Publications

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Publications of Jean-Yves Audibert

Thesis

  • J.-Y. Audibert. PAC-Bayesian aggregation and multi-armed bandits. Habilitation a Diriger les Recherches (HDR), Universite Paris Est, France, October 2010.


Articles in journal or book chapters

  • H. Sahbi, J.-Y. Audibert, and R. Keriven. Context-Dependent Kernels for Object Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(4):699-708, 2011.


  • H. Kong, J.-Y. Audibert, and J. Ponce. Detecting abandoned objects with a moving camera. IEEE Transactions on Image Processing, 9(18):2201-2210, 2010.


  • H. Kong, J.-Y. Audibert, and J. Ponce. General road detection from a single image. IEEE Transactions on Image Processing, 9(18):2211-2220, 2010.


  • J.-Y. Audibert. Fast learning rates in statistical inference through aggregation. Annals of Statistics, 37(4):1591-1646, 2009.


  • J.-Y Audibert and O. Bousquet. Combining PAC-Bayesian and generic chaining bounds. Journal of Machine Learning Research, 8:863-889, 2007.


  • J.-Y. Audibert and A. Tsybakov. Fast learning rates for plug-in classifiers. Annals of Statistics, 35(2):608-633, 2007.


  • M. Hein, J.-Y. Audibert, and U. Von Luxburg. Graph laplacians and their convergence on random neighborhood graphs. Journal of Machine Learning Research, 8:1325-1368, 2007.


Conference articles

  • M. Rodriguez, I. Laptev, J. Sivic, and J.-Y. Audibert. Density-aware person detection and tracking in crowds. In International Conference on Computer Vision, 2011.


  • M. Rodriguez, J. Sivic, I. Laptev, and J.-Y. Audibert. Data-driven Crowd Analysis. In International Conference on Computer Vision, 2011.


  • J.-Y. Audibert and S. Bubeck. Minimax policies for adversarial and stochastic bandits. In Conference on Learning Theory, June 2009.


  • H. Kong, J.-Y. Audibert, and J. Ponce. Vanishing Point Detection for Road Detection. In IEEE Conference on Computer Vision and Pattern Recognition, 2009.


  • O. Duchenne, J.-Y. Audibert, R. Keriven, J. Ponce, and F. Segonne. Segmentation by transduction. In IEEE Conference on Computer Vision and Pattern Recognition, 2008.


  • V. Mnih, Cs. Szepesvári, and J.-Y. Audibert. Empirical Bernstein stopping. In International Conference on Machine Learning, 2008.


  • H. Sahbi, J.-Y. Audibert, P. Etyngier, and R. Keriven. Context-Dependent Kernel Design for Object Matching and Recognition. In IEEE Conference on Computer Vision and Pattern Recognition, 2008.


  • H. Sahbi, P. Etyngier, J.-Y. Audibert, and R. Keriven. Manifold Learning using Robust Graph Laplacian for Interactive Image Retrieval. In IEEE Conference on Computer Vision and Pattern Recognition, 2008.


  • C. Allene, J.-Y. Audibert, J. Couprie, M.and Cousty, and R. Keriven. Some links between min-cuts, optimal spanning forests and watersheds. In Proc. 8th International Symposium on Mathematical Morphology, Rio de Janeiro, Brazil, October 2007.


  • J.-Y. Audibert. Progressive mixture rules are deviation suboptimal. In Advances in Neural Information Processing Systems, volume 20, December 2007.


  • J.-Y. Audibert, R. Munos, and C. Szepesvari. Tuning bandit algorithms in stochastic environments. In 18th International Conference on Algorithmic Learning Theory, Japon, October 2007.


  • A. Farahmand, C. Szepesvári, and J.-Y. Audibert. Manifold-adaptive dimension estimation. In Proceedings of the 24th International conference on Machine Learning, Oregon , USA, June 2007.


  • A. Farahmand, C. Szepesvári, and J.-Y. Audibert. Toward Manifold-Adaptive Learning. In NIPS Workshop on Topology learning, December 2007.


  • H. Sahbi, J.-Y. Audibert, and R. Keriven. Graph-cut transducers for relevance feedback in content based image retrieval. In ICCV, Rio de Janeiro, Brazil, October 2007.


Internal reports

  • P. Sahbi, H., Etyngier, J.-Y. Audibert, and R Keriven. Graph Laplacian for Interactive Image Retrieval. Technical report 07-32, CERTIS, April 2007.



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