Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

Personalized privacy-aware image classification

Conference of 6th ACM International Conference on Multimedia Retrieval, ICMR 2016 ; Conference Date: 6 June 2016 Through 9 June 2016; Conference Code:122023International audienceInformation sharing in online social networks is a daily practice for billions of users. The sharing process facilitates the maintenance of users' social ties but also entails privacy disclosure in relation to other users and third parties. Depending on the intentions of the latter, this disclosure can become a risk. It is thus important to propose tools that empower the users in their relations to social networks and third parties connected to them. As part of USEMP, a coordinated research effort aimed at user empowerment, we introduce a system that performs privacy-aware classification of images. We show that generic privacy models perform badly with real-life datasets in which images are contributed by individuals because they ignore the subjective nature of privacy. Motivated by this, we develop personalized privacy classification models that, utilizing small amounts of user feedback, provide significantly better performance than generic models. The proposed semi-personalized models lead to performance improvements for the best generic model ranging from 4%, when 5 user-specific examples are provided, to 18% with 35 examples. Furthermore, by using a semantic representation space for these models we manage to provide intuitive explanations of their decisions and to gain novel insights with respect to individuals' privacy concerns stemming from image sharing. We hope that the results reported here will motivate other researchers and practitioners to propose new methods of exploiting user feedback and of explaining privacy classifications to users

USEMP: Finding diverse images at MediaEval 2015

Conference of Multimedia Benchmark Workshop, MediaEval 2015 ; Conference Date: 14 September 2015 Through 15 September 2015; Conference Code:123804International audienceWe describe the participation of the USEMP team in the Retrieving Diverse Social Images Task of MediaEval 2015. Our runs are produced based on a supervised diversification method that jointly optimizes relevance and diversity. All runs are automated and use only resources given by the task organizers. Our best results in terms of the official ranking metric on the one-topic part of the test set came by the runs that combine visual and textual information while the textual-only run performed better on the multi-topic part