Recently, new promising theoretical results, techniques, and methodologies have attracted the attention of many researchers and have allowed to broaden the range of applications in which machine learning can be effectively applied in order to extract useful and actionable infor- mation from the huge amount of heterogeneous data produced everyday by an increasingly digital world. Examples of these methods and prob- lems are: learning under privacy and anonymity constraints, learning from structured, semi-structured, multi-modal (heterogeneous) data, construc- tive machine learning, reliable machine learning, learning to learn, mixing deep and structured learning, semantics-enabled recommender systems, re- producibility and interpretability in machine learning, human-in-the-loop, adversarial learning. The focus of this special session is to attract both solid contributions or preliminary results which show the potentiality and the limitations of new ideas, refinements, or contaminations between the different fields of machine learning and other fields of research in solving real world problems. Both theoretical and practical results are welcome to our special session.
However, it is often unclear how to handle these data properly in the case that the data contains sensitive information. Dierential privacy has become a powerful principle for privacy-preserving data analysis tasks in the last few years, since it entails a formal privacy guarantee for such settings. This is obtained by a separation of the utility of the database and the risk of an individual to lose his/her privacy. In this paper, authors introduced the Laplace mechanism and a stochastic gradient descent methodology which guarantee dierential privacy . Then, authors show how these paradigms can be incorporated into two popular machine learning algorithm, namely GLVQ and GMLVQ. Authors demonstrate the results of privacy-preserving LVQ based on three benchmarks.
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The fourth work accepted in our SS in entitled On aggregation in ranking median regression . In this work authors observed that the present era of personalized customer services and recommender systems, predicting the preferences of an individual/user over a set of items indexed by [[n]] = {1, , n}, n 1, based on its characteristics, modelled as a r.v. X say, is an ubiquitous issue. Though easy to state, this predictive problem referered to as ranking median regression (RMR in short) is very dicult to solve in practice. The major challenge lies in the fact that, here, the (discrete) output space is the symmetric group Sn, composed of all permutations of [[n]] , of explosive cardinality n!, and which is not a subset of a vector space. It is thus far from straightforward to build predictive rules taking their values in Sn, except by means of ranking aggregation techniques implemented at a local level, as proposed in or . However, such local learning techniques exhibi
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Beyond a theoretical analysis establishing its validity, the relevance of this novel ensemble learning technique is supported by experimental results.
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The fth work accepted in our SS in entitled LANN-DSVD: A new privacypreserving distributed algorithm for machine learning . In this work authors observed that in the Big Data era new challenges have arisen in machine learning related with the Volume (high number of samples or variables), the Velocity, etc. making many of the classic and brilliant methods not applicable. One main concern derives from Privacy issues when data is distributed and cannot be shared among locations. In their work, authors present LANN-DSVD, a non iterative algorithm for One-Layer Neural Networks that allows distributed learning guaranteeing privacy. Moreover, it is non iterative, parameter-free and provides incremental learning, thus making it very suitable to manage huge and/or continuous data. Results demonstrate its competitiveness both in eciency and ecacy.
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