References of "Wehenkel, Louis"
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See detailApprentissage actif par modification de la politique de décision courante
Fonteneau, Raphaël ULg; Murphy, Susan A.; Wehenkel, Louis ULg et al

in Sixièmes Journées Francophones de Planification, Décision et Apprentissage pour la conduite de systèmes (JFPDA 2011) (2011, June)

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See detailZebrafish Skeleton Measurements using Image Analysis and Machine Learning Methods
Stern, Olivier ULg; Marée, Raphaël ULg; Aceto, Jessica ULg et al

Poster (2011, May 20)

The zebrafish is a model organism for biological studies on development and gene function. Our work aims at automating the detection of the cartilage skeleton and measuring several distances and angles to ... [more ▼]

The zebrafish is a model organism for biological studies on development and gene function. Our work aims at automating the detection of the cartilage skeleton and measuring several distances and angles to quantify its development following different experimental conditions. [less ▲]

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See detailActive exploration by searching for experiments that falsify the computed control policy
Fonteneau, Raphaël ULg; Murphy, Susan; Wehenkel, Louis ULg et al

in Proceedings of the 2011 IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL-11) (2011, April)

We propose a strategy for experiment selection - in the context of reinforcement learning - based on the idea that the most interesting experiments to carry out at some stage are those that are the most ... [more ▼]

We propose a strategy for experiment selection - in the context of reinforcement learning - 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. Experiments are selected if, using the learnt environment model, they are predicted to yield a revision of the learnt control policy. Algorithms and simulation results are provided for a deterministic system with discrete action space. They show that the proposed approach is promising. [less ▲]

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See detailLooking for applications of mixtures of Markov trees in bioinformatics
Schnitzler, François ULg; Geurts, Pierre ULg; Wehenkel, Louis ULg

Scientific conference (2011, March 21)

Probabilistic graphical models (PGM) efficiently encode a probability distribution on a large set of variables. While they have already had several successful applications in biology, their poor scaling in ... [more ▼]

Probabilistic graphical models (PGM) efficiently encode a probability distribution on a large set of variables. While they have already had several successful applications in biology, their poor scaling in terms of the number of variables may make them unfit to tackle problems of increasing size. Mixtures of trees however scale well by design. Experiments on synthetic data have shown the interest of our new learning methods for this model, and we now wish to apply them to relevant problems in bioinformatics. [less ▲]

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See detailUsing Class-probability Models instead of Hard Classifiers as Base Learners in the Ranking by Pairwise Comparison Algorithm
Hiard, Samuel ULg; Wehenkel, Louis ULg

in Thatcher, Steve (Ed.) ICMLC 2011 3rd International Conference on Machine Learning and Computing Volume 1 (2011, February)

In the field of Preference Learning, the Ranking by Pairwise Comparison algorithm (RPC) consists of using the learning sample to derive pairwise comparators for each possible pair of class labels, and ... [more ▼]

In the field of Preference Learning, the Ranking by Pairwise Comparison algorithm (RPC) consists of using the learning sample to derive pairwise comparators for each possible pair of class labels, and then aggregating the predictions of the whole set of pairwise comparators for a given object in order to produce a global ranking of the class labels. In its standard form, RPC uses hard binary classifiers assigning an integer (0/1) score to each class concerned by a pairwise comparison. In the present work, we compare this setting with a modified version of RPC, where soft binary class-probability models replace the binary classifiers. To this end, we compare ensembles of extremely randomized classprobability estimation trees with ensembles of extremely randomized classification trees. We empirically show that both approaches lead to equivalent results in terms of Spearman’s rho value when using the optimal settings of their metaparameters. However, we also show that in the context of small and noisy datasets (e.g. with partial ranking information) the use of class-probability models is more robust with respect to variations of its meta-parameter values than the hard classifier ensembles. This suggests that using (soft) class-probability comparators is a sensible option in the context of RPC approaches. [less ▲]

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See detailAutomatic localization of interest points in zebrafish images with tree-based methods
Stern, Olivier ULg; Marée, Raphaël ULg; Aceto, Jessica ULg et al

in Proceedings of the 6th IAPR International Conference on Pattern Recognition in Bioinformatics (2011)

In many biological studies, scientists assess effects of experimental conditions by visual inspection of microscopy images. They are able to observe whether a protein is expressed or not, if cells are ... [more ▼]

In many biological studies, scientists assess effects of experimental conditions by visual inspection of microscopy images. They are able to observe whether a protein is expressed or not, if cells are going through normal cell cycles, how organisms evolve in different experimental conditions, etc. But, with the large number of images acquired in high-throughput experiments, this manual inspection becomes lengthy, tedious and error-prone. In this paper, we propose to automatically detect specific interest points in microscopy images using machine learning methods with the aim of performing automatic morphometric measurements in the context of Zebrafish studies. We systematically evaluate variants of ensembles of classification and regression trees on four datasets corresponding to different imaging modalities and experimental conditions. Our results show that all variants are effective, with a slight advantage for multiple output methods, which are more robust to parameter choices. [less ▲]

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See detailTowards min max generalization in reinforcement learning
Fonteneau, Raphaël ULg; Murphy, Susan; Wehenkel, Louis ULg et al

in Filipe, Joaquim; Fred, Ana; Sharp, Bernadette (Eds.) Agents and Artificial Intelligence: International Conference, ICAART 2010, Valencia, Spain, January 2010, Revised Selected Papers (2011)

In this paper, we introduce a min max approach for addressing the generalization problem in Reinforcement Learning. The min max approach works by determining a sequence of actions that maximizes the worst ... [more ▼]

In this paper, we introduce a min max approach for addressing the generalization problem in Reinforcement Learning. The min max approach works by determining a sequence of actions that maximizes the worst return that could possibly be obtained considering any dynamics and reward function compatible with the sample of trajectories and some prior knowledge on the environment. We consider the particular case of deterministic Lipschitz continuous environments over continuous state spaces, nite action spaces, and a nite optimization horizon. We discuss the non-triviality of computing an exact solution of the min max problem even after reformulating it so as to avoid search in function spaces. For addressing this problem, we propose to replace, inside this min max problem, the search for the worst environment given a sequence of actions by an expression that lower bounds the worst return that can be obtained for a given sequence of actions. This lower bound has a tightness that depends on the sample sparsity. From there, we propose an algorithm of polynomial complexity that returns a sequence of actions leading to the maximization of this lower bound. We give 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. Our experiments show that this algorithm can lead to more cautious policies than algorithms combining dynamic programming with function approximators. [less ▲]

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See detailData validation and missing data reconstruction using self-organizing map for water treatment
Lamrini, B, Lakhal, E K; Wehenkel, Louis ULg

in Neural Computing & Applications (2011), 20(4), 575-588

Applications in the water treatment domain generally rely on complex sensors located at remote sites. The processing of the corresponding measurements for generating higher-level information such as ... [more ▼]

Applications in the water treatment domain generally rely on complex sensors located at remote sites. The processing of the corresponding measurements for generating higher-level information such as optimization of coagulation dosing must therefore account for possible sensor failures and imperfect input data. In this paper, selforganizing map (SOM)-based methods are applied to multiparameter data validation and missing data reconstruction in a drinking water treatment. The SOM is a special kind of artificial neural networks that can be used for analysis and visualization of large high-dimensional data sets. It performs both in a nonlinear mapping from a high-dimensional data space to a low-dimensional space aiming to preserve the most important topological and metric relationships of the original data elements and, thus, inherently clusters the data. Combining the SOM results with those obtained by a fuzzy technique that uses marginal adequacy concept to identify the functional states (normal or abnormal), the SOM performances of validation and reconstruction process are tested successfully on the experimental data stemming from a coagulation process involved in drinking water treatment. [less ▲]

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See detailAutomatic discovery of ranking formulas for playing with multi-armed bandits
Maes, Francis ULg; Wehenkel, Louis ULg; Ernst, Damien ULg

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

We propose an approach for discovering in an automatic way formulas for ranking arms while playing with multi-armed bandits. The approach works by de ning a grammar made of basic elements such as for ... [more ▼]

We propose an approach for discovering in an automatic way formulas for ranking arms while playing with multi-armed bandits. The approach works by de ning a grammar made of basic elements such as for example addition, subtraction, the max operator, the average values of rewards collected by an arm, their standard deviation etc., and by exploiting this grammar to generate and test a large number of formulas. The systematic search for good candidate formulas is carried out by a built-on-purpose optimization algorithm used to navigate inside this large set of candidate formulas towards those that give high performances when using them on some multi-armed bandit problems. We have applied this approach on a set of bandit problems made of Bernoulli, Gaussian and truncated Gaussian distributions and have identi ed a few simple ranking formulas that provide interesting results on every problem of this set. In particular, they clearly outperform several reference policies previously introduced in the literature. We argue that these newly found formulas as well as the procedure for generating them may suggest new directions for studying bandit problems. [less ▲]

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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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