A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits

We study the K-armed dueling bandit problem which is a variation of the classical Multi-Armed Bandit (MAB) problem in which the learner receives only relative feedback about the selected pairs of arms. We propose a new algorithm called Relative Exponential-weight algorithm for Exploration and Exploitation (REX3) to handle the adversarial utility-based formulation of this problem. This algorithm is a non-trivial extension of the Exponential-weight algorithm for Exploration and Exploitation (EXP3) algorithm. We prove a finite time expected regret upper bound of order O(sqrt(K ln(K)T)) for this algorithm and a general lower bound of order omega(sqrt(KT)). At the end, we provide experimental results using real data from information retrieval applications

Individualized HRTFs From Few Measurements: a Statistical Learning Approach

©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEEInternational audienceVirtual Auditory Space (VAS) refers to the synthesis and simulation of spatial hearing using earphones and/or a speaker system. High-fidelity VAS requires the use of individualized head-related transfer functions (HRTFs) which describe the acoustic filtering properties of the listener's external auditory periphery. HRTFs serve the increasingly dominant role of implementation 3-D audio systems, which have been realized in some commercial applications. However, the cost of a 3-D audio system cannot be brought down because the efficiency of computation, the size of memory, and the synthesis of unmeasured HRTFs remain to be made better. Because HRTFs are unique for each user depending on his morphology, the economically realist synthesis of individualized HRTFs has to rely on some measurements. This paper presents a way to reduce the cost of a 3-D audio system using a statistical modeling which allows to use only few measurements for each user

Open challenges for Machine Learning based Early Decision-Making research

More and more applications require early decisions, i.e. taken as soon as possible from partially observed data. However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time. Such a compromise between the earliness and the accuracy of decisions has been particularly studied in the field of Early Time Series Classification. This paper introduces a more general problem, called Machine Learning based Early Decision Making (ML-EDM), which consists in optimizing the decision times of models in a wide range of settings where data is collected over time. After defining the ML-EDM problem, ten challenges are identified and proposed to the scientific community to further research in this area. These challenges open important application perspectives, discussed in this paper

Model Based Co-clustering of Mixed Numerical and Binary Data

International audienceCo-clustering is a data mining technique used to extract the underlying block structure between the rows and columns of a data matrix. Many approaches have been studied and have shown their capacity to extract such structures in continuous, binary or contingency tables. However, very little work has been done to perform co-clustering on mixed type data. In this article, we extend the latent block models based co-clustering to the case of mixed data (continuous and binary variables). We then evaluate the effectiveness of the proposed approach on simulated data and we discuss its advantages and potential limits

Co-clustering based exploratory analysis of mixed-type data tables

International audienceCo-clustering is a class of unsupervised data analysis techniques that extract the existing underlying dependency structure between the instances and variables of a data table as homogeneous blocks. Most of those techniques are limited to variables of the same type. In this paper, we propose a mixed data co-clustering method based on a two-step methodology. In the first step, all the variables are binarized according to a number of bins chosen by the analyst, by equal frequency discretization in the numerical case, or keeping the most frequent values in the categorical case. The second step applies a co-clustering to the instances and the binary variables, leading to groups of instances and groups of variable parts. We apply this methodology on several data sets and compare with the results of a Multiple Correspondence Analysis applied to the same data

Application du coclustering à l'analyse exploratoire d'une table de données

International audienceThe cross-classification method is an unsupervised analysis techniquethat extracts the existing underlying structure between individuals and the variables in a data table as homogeneous blocks. This technique is limited to variables of the same type, either numerical or categorical, and we propose to extend it by proposing a two-step methodology. In the first step, all the variables are binarized according to a number of bins chosen by the analyst, by discretization in equal frequency in the numerical case, or keeping the most frequent values in the categorical case. The second step applies a coclustering method between the individuals and the binary variables, leading to groups of individual and groups of variable parts.We apply this methodology on several data sets and compare with the results of a multiple correspondence analysis MCA applied to the same data.La classification croisée est une technique d'analyse non supervisée qui permet d'extraire la structure sous-jacente existante entre les individus et les variables d'une table de données sous forme de blocs homogènes. Cette technique se limitant aux variables de même nature, soit numériques soit catégo-rielles, nous proposons de l'étendre en proposant une méthodologie en deux étapes. Lors de la première étape, toutes les variables sont binarisées selon un nombre de parties choisi par l'analyste, par discrétisation en fréquences égales dans le cas numérique ou en gardant les valeurs les plus fréquentes dans le cas catégoriel. La deuxième étape consiste à utiliser une méthode de coclustering entre individus et variables binaires, conduisant à des regroupements d'indivi-dus d'une part, et de parties de variables d'autre part. Nous appliquons cette méthodologie sur plusieurs jeux de donnée en la comparant aux résultats d'une analyse par correspondances multiples ACM, appliquée aux même données bi-narisées