References of "Marcos Alvarez, Alejandro"
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See detailOn-the-fly domain adaptation of binary classifiers
Pierard, Sébastien ULg; Marcos Alvarez, Alejandro ULg; Lejeune, Antoine ULg et al

in 23rd Belgian-Dutch Conference on Machine Learning (BENELEARN) (2014, June 06)

This work considers the on-the-fly domain adaptation of supervised binary classifiers, learned off-line, in order to adapt them to a target context. The probability density functions associated to ... [more ▼]

This work considers the on-the-fly domain adaptation of supervised binary classifiers, learned off-line, in order to adapt them to a target context. The probability density functions associated to negative and positive classes are supposed to be mixtures of the source distributions. Moreover, the mixture weights and the priors are only available at runtime. We present a theoretical solution to this problem, and demonstrate the effectiveness of the proposed approach on a real computer vision application. Our theoretical solution is applicable to any classifier approximating Bayes' classifier. [less ▲]

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See detailA Supervised Machine Learning Approach to Variable Branching in Branch-And-Bound
Marcos Alvarez, Alejandro ULg; Louveaux, Quentin ULg; Wehenkel, Louis ULg

E-print/Working paper (2014)

We present in this paper a new approach that uses supervised machine learning techniques to improve the performances of optimization algorithms in the context of mixed-integer programming (MIP). We focus ... [more ▼]

We present in this paper a new approach that uses supervised machine learning techniques to improve the performances of optimization algorithms in the context of mixed-integer programming (MIP). We focus on the branch-and-bound (B&B) algorithm, which is the traditional algorithm used to solve MIP problems. In B&B, variable branching is the key component that most conditions the efficiency of the optimization. Good branching strategies exist but are computationally expensive and usually hinder the optimization rather than improving it. Our approach consists in imitating the decisions taken by a supposedly good branching strategy, strong branching in our case, with a fast approximation. To this end, we develop a set of features describing the state of the ongoing optimization and show how supervised machine learning can be used to approximate the desired branching strategy. The approximated function is created by a supervised machine learning algorithm from a set of observed branching decisions taken by the target strategy. The experiments performed on randomly generated and standard benchmark (MIPLIB) problems show promising results. [less ▲]

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See detailImage Context Discovery from Socially Curated Contents
Kimura, Akisato; Ishiguro, Katsuhiko; Marcos Alvarez, Alejandro ULg et al

in Proceedings of the 21st ACM International Conference on Multimedia (2013, October)

This paper proposes a novel method of discovering a set of image contents sharing a specific context (attributes or implicit meaning) with the help of image collections obtained from social curation ... [more ▼]

This paper proposes a novel method of discovering a set of image contents sharing a specific context (attributes or implicit meaning) with the help of image collections obtained from social curation platforms. Socially curated contents are promising to analyze various kinds of multimedia information, since they are manually filtered and organized based on specific individual preferences, interests or perspectives. Our proposed method fully exploits the process of social curation: (1) How image contents are manually grouped together by users, and (2) how image contents are distributed in the platform. Our method reveals the fact that image contents with a specific context are naturally grouped together and every image content includes really various contexts that cannot necessarily be verbalized by texts. [less ▲]

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See detailExploiting Socially-Generated Side Information in Dimensionality Reduction
Marcos Alvarez, Alejandro ULg; Yamada, Makoto; Kimura, Akisato

in Proceedings of the 2nd International Workshop on Socially-Aware Multimedia (2013, October)

In this paper, we show how side information extracted from socially-curated data can be used within a dimensionality reduction method and to what extent this side information is beneficial to several ... [more ▼]

In this paper, we show how side information extracted from socially-curated data can be used within a dimensionality reduction method and to what extent this side information is beneficial to several tasks such as image classification, data visualization and image retrieval. The key idea is to incorporate side information of an image into a dimensionality reduction method. More specifically, we propose a dimensionality reduction method that can find an embedding transformation so that images with similar side information are close in the embedding space. We introduce three types of side information derived from user behavior. Through experiments on images from Pinterest, we show that incorporating socially-generated side information in a dimensionality reduction method benefits several image-related tasks such as image classification, data visualization and image retrieval. [less ▲]

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See detailClustering-Based Anomaly Detection in Multi-View Data
Marcos Alvarez, Alejandro ULg; Yamada, Makoto; Kimura, Akisato et al

in Proceedings of the 22nd International Conference on Information and Knowledge Management (2013, October)

This paper proposes a simple yet effective anomaly detection method for multi-view data. The proposed approach detects anomalies by comparing the neighborhoods in different views. Specifically, clustering ... [more ▼]

This paper proposes a simple yet effective anomaly detection method for multi-view data. The proposed approach detects anomalies by comparing the neighborhoods in different views. Specifically, clustering is performed separately in the different views and affinity vectors are derived for each object from the clustering results. Then, the anomalies are detected by comparing affinity vectors in the multiple views. An advantage of the proposed method over existing methods is that the tuning parameters can be determined effectively from the given data. Through experiments on synthetic and benchmark datasets, we show that the proposed method outperforms existing methods. [less ▲]

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See detailSupervised learning to tune simulated annealing for in silico protein structure prediction
Marcos Alvarez, Alejandro ULg; Maes, Francis ULg; Wehenkel, Louis ULg

in Verleysen, Michel (Ed.) ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2012, April 25)

Simulated annealing is a widely used stochastic optimization algorithm whose efficiency essentially depends on the proposal distribu- tion used to generate the next search state at each step. We propose ... [more ▼]

Simulated annealing is a widely used stochastic optimization algorithm whose efficiency essentially depends on the proposal distribu- tion used to generate the next search state at each step. We propose to adapt this distribution to a family of parametric optimization problems by using supervised machine learning on a sample of search states derived from a set of typical runs of the algorithm over this family. We apply this idea in the context of in silico protein structure prediction. [less ▲]

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See detailSupervised learning to tune simulated annealing for in silico protein structure prediction
Marcos Alvarez, Alejandro ULg

Poster (2012, February 21)

Simulated annealing is a widely used stochastic optimization algorithm whose efficiency essentially depends on the proposal distribution used to generate the next search state at each step. We propose to ... [more ▼]

Simulated annealing is a widely used stochastic optimization algorithm whose efficiency essentially depends on the proposal distribution used to generate the next search state at each step. We propose to adapt this distribution to a family of parametric optimization problems by using supervised machine learning on a sample of search states derived from a set of typical runs of the algorithm over this family. We apply this idea in the context of in silico protein structure prediction. [less ▲]

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See detailPrédiction de structures de macromolécules par apprentissage automatique
Marcos Alvarez, Alejandro ULg

Master's dissertation (2011)

Proteins are an essential constituent of cellular life whose biggest part of their function is determined by their tridimensional shape. Nowadays, however, no method is able to predict efficiently ... [more ▼]

Proteins are an essential constituent of cellular life whose biggest part of their function is determined by their tridimensional shape. Nowadays, however, no method is able to predict efficiently tridimensional protein structures based only on their amino acids sequence. We propose here an "ab initio" approach based on the concept of learning for search. Protein structure prediction is modeled in the form of an optimization problem solved by an optimization algorithm that follows an iterative framework in which a structure modification operator is selected and then applied to the current structure. The quality of the new structure is then assessed by an oracle that will determine whether or not the structure is accepted. The repetition of this framework will eventually lead to the sought structure. The critical point of this rationale lies in the choice of the modification operator, which has to be done very accurately in order to avoid the classical pitfalls of optimization problems. The operator selection step will then be subjected to machine learning thus legitimizing the term "learning for search" of the proposed method. The goal of this thesis is to show that machine learning can improve the results obtained via a simple optimization procedure. Our experiments show that this goal is fulfilled. We however know that many choices that we did should be questioned regarding both the optimization and the machine learning procedures. Finally, we can notice that the application domain of this work extends beyond the protein structure prediction problem. There exist indeed many optimization problems in the scientific literature for which no exact neither approximation algorithm exists and that are thus still very badly solved. Such problems could greatly benefit from a "learning for search" approach such as the one described in this work. [less ▲]

Detailed reference viewed: 190 (35 ULg)