Guillaume Obozinski

Publications

• new: Revue Bibliographique des Méthodes de Couplage des Bases de Données :
  Applications et Perspectives dans le Cas des Données de Santé Publique,
  S. K. Bounebache, C. Quentin, E. Benzenine, G. Obozinski, G. Rey, Journal de la SFDS, 159 (3), 2018 [pdf]
• Canonical Tensor Decomposition for Knowledge Base Completion,
  T. Lacroix, N. Usunier, G. Obozinski, Proceedings of the 35th International Conference on Machine Learning, 2018, In press, hal-01817595v1 [pdf]
SDCA-Powered Inexact Dual Augmented Lagrangian Method for Fast CRF Learning,
X. Hu, G. Obozinski, Proceedings of the 21st Conference on Artificial Intelligence and Statistics, 2018. [pdf][supplementary material]
• Fast column generation for atomic norm regularization, M. Vinyes, G. Obozinski, Proceedings of the 20th Conference on Artificial Intelligence and Statistics, 2017. [pdf][supplementary material]
• A unified perspective on convex structured sparsity: Hierarchical, symmetric, submodular norms and beyond, G. Obozinski, F. Bach , Preprint, 2016. [pdf]
• Cut pursuit: fast algorithms to learn piecewise constant functions on general weighted graphs, L. Landrieu, G. Obozinski, 2016. To appear in the SIAM Journal of Imaging Sciences. [pdf] [code]
• Cut pursuit: fast algorithms to learn piecewise constant functions, L. Landrieu, G. Obozinski, In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR W&CP, pp. 1384-1393, 2016. [pdf][supplementary material]
• Learning and calibrating per-location classifiers for visual place recognition P. Gronat, G. Obozinski, J. Sivic, T. Pajdla, International Journal of Computer Vision (IJCV), 118(3), pp. 319-336, 2016, DOI 10.1007/s11263-015-0878-x [pdf]
• A MRF Shape Prior for Facade Parsing with Occlusions M. Kozinski, R. Gadde, S. Zagoruyko, R. Marlet, G. Obozinski, CVPR, 2015. [pdf][supplementary material]
• Beyond procedural facade parsing: bidirectional alignment via linear programming M. Kozinski, G. Obozinski, R. Marlet, ACCV, 2014. [pdf][supplementary]
• Tight convex relaxations for sparse matrix factorization E. Richard, G. Obozinski, J.P. Vert, NIPS, 2014. [pdf][technical report]
• Continuously indexed Potts models on unoriented graphs L. Landrieu, G. Obozinski, Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2014. [pdf][appendix]
• A Markovian approach to distributional semantics w. application to semantic compositionality E. Grave, G. Obozinski, F. Bach, Proceedings of the International Conference on Computational Linguistics (COLING), 2014. [pdf]
• Domain adaptation for sequence labeling using hidden Markov models E. Grave, G. Obozinski, F. Bach, arXiv:1312.4092, 2013. Presented in the NIPS workshop "Learning accross domains and tasks". [pdf]
• Hidden Markov tree model for semantic class induction E. Grave, G. Obozinski, F. Bach, Seventeenth Conference on Computational Natural Language Learning, 2013. [pdf]
• Discussion of the paper "Grouping strategies and thresholding for high dimensional linear models" by M. Mougeot, D. Picard and K. Tribouley G. Obozinski, Journal of Statistical Planning and Inference, 143(9) pp.1441-1446, 2013. [Original paper] [Original paper arxiv preprint]
• Learning and calibrating per-location classifiers for visual place recognition P. Gronat, G. Obozinski, J. Sivic, T. Pajdla, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.907-914, 2013. [pdf]
• A latent factor model for highly multi-relational data R. Jenatton, N. Le Roux, A. Bordes, G. Obozinski, Advances in Neural Information Processing Systems 25,pp.3176-3184, 2012. [pdf][project page with code + data]
• Convex Relaxation for Combinatorial Penalties G. Obozinski, F. Bach, Technical report, HAL 00694765, 2012. [pdf]
• On the equivalence between Herding and Conditional Gradient algorithms F. Bach, S. Lacoste-Julien, G. Obozinski, Technical report, HAL 00681128, 2012. [pdf]
• Trace Lasso: a trace norm regularization for correlated designs E. Grave, G. Obozinski, F. Bach, Advances in Neural Information Processing Systems (NIPS), 24 pp.2187-2195, 2011. [arXiv technical report]
• Multi-scale Mining of fMRI Data with Hierarchical Structured Sparsity R. Jenatton, A. Gramfort, V. Michel, G. Obozinski, E. Eger, F. Bach and B. Thirion, HAL - Technical report, accepted for publication in the SIAM Journal on Imaging Sciences , 2011.
• Group Lasso with Overlaps: the Latent group Lasso approach G. Obozinski*, L. Jacob*, J.P. Vert, Technical report, HAL - inria-00628498, 2011. *contributed equally
• Optimization with sparsity-inducing penalties. F. Bach, R. Jenatton, J. Mairal and G. Obozinski, Foundations and Trends in Machine Learning, 4(1):1-106, 2012. [pdf] [cropped pdf]
• Book chapter: Convex Optimization with Sparsity-Inducing Norms. F. Bach, R. Jenatton, J. Mairal and G. Obozinski. In S. Sra, S. Nowozin, S. J. Wright., editors, Optimization for Machine Learning, MIT Press, 2011.
• Convex and Network Flow Optimization for Structured Sparsity J. Mairal*, R. Jenatton*, G. Obozinski and F. Bach. JMLR 12(Jul):2681-2730, 2011 [pdf] *contributed equally
• Proximal Methods for Hierarchical Sparse Coding R. Jenatton, J. Mairal, G. Obozinski and F. Bach. JMLR 12(Jul):2297-2334, 2011 [pdf] Former version: arXiv:1009.2139 v1, 2010.
• Network Flow Algorithms for Structured Sparsity. J. Mairal, R. Jenatton, G. Obozinski, F. Bach, Adv. Neural Information Processing Systems (NIPS), 2010. [arxiv techreport]
• Proximal Methods for Sparse Hierarchical Dictionary Learning R. Jenatton, J. Mairal, G. Obozinski and F. Bach. Proceedings of the 27th International Conference on Machine Learning (ICML-10), p.487-494, 2010.[appendix] [bibtex]
• Support Union Recovery in High-Dimensional Multivariate Regression, G. Obozinski, M.J. Wainwright, M.I. Jordan, Annals of Statistics, 39 (1) p.1-17, 2011.
• Structured Sparse Principal Component Analysis, R. Jenatton, G. Obozinski, F. Bach. International Conference on Artificial Intelligence and Statistics (AISTATS), 2010 [pdf] [code] [bib]
• • Genomic privacy and limits of individual detection in a pool , S. Sankararaman *, G. Obozinski *, M.I. Jordan, E. Halperin, Nature Genetics 41 965-967, 2009. *Contributed equally.
  • Supplementary Material containing figures, methods, and empirical and theoretical analyses of the LR-tests proposed.
  • SecureGenome software
• Group Lasso with Overlap and Graph Lasso. L. Jacob, G. Obozinski, J.-P. Vert. Proceedings of the 26th Annual International Conference on Machine Learning (ICML), p. 433-440, 2009.
• Joint covariate selection and joint subspace selection for multiple classification problems , G. Obozinski, B.Taskar, M.I. Jordan, Statistics and Computing, 20(2): 231-252, 2010.
• High-dimensional union support recovery in multivariate regression, G. Obozinski, M.J. Wainwright, M.I. Jordan, Preproceedings of the 21st International Conference on Neural Information Processing Systems (NIPS), 2009. [appendix].
• Union support recovery in high-dimensional multivariate regression, G. Obozinski, M.J. Wainwright, M.I. Jordan, Berkeley Stat. Technical Report 761 , [ pdf], August 2008.
• Consistent probabilistic output for protein function prediction, G. Obozinski, C. Grant, G. Lanckriet, M.I. Jordan, W. S. Noble, Genome Biology 2008, 9 (Suppl 1):S6.
• A critical assessment of Mus musculus gene function prediction using integrated genomic evidence, Lourdes Peña-Castillo, Murat Tasan, Chad L Myers, Hyunju Lee, Trupti Joshi, Chao Zhang, Yuanfang Guan, Michele Leone, Andrea Pagnani, Wan Kyu Kim, Chase Krumpelman, Weidong Tian, Guillaume Obozinski, Yanjun Qi, Sara Mostafavi, Guan Ning Lin, Gabriel F Berriz, Francis D Gibbons, Gert Lanckriet, Jian Qiu, Charles Grant, Zafer Barutcuoglu, David P Hill, David Warde-Farley, Chris Grouios, Debajyoti Ray, Judith A Blake, Minghua Deng, Michael I Jordan, William S Noble, Quaid Morris, Judith Klein-Seetharaman, Ziv Bar-Joseph, Ting Chen, Fengzhu Sun, Olga G Troyanskaya, Edward M Marcotte, Dong Xu, Timothy R Hughes, Frederick P Roth, Genome Biology 2008, 9(Suppl 1):S2.
• Multi-task Feature Selection, G. Obozinski, B. Taskar, M. I. Jordan, UC Berkeley Technical Report [pdf], 2007.
• Work presented at the ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning, Pittsburgh, PA, 2006.
• Imaging Brain Activation Streams from Optical Flow Computation on 2-Riemannian Manifolds , J. Lefèvre, G. Obozinski, S. Baillet, IPMI 2007: 470-481.