Simon Lacoste-Julien homepage

Simon Lacoste-Julien

INRIA - SIERRA project-team
Laboratoire d'Informatique de l'École Normale Supérieure
23, avenue d'Italie
75013, Paris, France
office: 561 -- directions

To send me a message, build my address by first using firstname.lastname (as written on my webpage -- don't forget the hypen between the two last names!), and then use as server ensDOTfr.

Since September 2011, I am a Research in Paris fellow working with Francis Bach in the SIERRA project team, part of INRIA and the Computer Science Department of École Normale Supérieure in Paris. Before that, I worked with Zoubin Ghahramani as a postdoc in the Machine Learning Group of the University of Cambridge. I did my PhD in Computer Science at the University of California, Berkeley under the supervision of Michael I. Jordan, and (basically) a B.Sc. Triple Honours in Mathematics, Physics and Computer Science at McGill University.

CV (Dec 2012) | Google Scholar citation profile

Research Interests

Structured prediction
Latent variable models
Convex optimization
Approximate inference
Combining generative and discriminative methods / frequentist and Bayesian statistics -- see the NIPS 2009 workshop on The Generative and Discriminative Learning Interface that I have co-organized, and my recent AISTATS 2011 paper on the loss-calibrated Bayesian below.
Applications: NLP, information retrieval, computer vision, computational biology

Papers

Block-Coordinate Frank-Wolfe Optimization for Structural SVMs, S. Lacoste-Julien*, M. Jaggi*, M. Schmidt, P. Pletscher, To appear at the International Conference on Machine Learning (ICML 2013), Atlanta, USA, June 2013. *Both authors contributed equally.

A Simpler Approach to Obtaining an O(1/t) Convergence Rate for the Projected Stochastic Subgradient Method, S. Lacoste-Julien, M. Schmidt, F. Bach, arXiv:1212.2002v2 [cs.LG], December 2012.

SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases, S. Lacoste-Julien, K. Palla, A. Davies, G. Kasneci, T. Graepel, Z. Ghahramani, arXiv:1207.4525v1 [cs.AI], July 2012.

On the Equivalence between Herding and Conditional Gradient Algorithms, F. Bach, S. Lacoste-Julien and G. Obozinski, International Conference on Machine Learning (ICML 2012), Edinburgh, UK, June 2012.

Approximate Gaussian Integration using Expectation Propagation, J.P. Cunningham, P. Hennig and S. Lacoste-Julien, arXiv:11111.6832v1 [stat.ML], November 2011.

A Kernel Approach to Tractable Bayesian Nonparametrics., F. Huszár and S. Lacoste-Julien, arXiv:1103.1761v3, [stat.ML], March 2011.

Approximate Inference for the Loss-Calibrated Bayesian, S. Lacoste-Julien, F. Huszár, and Z. Ghahramani, International Conference on Artificial Intelligence and Statistics (AISTATS11), Florida, April 2011.

Discriminative Machine Learning with Structure, S. Lacoste-Julien, PhD Thesis, University of California, Berkeley, 2009.

DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification. S. Lacoste-Julien, F. Sha, and M. Jordan, Neural Information Processing Systems Conference (NIPS08), Vancouver, British Columbia, December 2008.

Word Alignment via Quadratic Assignment. S. Lacoste-Julien, B. Taskar, D. Klein, and M. Jordan, Human Language Technology conference - North American chapter of the Association for Computational Linguistics (HLT-NAACL06), New York, June 2006.

Structured Prediction, Dual Extragradient and Bregman Projections. B. Taskar, S. Lacoste-Julien, and M. Jordan, Journal of Machine Learning Research (JMLR), Special Topic on Machine Learning and Large Scale Optimization, 7, 1627-1653, 2006.

Structured Prediction via the Extragradient Method. B. Taskar, S. Lacoste-Julien, and M. Jordan, Neural Information Processing Systems Conference (NIPS05), Vancouver, British Columbia, December 2005. [Longer version]

A Discriminative Matching Approach to Word Alignment. B. Taskar, S. Lacoste-Julien, and D. Klein, Empirical Methods in Natural Language Processing (EMNLP05), Vancouver, British Columbia, October 2005.

Meta-Modelling Hybrid Formalisms. S. Lacoste-Julien, H. Vangheluwe, J. de Lara and P. Mosterman, IEEE International Symposium on Computer Aided Control System Design, special section on multi-paradigm modelling. Taiwan, September 2004.

A UC Berkeley class project which has been cited a few times as a tutorial: An introduction to Max-Margin Markov Networks. S. Lacoste-Julien, 2003.