Browse ORBi by ORBi project

- Background
- Content
- Benefits and challenges
- Legal aspects
- Functions and services
- Team
- Help and tutorials

Sensitivity-based approaches for handling discrete variables in optimal power flow computations Capitanescu, Florin ; Wehenkel, Louis 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 ▲] Detailed reference viewed: 65 (5 ULg)Inferring Regulatory Networks from Expression Data using Tree-based Methods Huynh-Thu, Vân Anh ; Irrthum, Alexandre ; Wehenkel, Louis et al Conference (2010, October) Detailed reference viewed: 62 (8 ULg)Inferring Regulatory Networks from Expression Data Using Tree-Based Methods Huynh-Thu, Vân Anh ; Irrthum, Alexandre ; Wehenkel, Louis 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 ▲] Detailed reference viewed: 413 (43 ULg)Inferring regulatory networks from expression data using tree-based methods Huynh-Thu, Vân Anh ; Irrthum, Alexandre ; Wehenkel, Louis et al Poster (2010, September) Detailed reference viewed: 20 (12 ULg)Sub-quadratic Markov tree mixture learning based on randomizations of the Chow-Liu algorithm ; ; Schnitzler, François 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 ▲] Detailed reference viewed: 40 (19 ULg)Security management under uncertainty: From day-ahead planning to intraday operation ; ; 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 ▲] Detailed reference viewed: 10 (2 ULg)Consequence driven decomposition of large-scale power system security analysis Fonteneau, Florence ; Ernst, Damien ; 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 ▲] Detailed reference viewed: 64 (13 ULg)Radar Classification based on Extra-Trees Pisane, Jonathan ; Marée, Raphaël ; Wehenkel, Louis 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 ▲] Detailed reference viewed: 72 (10 ULg)Vers un apprentissage subquadratique pour les mélanges d’arbres Schnitzler, François ; ; Wehenkel, Louis 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 ▲] Detailed reference viewed: 45 (16 ULg)Model-free Monte Carlo–like policy evaluation Fonteneau, Raphaël ; ; Wehenkel, Louis 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 ▲] Detailed reference viewed: 25 (10 ULg)Model-free Monte Carlo-like policy evaluation Fonteneau, Raphaël ; ; Wehenkel, Louis 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 ▲] Detailed reference viewed: 82 (17 ULg)Generating informative trajectories by using bounds on the return of control policies Fonteneau, Raphaël ; ; Wehenkel, Louis 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 ▲] Detailed reference viewed: 37 (12 ULg)Towards sub-quadratic learning of probability density models in the form of mixtures of trees Schnitzler, François ; ; Wehenkel, Louis (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 ▲] Detailed reference viewed: 70 (21 ULg)Zebrafish as model in toxicology/pharmacology. Voncken, Audrey ; Piot, Amandine ; Stern, Olivier et al Poster (2010, March 17) Detailed reference viewed: 87 (35 ULg)Incremental Indexing and Distributed Image Search using Shared Randomized Vocabularies Marée, Raphaël ; ; Wehenkel, Louis 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 ▲] Detailed reference viewed: 137 (18 ULg)Optimal power flow computations with a limited number of controls allowed to move Capitanescu, Florin ; Wehenkel, Louis 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 ▲] Detailed reference viewed: 69 (7 ULg)A cautious approach to generalization in reinforcement learning Fonteneau, Raphaël ; ; Wehenkel, Louis 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 ▲] Detailed reference viewed: 130 (25 ULg)Model-free Monte Carlo-like policy evaluation Fonteneau, Raphaël ; ; Wehenkel, Louis et al in 29th Benelux Meeting on Systems and Control (2010) Detailed reference viewed: 13 (1 ULg)Computing bounds for kernel-based policy evaluation in reinforcement learning Fonteneau, Raphaël ; ; Wehenkel, Louis 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 ▲] Detailed reference viewed: 18 (3 ULg)Robust Automatic Target Recognition Using Extra-trees Pisane, Jonathan ; Marée, Raphaël ; Wehenkel, Louis 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 ▲] Detailed reference viewed: 121 (5 ULg) |
||