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Simon Lacoste-Julien
Simon Lacoste-Julien
Associate Professor - Canada CIFAR AI Chair, University of Montreal / Mila
Zweryfikowany adres z iro.umontreal.ca - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives
A Defazio, F Bach, S Lacoste-Julien
NIPS 2014 - Advances in Neural Information Processing Systems 27, 1646-1654, 2014
19762014
A Closer Look at Memorization in Deep Networks
D Arpit, S Jastrzębski, N Ballas, D Krueger, E Bengio, MS Kanwal, ...
ICML 2017 - Proceedings of the 34th International Conference on Machine Learning, 2017
16162017
DiscLDA: Discriminative learning for dimensionality reduction and classification
S Lacoste-Julien, F Sha, MI Jordan
NIPS 2008 - Advances in Neural Information Processing Systems 21, 2008
5302008
On the Global Linear Convergence of Frank-Wolfe Optimization Variants
S Lacoste-Julien, M Jaggi
NIPS 2015 - Advances in Neural Information Processing Systems 28, 2015
4422015
Block-Coordinate Frank-Wolfe Optimization for Structural SVMs
S Lacoste-Julien, M Jaggi, M Schmidt, P Pletscher
ICML 2013 - Proceedings of the 30th International Conference on Machine …, 2013
4302013
A Variational Inequality Perspective on Generative Adversarial Nets
G Gidel, H Berard, G Vignoud, P Vincent, S Lacoste-Julien
ICLR 2019 - Seventh International Conference on Learning Representations, 2019
382*2019
Unsupervised Learning from Narrated Instruction Videos
JB Alayrac, P Bojanowski, N Agrawal, J Sivic, I Laptev, S Lacoste-Julien
CVPR 2016 - Proceedings of the IEEE Conference on Computer Vision and …, 2016
3252016
A discriminative matching approach to word alignment
B Taskar, S Lacoste-Julien, D Klein
EMNLP 2005 - Proceedings of the conference on Human Language Technology and …, 2005
2612005
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 preprint arXiv:1212.2002, 2012
2582012
Convergence Rate of Frank-Wolfe for Non-Convex Objectives
S Lacoste-Julien
arXiv preprint arXiv:1607.00345, 2016
2092016
SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases
S Lacoste-Julien, K Palla, A Davies, G Kasneci, T Graepel, Z Ghahramani
KDD 2013 - Proceedings of the 19th ACM SIGKDD international conference on …, 2013
1952013
Gradient-Based Neural DAG Learning
S Lachapelle, P Brouillard, T Deleu, S Lacoste-Julien
ICLR 2020 - Eight International Conference on Learning Representations, 2020
1882020
PAC-Bayesian Theory Meets Bayesian Inference
P Germain, F Bach, A Lacoste, S Lacoste-Julien
NIPS 2016 - Advances in Neural Information Processing Systems 29, 2016
1822016
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
S Vaswani, A Mishkin, I Laradji, M Schmidt, G Gidel, S Lacoste-Julien
NeurIPS 2019 - Advances in Neural Information Processing Systems 32, 2019
1812019
Negative Momentum for Improved Game Dynamics
G Gidel, R Askari, M Pezeshki, R Le Priol, G Huang, S Lacoste-Julien, ...
AISTATS 2019 - Proceedings of the 22nd International Conference on …, 2019
1792019
A Modern Take on the Bias-Variance Tradeoff in Neural Networks
B Neal, S Mittal, A Baratin, V Tantia, M Scicluna, S Lacoste-Julien, ...
arXiv preprint arXiv:1810.08591, 2018
1782018
On Pairwise Costs for Network Flow Multi-Object Tracking
V Chari, S Lacoste-Julien, I Laptev, J Sivic
CVPR 2015 - Proceedings of the IEEE Conference on Computer Vision and …, 2015
1712015
On the Equivalence between Herding and Conditional Gradient Algorithms
F Bach, S Lacoste-Julien, G Obozinski
ICML 2012 - Proceedings of the 29th International Conference on Machine Learning, 2012
1712012
Variance Reduced Stochastic Gradient Descent with Neighbors
T Hofmann, A Lucchi, S Lacoste-Julien, B McWilliams
NIPS 2015 - Advances in Neural Information Processing Systems 28, 2015
1602015
Structured prediction, dual extragradient and Bregman projections
B Taskar, S Lacoste-Julien, MI Jordan
JMLR - Journal of Machine Learning Research 7, 1627-1653, 2006
1472006
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