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Peer Reviewed
See detailLearning dextrous grasps that generalise to novel objects by combining hand and contact models
Kopicki, Marek; Detry, Renaud ULg; Schmidt, Florian et al

in IEEE International Conference on Robotics and Automation (2014)

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See detailLearning exploration/exploitation strategies for single trajectory reinforcement learning
Castronovo, Michaël ULg; Maes, Francis ULg; Fonteneau, Raphaël ULg et al

in Proceedings of the 10th European Workshop on Reinforcement Learning (EWRL 2012) (2012)

We consider the problem of learning high-performance Exploration/Exploitation (E/E) strategies for finite Markov Decision Processes (MDPs) when the MDP to be controlled is supposed to be drawn from a ... [more ▼]

We consider the problem of learning high-performance Exploration/Exploitation (E/E) strategies for finite Markov Decision Processes (MDPs) when the MDP to be controlled is supposed to be drawn from a known probability distribution pM( ). The performance criterion is the sum of discounted rewards collected by the E/E strategy over an in finite length trajectory. We propose an approach for solving this problem that works by considering a rich set of candidate E/E strategies and by looking for the one that gives the best average performances on MDPs drawn according to pM( ). As candidate E/E strategies, we consider index-based strategies parametrized by small formulas combining variables that include the estimated reward function, the number of times each transition has occurred and the optimal value functions V and Q of the estimated MDP (obtained through value iteration). The search for the best formula is formalized as a multi-armed bandit problem, each arm being associated with a formula. We experimentally compare the performances of the approach with R-max as well as with e-Greedy strategies and the results are promising. [less ▲]

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See detailLearning for exploration/exploitation in reinforcement learning
Castronovo, Michaël ULg

Master's dissertation (2012)

We consider the problem of learning high-performance Exploration/Exploitation (E/E) strategies for finite Markov Decision Processes (MDPs) when the MDP to be controlled is supposed to be drawn from a ... [more ▼]

We consider the problem of learning high-performance Exploration/Exploitation (E/E) strategies for finite Markov Decision Processes (MDPs) when the MDP to be controlled is supposed to be drawn from a known probability distribution pM(·). The performance criterion is the sum of discounted rewards collected by the E/E strategy over an infinite length trajectory. We propose an approach for solving this problem that works by considering a rich set of candidate E/E strategies and by looking for the one that gives the best average performances on MDPs drawn according to pM(·). As candidate E/E strategies, we consider index-based strategies parametrized by small formulas combining variables that include the estimated reward function, the number of times each transition has occurred and the optimal value functions ˆ V and ˆQ of the estimated MDP (obtained through value iteration). The search for the best formula is formalized as a multi-armed bandit problem, each arm being associated with a formula. We experimentally compare the performances of the approach with R-max as well as with -Greedy strategies and the results are promising. [less ▲]

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See detailLearning from Maasmechelen ou la ville comme décor
Dawans, Stéphane ULg; Houbart, Claudine ULg

in Pinto da Silva, Madalena (Ed.) EURAU12 Porto | Espaço Público e Cidade Contemporânea: Actas do 6º European Symposium on Research in Architecture and Urban Design (2012)

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See detailLearning from positive and unlabeled examples by enforcing statistical significance
Geurts, Pierre ULg

in JMLR: Workshop and Conference Proceedings (2011, April), 15

Given a finite but large set of objects de- scribed by a vector of features, only a small subset of which have been labeled as ‘positive’ with respect to a class of interest, we consider the problem of ... [more ▼]

Given a finite but large set of objects de- scribed by a vector of features, only a small subset of which have been labeled as ‘positive’ with respect to a class of interest, we consider the problem of characterizing the positive class. We formalize this as the problem of learning a feature based score function that minimizes the p-value of a non parametric statistical hypothesis test. For lin- ear score functions over the original feature space or over one of its kernelized versions, we provide a solution of this problem computed by a one-class SVM applied on a surrogate dataset obtained by sampling subsets of the overall set of objects and representing them by their average feature-vector shifted by the average feature-vector of the original sample of positive examples. We carry out experiments with this method on the prediction of targets of transcription factors in two different organisms, E. Coli and S. Cererevisiae. Our method extends enrichment analysis commonly carried out in Bioinformatics and its results outperform common solutions to this problem. [less ▲]

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See detailLearning from recombinant GH treatment in obesity
Vandeva, S.; Rixhon, M.; Tichomirova, M. A. et al

in Obesity and Metabolism (2009), 5(3/4), 156-166

Obese and untreated growth hormone deficiency (GHD) patients have a number of similar clinical and biological abnormalities. Treatment with recombinant human growth hormone (rhGH) in GHD patients has ... [more ▼]

Obese and untreated growth hormone deficiency (GHD) patients have a number of similar clinical and biological abnormalities. Treatment with recombinant human growth hormone (rhGH) in GHD patients has proven effective in beneficially modulating body composition and certain cardiovascular risk factors, thus leading to the hypothesis that administration of rhGH in obese patients could show similar beneficial results. Hyperinsulinism and increased free fatty acid levels are the main factors causing reduced GH release in the setting of obesity. We reviewed the outcomes of 25 adult and paediatric clinical studies carried out in 1987-2009 that examined the effects of rhGH administration in the obese state. Body composition showed mainly a reduction in visceral abdominal fat, whereas total bodyweight increased or remained unchanged. Effects of rhGH on lipid and carbohydrate metabolic profiles in obese patients were heterogeneous. The increasing burden of obesity on one hand, the absence of definitive medical treatment on the other, give rise to grounds for considering rhGH as a possible therapeutic option if not in the general obese population, at least in patients with higher risk of cardiovascular morbidity and mortality. [less ▲]

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See detailLearning from the Past to Improve Future Resettlement Associated with Climate Change
de Sherbinin, Alex; Castro, Marcia; Gemenne, François ULg et al

in Science (2011), 334(6055), 456-457

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See detailLearning Grasp Affordance Densities
Detry, Renaud ULg; Kraft, D.; Kroemer, O. et al

in Paladyn. Journal of Behavioral Robotics (2011), 2(1), 1--17

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See detailLearning in, through, and about participatory technology assessment: The case of nanotechnologies for tomorrow's society (NanoSoc)
Van Oudheusden, Michiel ULg

in Technology Analysis & Strategic Management (2014)

In participatory technology assessment (pTA), technical and nontechnical communities convene to share their views on a sociotechnical challenge, in an attempt to render technology research and development ... [more ▼]

In participatory technology assessment (pTA), technical and nontechnical communities convene to share their views on a sociotechnical challenge, in an attempt to render technology research and development more socially robust. Taking these commitments to transdisciplinary collaboration and co-construction of technology as entry points, this article describes key tensions that emerged in a Flemish pTA project on nanotechnologies, entitled ‘Nanotechnologies for Tomorrow’s Society’ (NanoSoc). The tensions pertain to how the terms of participation were enacted, the potentially conflicting aims embedded in the project’s mission and methods, the various roles initiating pTA researchers (social scientists) assumed throughout the project’s duration, and the deliberative-democratic rationale that sustains pTA frameworks at large. The article is a response to a pressing question posed to the author by pTA professionals, project participants, and policymakers who ask publics to partake in science and technology decision making: Now that NanoSoc is terminated, what can we learn from it? [less ▲]

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See detailLearning inclusion-optimal chordal graphs
Auvray, Vincent; Wehenkel, Louis ULg

in Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI-08) (2008, July 09)

Chordal graphs can be used to encode dependency models that are representable by both directed acyclic and undirected graphs. This paper discusses a very simple and efficient algorithm to learn the ... [more ▼]

Chordal graphs can be used to encode dependency models that are representable by both directed acyclic and undirected graphs. This paper discusses a very simple and efficient algorithm to learn the chordal structure of a probabilistic model from data. The algorithm is a greedy hill-climbing search algorithm that uses the inclusion boundary neighborhood over chordal graphs. In the limit of a large sample size and under appropriate hypotheses on the scoring criterion, we prove that the algorithm will find a structure that is inclusion-optimal when the dependency model of the data-generating distribution can be represented exactly by an undirected graph. The algorithm is evaluated on simulated datasets. [less ▲]

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See detailLearning Latin: Grammars and Annotations in Latin Papyri
Scappaticcio, Maria Chiara ULg

Conference (2012, April 14)

Detailed reference viewed: 10 (1 ULg)
See detailLearning modern languages at school in the European Union
Blondin, Christiane ULg

Book published by Office for Official Publications of the European Communities (1997)

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See detailLearning movement patterns in mobile networks: a generic method
François, Jean-Marc; Leduc, Guy ULg; Martin, Sylvain ULg

in European Wireless 2004 (2004, February)

Predicting terminals movements in mobile networks is useful for more than one reason, in particular for routing management. A way to do such prediction is to learn the movement patterns of mobile nodes ... [more ▼]

Predicting terminals movements in mobile networks is useful for more than one reason, in particular for routing management. A way to do such prediction is to learn the movement patterns of mobile nodes passing by an access router. In this paper, the information (e.g. layer 2 measurements) related to the different paths followed by mobiles are learned using a hidden Markov model. Simulations have been done using this method and show it can handle different layer~2 signals and collect statistical information when no such signal is available. Furthermore, the method works when no information is available and can be extended so as to guess the timing of the handoffs. [less ▲]

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See detailLearning new names for new stimuli : Making the connection
Thi, Jean-Pierre; Comblain, Annick ULg

Poster (1999, September)

Detailed reference viewed: 3 (1 ULg)
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See detailLearning Object-specific Grasp Affordance Densities
Detry, Renaud ULg; Başeski, Emre; Krüger, Norbert et al

in International Conference on Development and Learning (2009)

Detailed reference viewed: 13 (1 ULg)
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See detailLearning Objects and Grasp Affordances through Autonomous Exploration
Kraft, Dirk; Detry, Renaud ULg; Pugeault, Nicolas et al

in International Conference on Computer Vision Systems (2009)

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See detailLearning of Multi-Dimensional, Multi-Modal Features for Robotic Grasping
Detry, Renaud ULg

Doctoral thesis (2010)

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See detailLearning parameters in discrete naive Bayes models by computing fibers of the parametrization map
Auvray, Vincent; Wehenkel, Louis ULg

in NIPS ´08 Workshop: Algebraic and combinatorial methods in machine learning (2008, December 20)

Discrete Naive Bayes models are usually defined parametrically with a map from a parameter space to a probability distribution space. First, we present two families of algorithms that compute the set of ... [more ▼]

Discrete Naive Bayes models are usually defined parametrically with a map from a parameter space to a probability distribution space. First, we present two families of algorithms that compute the set of parameters mapped to a given discrete Naive Bayes distribution satisfying certain technical assumptions. Using these results, we then present two families of parameter learning algorithms that operate by projecting the distribution of observed relative frequencies in a dataset onto the discrete Naive Bayes model considered. They have nice convergence properties, but their computational complexity grows very quickly with the number of hidden classes of the model. [less ▲]

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See detailLearning Probabilistic Discriminative Models of Grasp Affordances under Limited Supervision
Erkan, Ayse; Kroemer, Oliver; Detry, Renaud ULg et al

in IEEE/RSJ International Conference on Intelligent Robots and Systems (2010)

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See detailLearning Reliability Models of Grid Resource Supplying
Briquet, Cyril ULg; de Marneffe, Pierre-Arnoul ULg

in Bubak, Marian; Turala, Michal; Wiatr, Kazimierz (Eds.) CGW'05 Proceedings (2005, November 22)

Resource exchange between Grid participants is at the core of Grid computing. Distributed bartering is a distributed and moneyless method of resource exchange. Recent work related to distributed bartering ... [more ▼]

Resource exchange between Grid participants is at the core of Grid computing. Distributed bartering is a distributed and moneyless method of resource exchange. Recent work related to distributed bartering has mainly dealt with resource supplying. However, Grid participants still face an unstable resource environment due to the partial and intermittent nature of the exchanged resources. The problem considered in this paper is the unreliability of resource supplying. Though it cannot be totally avoided, a proactive stance may lower its impact in the long run. We propose to explore the reduction of performance variability by improving resource consumption. The goal is to enable Grid participants to identify and avoid unreliable resource suppliers by learning reliability models of resource supplying. A Machine Learning problem is defined and the generated models are applied to select more reliable resources in the hope of improving resource consumption. [less ▲]

Detailed reference viewed: 26 (6 ULg)