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Item type:Publication, Access status: without files , Solving analogies on words based on minimal complexity transformationsAnalogies are 4-ary relations of the form “A is to B as C is to D”. When A, B and C are fixed, we call analogical equation the problem of finding the correct D. A direct applicative domain is Natural Language Processing, in which it has been shown successful on word inflections, such as conjugation or declension. If most approaches rely on the axioms of proportional analogy to solve these equations, these axioms are known to have limitations, in particular in the nature of the considered flections. In this paper, we propose an alternative approach, based on the assumption that optimal word inflections are transformations of minimal complexity. We propose a rough estimation of complexity for word analogies and an algorithm to find the optimal transformations. We illustrate our method on a large-scale benchmark dataset and compare with state-of-the-art approaches to demonstrate the interest of using complexity to solve analogies on words.Publicationtype: Conference Paper19 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Transfer Learning by Learning Projections from Target to SourceUsing transfer learning to help in solving a new classification task where labeled data is scarce is becoming popular. Numerous experiments with deep neural networks, where the representation learned on a source task is transferred to learn a target neural network, have shown the benefits of the approach. This paper, similarly, deals with hypothesis transfer learning. However, it presents a new approach where, instead of transferring a representation, the source hypothesis is kept and this is a translation from the target domain to the source domain that is learned. In a way, a change of representation is learned. We show how this method performs very well on a classification of time series task where the space of time series is changed between source and target.Publicationtype: Conference Paper29 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Adaptive collaborative topic modeling for online recommendationCollaborative ltering (CF) mainly suers from rating sparsity and from the cold-start problem. Auxiliary information like texts and images has been leveraged to alleviate these problems, resulting in hybrid recommender systems (RS). Due to the abundance of data continuously generated in real-world applications, it has become essential to design online RS that are able to handle user feedback and the availability of new items in real-time. These systems are also required to adapt to drifts when a change in the data distribution is detected. In this paper, we propose an adaptive collaborative topic modeling approach, CoAWILDA, as a hybrid system relying on adaptive online Latent Dirichlet Allocation (AWILDA) to model newly available items arriving as a document stream and incremental matrix factorization for CF. The topic model is maintained up-to-date in an online fashion and is retrained in batch when a drift is detected using documents automatically selected by an adaptive windowing technique. Our experiments on real-world datasets prove the eectiveness of our approach for online recommendation.Publicationtype: Conference Paper20 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Opening the Parallelogram: Considerations on Non-Euclidean AnalogiesAnalogical reasoning is a cognitively fundamental way of reasoning by comparing two pairs of elements. Several computational approaches are proposed to efficiently solve analogies: among them, a large number of practical methods rely on either a parallelogram representation of the analogy or, equivalently, a model of proportional analogy. In this paper, we propose to broaden this view by extending the parallelogram representation to differential manifolds, hence spaces where the notion of vectors does not exist. We show that, in this context, some classical properties of analogies do not hold any longer. We illustrate our considerations with two examples: analogies on a sphere and analogies on probability distribution manifold.Publicationtype: Conference Paper12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Adaptive Window Strategy for Topic Modeling in Document Streams(IEEE, 2018-07); ; ; Extracting global themes from a written text has recently become a major issue for computational intelligence, in particular in Natural Language Processing communities. Among all proposed solutions, Latent Dirichlet Allocation (LDA) has gained a vast interest and several variants have been proposed to adapt to changing environments. With the emergence of data streams, for instance from social media, the domain faces a new challenge: Topic extraction in real time. In this paper, we propose a simple approach called Adaptive Window based Incremental LDA (AWILDA) originating from the cross-over between LDA and state-of-the-art methods in data stream mining. We train new topic models only when a drift is detected and select training data on the fly using ADWIN algorithm. We provide both theoretical guarantees for our method and experimental validation on artificial and real-world data.Publicationtype: Conference Paper15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , An information theory based approach to multisource clusteringClustering is a compression task which consists in grouping similar objects into clusters. In real-life applications, the system may have access to several views of the same data and each view may be processed by a specific clustering algorithm: this framework is called multi-view clustering and can benefit from algorithms capable of exchanging information between the different views. In this paper, we consider this type of unsupervised ensemble learning as a compression problem and develop a theoretical framework based on algorithmic theory of information suitable for multi-view clustering and collaborative clustering applications. Using this approach, we propose a new algorithm based on solid theoretical basis, and test it on several real and artificial data sets.Publicationtype: Conference Paper20 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A complexity based approach for solving Hofstadter's analogiesAnalogical reasoning is a central problem both for human cognition and for artificial learning. Many aspects of this problem remain unsolved, though, and analogical reasoning is still a difficult task for machines. In this paper, we consider the problem of analogical reasoning and assume that the relevance of a solution can be measured by the complexity of the analogy. This hypothesis is tested in a basic alphanumeric micro-world. In order to compute complexity, we present specifications for a prototype language used to describe analogies. A few elementary operators for this language are exposed, and their complexity is discussed both from a theoretical and practical point of view. We expose several alternative definitions of relevance in analogical reasoning and show how they are related to complexity.Publicationtype: Conference Paper19 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Incremental learning with the minimum description length principleWhereas a large number of machine learning methods focus on offline learning over a single batch of data called training data set, the increasing number of automatically generated data leads to the emergence of new issues that offline learning cannot cope with. Incremental learning designates online learning of a model from streaming data. In non-stationary environments, the process generating these data may change over time, hence the learned concept becomes invalid. Adaptation to this non-stationary nature, called concept drift, is an intensively studied topic and can be reached algorithmically by two opposite approaches: active or passive approaches. We propose a formal framework to deal with concept drift, both in active and passive ways. Our framework is derived from the Minimum Description Length principle and exploits the algorithmic theory of information to quantify the model adaptation. We show that this approach is consistent with state of the art techniques and has a valid probabilistic counterpart. We propose two simple algorithms to use our framework in practice and tested both of them on real and simulated data.Publicationtype: Conference Paper22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Minimum Description Length Principle applied to structure adaptation for classification under concept drift(2016-07); Traditional supervised machine learning tests the learned classifiers on data which are drawn from the same distribution as the data used for the learning. In practice, this hypothesis does not always hold and the learned classifier has to be transferred from the space of learning data (also called source data) to the space of test data (also called target data) where it is not directly applicable. To operate this transfer, several methods aim at extracting common structural features in the source and target. Our approach employs a neural model to encode the structure of data: such a model is shown to compress the information in the sense of Kolmogorov theory of information. To perform transfer from source to target, we adapt a result shown for analogy reasoning: the structure of the source and target models are learned by applying the Minimum Description Length Principle which assumes that the chosen transformation has the shortest symbolic description on a universal Turing machine. We encounter a minimization problem over the source and target models. To describe the transfer, we develop a multi-level description of the model transformation which is used directly in the minimization of the description length. Our approach has been tested on toy examples, the difficulty of which can be controlled easily by a one-dimensional parameter and is shown to work efficiently on a wide range of problems.Publicationtype: Conference Paper22