I am a researcher at INRIA in the SIERRA project team which is part of the Computer Science Department of École Normale Supérieure in Paris.
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. I then worked with Zoubin Ghahramani as a postdoc in the Machine Learning Group of the University of Cambridge. In September 2011, I got a Research in Paris fellowship to work with Francis Bach in the SIERRA project team, and then I joined as a researcher in September 2013.short CV (Apr 2016) | Google Scholar citation profile
[new!] Beyond CCA: Moment Matching for Multi-View Models, A. Podosinnikova, F. Bach and S. Lacoste-Julien, arXiv:1602.09013 [stat.ML], Feb 2016.
[new!] Unsupervised Learning from Narrated Instruction Videos, J.-B. Alayrac, P. Bojanowski, N. Agrawal, I. Laptev, J. Sivic and S. Lacoste-Julien, To appear in Conference on Computer Vision and Pattern Recognition (CVPR 2016), Las Vega, USA, June 2016. [project website]
[new!] On the Global Linear Convergence of Frank-Wolfe Optimization Variants, S. Lacoste-Julien and M. Jaggi, Neural Information Processing Systems Conference (NIPS15), Montreal, Canada, December 2015. [code]
[new!] Barrier Frank-Wolfe for Marginal Inference, R. Krishnan, S. Lacoste-Julien and D. Sontag, Neural Information Processing Systems Conference (NIPS15), Montreal, Canada, December 2015. [code]
[new!] Variance Reduced Stochastic Gradient Descent with Neighbors, T. Hofmann, A. Lucchi, S. Lacoste-Julien, and Brian McWilliams, Neural Information Processing Systems Conference (NIPS15), Montreal, Canada, December 2015.
[new!] Rethinking LDA: Moment Matching for Discrete ICA, A. Podosinnikova, F. Bach and S. Lacoste-Julien, Neural Information Processing Systems Conference (NIPS15), Montreal, Canada, December 2015. [code] [project's web page]
On Pairwise Costs for Network Flow Multi-Object Tracking, V. Chari, S. Lacoste-Julien, I. Laptev and J. Sivic, Conference on Computer Vision and Pattern Recognition (CVPR 2015), Boston, USA, June 2015. [project website]
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering, S. Lacoste-Julien, F. Lindsten and F. Bach. International Conference on Artificial Intelligence and Statistics (AISTATS 2015), San Diego, California, USA, May 2015. MCMCSki IV poster prize honourable mention (2014).
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives, A. Defazio, F. Bach and S. Lacoste-Julien, Neural Information Processing Systems Conference (NIPS14), Montreal, Canada, December 2014.
An Affine Invariant Linear Convergence Analysis for Frank-Wolfe Algorithms, S. Lacoste-Julien and M. Jaggi, appeared at the NIPS 2013 Workshop on Greedy Algorithms, Frank-Wolfe and Friends, arXiv:1312.7864 [math.OC], December 2013.
SiGMa: Simple Greedy
Matching for Aligning Large Knowledge Bases, S. Lacoste-Julien, K. Palla, A. Davies, G. Kasneci,
T. Graepel and
Z. Ghahramani, 19th ACM SIGKDD International Conference on Knowledge Discovery and
Data Mining (KDD 2013), Chicago,
USA, August 2013.
Previous longer preprint: arXiv:1207.4525v1 [cs.AI], July 2012.
Block-Coordinate Frank-Wolfe Optimization for Structural SVMs, S. Lacoste-Julien*, M. Jaggi*, M. Schmidt and P. Pletscher, International Conference on Machine Learning (ICML 2013), Atlanta, USA, June 2013. *Both authors contributed equally. [code (Matlab / Octave)]
A Simpler Approach to Obtaining an O(1/t) Convergence Rate for the Projected Stochastic Subgradient Method, S. Lacoste-Julien, M. Schmidt and F. Bach, arXiv:1212.2002v2 [cs.LG], December 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.