Gaussian process classification and active learning with multiple annotators

31st International Conference on Machine Learning, ICML 2014

Volumn 2, Issue , 2014, Pages 1774-1786

  Rodrigues, Filipe ^a   Pereira, Francisco C ^b   Ribeiro, Bernardete ^a  

^a UNIVERSITY OF COIMBRA   (Portugal)

^b SINGAPORE MIT ALLIANCE FOR RESEARCH AND TECHNOLOGY   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); INFERENCE ENGINES; LEARNING SYSTEMS;

ACTIVE LEARNING; APPROXIMATE BAYESIAN INFERENCE; COMPUTATIONAL COSTS; GAUSSIAN PROCESS CLASSIFICATIONS; GAUSSIAN PROCESSES; MODEL DATA;

ARTIFICIAL INTELLIGENCE;

EID: 84919930803     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Bachrach, Y., Graepel, T., Minka, T., and Guiver, J. How to grade a test without knowing the answers - A Bayesian graphical model for adaptive Crowdsourcing and aptitude testing. In Proc. of the 29th Int. Conf. on Machine Learning, 2012.
2. Barber, D. Bayesian reasoning and machine learning. Cambridge University Press, 2012.
3. Chen, X., Lin, Q., and Zhou, D. Optimistic knowledge gradient policy for optimal budget allocation in crowdsourcing. In Proc. of the 30th Int. Conf. on Machine Learning, pp. 64-72, 2013.
4. Dawid, A. P. and Skene, A. M. Maximum likelihood estimation of observer error-rates using the EM algorithm. Journal of the Royal Statistical Society. Series C, 28(1): 20-28, 1979.
5. Groot, P., Birlutiu, A., and Heskes, T. Learning from multiple annotators with Gaussian processes. In Proc. of the 21st Int. Conf. on Artificial Neural Networks, volume 6792, pp. 159-164, 2011.
6. Howe, J. Crowdsourcing: why the power of the Crowd is driving the future of business. Crown Publishing Group, New York, NY, USA, 1 edition, 2008.
7. Kapoor, A., Grauman, K., Urtasun, R., and Darrell, T. Active learning with Gaussian processes for object categorization. In Int. Conf. on Computer Vision (ICCV), pp. 1-8, 2007.
8. Lawrence, N. D., Seeger, M., and Herbrich, R. Fast sparse Gaussian process methods: The informative vector machine. In Advances in Neural Information Processing Systems 15, pp. 609-616. MIT Press, 2003.
9. Liu, C. and Wang, Y. TrueLabel + Confusions: A spectrum of probabilistic models in analyzing multiple ratings. In Proc. of the 29th Int. Conf. on Machine Learning, 2012.
10. Minka, T. Expectation Propagation for approximate Bayesian inference. In Proc. of the 17th Conference in Uncertainty in Artificial Intelligence, pp. 362-369, 2001.
11. Rasmussen, C. E. and Williams, C. Gaussian processes for machine learning (Adaptive computation and machine learning). The MIT Press, 2005.
12. Raykar, V., Yu, S., Zhao, L., Valadez, G., Florin, C., Bogoni, L., and Moy, L. Learning from Crowds. Journal of Machine Learning Research, pp. 1297-1322, 2010.
13. Rodrigues, F., Pereira, F., and Ribeiro, B. Learning from multiple annotators: distinguishing good from random labelers. Pattern Recognition Letters, pp. 1428-1436, 2013a.
14. Rodrigues, F., Pereira, F., and Ribeiro, B. Sequence labeling with multiple annotators. Machine Learning, pp. 1-17, 2013b.
15. Snow, R., O'Connor, B., Jurafsky, D., and Ng, A. Cheap and fast - but is it good?: Evaluating non-expert annotations for natural language tasks. In Proc. of the Conf. on Empirical Methods in Natural Language Processing, pp. 254-263, 2008.
16. Welinder, P., Branson, S., Belongie, S., and Perona, P. The multidimensional wisdom of crowds. In Advances in Neural Information Processing Systems 23, pp. 2424- 2432, 2010.
17. Wu, O., Hu, W., and Gao, I. Learning to rank under multiple annotators. In Proc. of the 22nd Int. Joint Conf. on Artificial Intelligence, pp. 1571-1576, 2011.
18. Yan, Y., Rosales, R., Fung, G., Schmidt, M., Valadez, G., Bogoni, L., Moy, L., and Dy, J. Modeling annotator expertise: Learning when everybody knows a bit of something. Journal of Machine Learning Research, 9:932- 939, 2010.
19. Yan, Y., Rosales, R., Fung, G., and Dy, J. Active learning from Crowds. In Proc. of the 28th Int. Conf. on Machine Learning, pp. 1161-1168, 2011.