meeting on Systems and Control (2010)

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See detailComputing bounds for kernel-based policy evaluation in reinforcement learning
Fonteneau, Raphaël ULg; Murphy, Susan A.; Wehenkel, Louis ULg et al

Report (2010)

This technical report proposes an approach for computing bounds on the finite-time return of a policy using kernel-based approximators from a sample of trajectories in a continuous state space and ... [more ▼]

This technical report proposes an approach for computing bounds on the finite-time return of a policy using kernel-based approximators from a sample of trajectories in a continuous state space and deterministic framework. [less ▲]

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See detailRobust Automatic Target Recognition Using Extra-trees
Pisane, Jonathan ULg; Marée, Raphaël ULg; Wehenkel, Louis ULg et al

in Pisane, Jonathan (Ed.) Robust Automatic Target Recognition Using Extra-trees (2010)

In this paper, we describe a new automatic target recognition algorithm for classifying SAR images based on the PiXiT image 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 image classifier. It uses randomized sub-windows extraction and extremely randomized trees (extra-trees). This approach requires very little pre-processing of the images, thereby limiting 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 detailEditorial - Special Section on Processing and Analysis of High-Dimensional Masses of Image and Signal Data
Charrier, Christophe; Lézoray, Olivier; Elmoataz, Abderrahim et al

in Signal Processing (2010), 90(8), 2331-2332

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See detailTree based ensemble models regularization by convex optimization
Cornélusse, Bertrand ULg; Geurts, Pierre ULg; Wehenkel, Louis ULg

Conference (2009, December 12)

Tree based ensemble methods can be seen as a way to learn a kernel from a sample of input-output pairs. This paper proposes a regularization framework to incorporate non-standard information not used in ... [more ▼]

Tree based ensemble methods can be seen as a way to learn a kernel from a sample of input-output pairs. This paper proposes a regularization framework to incorporate non-standard information not used in the kernel learning algorithm, so as to take advantage of incomplete information about output values and/or of some prior information about the problem at hand. To this end a generic convex optimization problem is formulated which is first customized into a manifold regularization approach for semi-supervised learning, then as a way to exploit censored output values, and finally as a generic way to exploit prior information about the problem. [less ▲]

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See detailLarge Margin Classification with the Progressive Hedging Algorithm
Defourny, Boris ULg; Wehenkel, Louis ULg

Conference (2009, December)

Several learning algorithms in classification and structured prediction are formulated as large scale optimization problems. We show that a generic iterative reformulation and resolving strategy based on ... [more ▼]

Several learning algorithms in classification and structured prediction are formulated as large scale optimization problems. We show that a generic iterative reformulation and resolving strategy based on the progressive hedging algorithm from stochastic programming results in a highly parallel algorithm when applied to the large margin classification problem with nonlinear kernels. We also underline promising aspects of the available analysis of progressive hedging strategies. [less ▲]

Detailed reference viewed: 38 (2 ULg)