%Aigaion2 BibTeX export from Idiap Publications
%Thursday 21 November 2024 11:41:19 AM

@INPROCEEDINGS{Luo_NIPS10_2010,
         author = {Luo, Jie and Orabona, Francesco},
       projects = {Idiap, DIRAC},
          month = {12},
          title = {Learning from Candidate Labeling Sets},
      booktitle = {Advances in Neural Information Processing Systems 23 (NIPS10)},
         volume = {23},
           year = {2010},
      publisher = {MIT Press},
       location = {Vancouver, B.C., Canada},
   organization = {NIPS Foundation},
       crossref = {Luo_Idiap-RR-27-2011},
       abstract = {In many real world applications we do not have access to fully-labeled training data, but only to a list of possible labels. This is the case, e.g., when learning visual classifiers from images downloaded from the web, using just their text captions or tags as learning oracles. In general, these problems can be very difficult. However most of the time there exist different implicit sources of information, coming from the relations between instances and labels, which are usually dismissed. In this paper, we propose a semi-supervised framework to model this kind of problems. Each training sample is a bag containing multi-instances, associated with a set of candidate labeling vectors. Each labeling vector encodes the possible labels for the instances in the bag, with only one being fully correct. The use of the labeling vectors provides a principled way not to exclude any information. We propose a large margin discriminative formulation, and an efficient algorithm to solve it. Experiments conducted on artificial datasets and a real-world images and captions dataset show that our approach achieves performance comparable to an SVM trained with the ground-truth labels, and outperforms other baselines.},
            pdf = {https://publications.idiap.ch/attachments/papers/2011/Luo_NIPS10_2010.pdf}
}



crossreferenced publications: 
@TECHREPORT{Luo_Idiap-RR-27-2011,
         author = {Luo, Jie and Orabona, Francesco},
       projects = {Idiap, DIRAC},
          month = {8},
          title = {Learning from Candidate Labeling Sets},
           type = {Idiap-RR},
         number = {Idiap-RR-27-2011},
           year = {2011},
    institution = {Idiap},
       abstract = {In many real world applications we do not have access to fully-labeled training data, but only to a list of possible labels. This is the case, e.g., when learning visual classifiers from images downloaded from the web, using just their text captions or tags as learning oracles. In general, these problems can be very difficult. However most of the time there exist different implicit sources of information, coming from the relations between instances and labels, which are usually dismissed. In this paper, we propose a semi-supervised framework to model this kind of problems. Each training sample is a bag containing multi-instances, associated with a set of candidate labeling vectors. Each labeling vector encodes the possible labels for the instances in the bag, with only one being fully correct. The use of the labeling vectors provides a principled way not to exclude any information. We propose a large margin discriminative formulation, and an efficient algorithm to solve it. Experiments conducted on artificial datasets and two images and captions datasets show that our approach achieves performance comparable to SVM trained with the ground-truth labels, and outperforms other baselines.},
            pdf = {https://publications.idiap.ch/attachments/reports/2010/Luo_Idiap-RR-27-2011.pdf}
}