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2020 – today
- 2020
[c44]Si Yi Meng, Sharan Vaswani, Issam Hadj Laradji, Mark Schmidt, Simon Lacoste-Julien:
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. AISTATS 2020: 1375-1386- [c43]Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel:
Accelerating Smooth Games by Manipulating Spectral Shapes. AISTATS 2020: 1705-1715 - [c42]Jose Gallego-Posada, Ankit Vani, Max Schwarzer, Simon Lacoste-Julien:
GAIT: A Geometric Approach to Information Theory. AISTATS 2020: 2601-2611 - [c41]Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel:
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games. AISTATS 2020: 2863-2873 - [c40]Hugo Berard, Gauthier Gidel, Amjad Almahairi, Pascal Vincent, Simon Lacoste-Julien:
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks. ICLR 2020 - [c39]Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien:
Gradient-Based Neural DAG Learning. ICLR 2020 - [c38]Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas:
Stochastic Hamiltonian Gradient Methods for Smooth Games. ICML 2020: 6370-6381 - [c37]Avishek Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton:
Adversarial Example Games. NeurIPS 2020 - [c36]Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin:
Differentiable Causal Discovery from Interventional Data. NeurIPS 2020 - [i55]Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel:
Accelerating Smooth Games by Manipulating Spectral Shapes. CoRR abs/2001.00602 (2020) - [i54]Nicolas Loizou, Sharan Vaswani, Issam H. Laradji, Simon Lacoste-Julien:
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence. CoRR abs/2002.10542 (2020) - [i53]Rémi Le Priol, Reza Babanezhad Harikandeh, Yoshua Bengio, Simon Lacoste-Julien:
An Analysis of the Adaptation Speed of Causal Models. CoRR abs/2005.09136 (2020) - [i52]Sharan Vaswani, Reza Babanezhad, Jose Gallego, Aaron Mishkin, Simon Lacoste-Julien, Nicolas Le Roux:
To Each Optimizer a Norm, To Each Norm its Generalization. CoRR abs/2006.06821 (2020) - [i51]Sharan Vaswani, Frederik Kunstner, Issam H. Laradji, Si Yi Meng, Mark Schmidt, Simon Lacoste-Julien:
Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search). CoRR abs/2006.06835 (2020) - [i50]Avishek Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton:
Adversarial Example Games. CoRR abs/2007.00720 (2020) - [i49]Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin:
Differentiable Causal Discovery from Interventional Data. CoRR abs/2007.01754 (2020) - [i48]Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas:
Stochastic Hamiltonian Gradient Methods for Smooth Games. CoRR abs/2007.04202 (2020) - [i47]Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien:
Implicit Regularization in Deep Learning: A View from Function Space. CoRR abs/2008.00938 (2020) - [i46]Yassine Yaakoubi, Simon Lacoste-Julien, François Soumis:
Flight-connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer. CoRR abs/2009.12501 (2020) - [i45]Yassine Yaakoubi, François Soumis, Simon Lacoste-Julien:
Machine Learning in Airline Crew Pairing to Construct Initial Clusters for Dynamic Constraint Aggregation. CoRR abs/2010.00134 (2020) - [i44]Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien:
On the Convergence of Continuous Constrained Optimization for Structure Learning. CoRR abs/2011.11150 (2020) - [i43]Reza Babanezhad, Simon Lacoste-Julien:
Geometry-Aware Universal Mirror-Prox. CoRR abs/2011.11203 (2020)
2010 – 2019
- 2019
- [j4]Edouard Oyallon
, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew B. Blaschko, Eugene Belilovsky:
Scattering Networks for Hybrid Representation Learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(9): 2208-2221 (2019) - [c35]Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Rémi Le Priol, Gabriel Huang, Simon Lacoste-Julien, Ioannis Mitliagkas:
Negative Momentum for Improved Game Dynamics. AISTATS 2019: 1802-1811 - [c34]Gauthier Gidel, Hugo Berard, Gaëtan Vignoud, Pascal Vincent, Simon Lacoste-Julien:
A Variational Inequality Perspective on Generative Adversarial Networks. ICLR (Poster) 2019 - [c33]Tatjana Chavdarova, Gauthier Gidel, François Fleuret, Simon Lacoste-Julien:
Reducing Noise in GAN Training with Variance Reduced Extragradient. NeurIPS 2019: 391-401 - [c32]Gauthier Gidel, Francis R. Bach, Simon Lacoste-Julien:
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks. NeurIPS 2019: 3196-3206 - [c31]Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien:
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. NeurIPS 2019: 3727-3740 - [i42]Gabriel Huang, Hugo Larochelle, Simon Lacoste-Julien:
Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification. CoRR abs/1902.08605 (2019) - [i41]Tatjana Chavdarova, Gauthier Gidel, François Fleuret, Simon Lacoste-Julien:
Reducing Noise in GAN Training with Variance Reduced Extragradient. CoRR abs/1904.08598 (2019) - [i40]Gauthier Gidel, Francis R. Bach, Simon Lacoste-Julien:
Implicit Regularization of Discrete Gradient Dynamics in Deep Linear Neural Networks. CoRR abs/1904.13262 (2019) - [i39]Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien:
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. CoRR abs/1905.09997 (2019) - [i38]Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien:
Gradient-Based Neural DAG Learning. CoRR abs/1906.02226 (2019) - [i37]Hugo Berard, Gauthier Gidel, Amjad Almahairi, Pascal Vincent, Simon Lacoste-Julien:
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks. CoRR abs/1906.04848 (2019) - [i36]Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel:
A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games. CoRR abs/1906.05945 (2019) - [i35]Jose Gallego-Posada, Ankit Vani, Max Schwarzer, Simon Lacoste-Julien:
GEAR: Geometry-Aware Rényi Information. CoRR abs/1906.08325 (2019) - [i34]Si Yi Meng, Sharan Vaswani, Issam H. Laradji, Mark Schmidt, Simon Lacoste-Julien:
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. CoRR abs/1910.04920 (2019) - 2018
- [j3]Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien:
Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods. J. Mach. Learn. Res. 19: 81:1-81:68 (2018) - [j2]Jean-Baptiste Alayrac, Piotr Bojanowski, Nishant Agrawal, Josef Sivic, Ivan Laptev, Simon Lacoste-Julien:
Learning from Narrated Instruction Videos. IEEE Trans. Pattern Anal. Mach. Intell. 40(9): 2194-2208 (2018) - [c30]Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien:
Frank-Wolfe Splitting via Augmented Lagrangian Method. AISTATS 2018: 1456-1465 - [c29]Gabriel Huang, Hugo Berard, Ahmed Touati, Gauthier Gidel, Pascal Vincent, Simon Lacoste-Julien:
Parametric Adversarial Divergences are Good Task Losses for Generative Modeling. ICLR (Workshop) 2018 - [c28]Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin, Simon Lacoste-Julien:
SEARNN: Training RNNs with global-local losses. ICLR (Poster) 2018 - [c27]Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin:
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates. NeurIPS 2018: 667-675 - [c26]Rémi Le Priol, Alexandre Piché, Simon Lacoste-Julien:
Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields. UAI 2018: 815-824 - [i33]Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien:
Improved asynchronous parallel optimization analysis for stochastic incremental methods. CoRR abs/1801.03749 (2018) - [i32]Akram Erraqabi, Aristide Baratin, Yoshua Bengio, Simon Lacoste-Julien:
A3T: Adversarially Augmented Adversarial Training. CoRR abs/1801.04055 (2018) - [i31]Gauthier Gidel, Hugo Berard, Pascal Vincent, Simon Lacoste-Julien:
A Variational Inequality Perspective on Generative Adversarial Nets. CoRR abs/1802.10551 (2018) - [i30]Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien:
Frank-Wolfe Splitting via Augmented Lagrangian Method. CoRR abs/1804.03176 (2018) - [i29]Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Gabriel Huang, Rémi Le Priol, Simon Lacoste-Julien, Ioannis Mitliagkas:
Negative Momentum for Improved Game Dynamics. CoRR abs/1807.04740 (2018) - [i28]Eric Larsen, Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien, Andrea Lodi:
Predicting Solution Summaries to Integer Linear Programs under Imperfect Information with Machine Learning. CoRR abs/1807.11876 (2018) - [i27]Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew B. Blaschko, Eugene Belilovsky:
Scattering Networks for Hybrid Representation Learning. CoRR abs/1809.06367 (2018) - [i26]Brady Neal, Sarthak Mittal, Aristide Baratin, Vinayak Tantia, Matthew Scicluna, Simon Lacoste-Julien, Ioannis Mitliagkas:
A Modern Take on the Bias-Variance Tradeoff in Neural Networks. CoRR abs/1810.08591 (2018) - [i25]Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin:
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates. CoRR abs/1810.11544 (2018) - 2017
- [c25]Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien:
ASAGA: Asynchronous Parallel SAGA. AISTATS 2017: 46-54 - [c24]Gauthier Gidel, Tony Jebara, Simon Lacoste-Julien:
Frank-Wolfe Algorithms for Saddle Point Problems. AISTATS 2017: 362-371 - [c23]Jean-Baptiste Alayrac, Josef Sivic, Ivan Laptev, Simon Lacoste-Julien:
Joint Discovery of Object States and Manipulation Actions. ICCV 2017: 2146-2155 - [c22]Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, Simon Lacoste-Julien:
A Closer Look at Memorization in Deep Networks. ICML 2017: 233-242 - [c21]Fabian Pedregosa, Rémi Leblond, Simon Lacoste-Julien:
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization. NIPS 2017: 56-65 - [c20]Anton Osokin, Francis R. Bach, Simon Lacoste-Julien:
On Structured Prediction Theory with Calibrated Convex Surrogate Losses. NIPS 2017: 302-313 - [i24]Jean-Baptiste Alayrac, Josef Sivic, Ivan Laptev, Simon Lacoste-Julien:
Joint Discovery of Object States and Manipulating Actions. CoRR abs/1702.02738 (2017) - [i23]Anton Osokin, Francis R. Bach, Simon Lacoste-Julien:
On Structured Prediction Theory with Calibrated Convex Surrogate Losses. CoRR abs/1703.02403 (2017) - [i22]Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin, Simon Lacoste-Julien:
SEARNN: Training RNNs with Global-Local Losses. CoRR abs/1706.04499 (2017) - [i21]Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, Simon Lacoste-Julien:
A Closer Look at Memorization in Deep Networks. CoRR abs/1706.05394 (2017) - [i20]Fabian Pedregosa, Rémi Leblond, Simon Lacoste-Julien:
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization. CoRR abs/1707.06468 (2017) - [i19]Gabriel Huang, Gauthier Gidel, Hugo Berard, Ahmed Touati, Simon Lacoste-Julien:
Adversarial Divergences are Good Task Losses for Generative Modeling. CoRR abs/1708.02511 (2017) - [i18]Rémi Le Priol, Alexandre Piché, Simon Lacoste-Julien:
Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields. CoRR abs/1712.08577 (2017) - 2016
- [c19]Jean-Baptiste Alayrac, Piotr Bojanowski, Nishant Agrawal, Josef Sivic, Ivan Laptev, Simon Lacoste-Julien:
Unsupervised Learning from Narrated Instruction Videos. CVPR 2016: 4575-4583 - [c18]Anastasia Podosinnikova, Francis R. Bach, Simon Lacoste-Julien:
Beyond CCA: Moment Matching for Multi-View Models. ICML 2016: 458-467 - [c17]Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet Kumar Dokania, Simon Lacoste-Julien:
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs. ICML 2016: 593-602 - [c16]Pascal Germain, Francis R. Bach, Alexandre Lacoste, Simon Lacoste-Julien:
PAC-Bayesian Theory Meets Bayesian Inference. NIPS 2016: 1876-1884 - [i17]Anastasia Podosinnikova, Francis R. Bach, Simon Lacoste-Julien:
Beyond CCA: Moment Matching for Multi-View Models. CoRR abs/1602.09013 (2016) - [i16]Pascal Germain, Francis R. Bach, Alexandre Lacoste, Simon Lacoste-Julien:
PAC-Bayesian Theory Meets Bayesian Inference. CoRR abs/1605.08636 (2016) - [i15]Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet Kumar Dokania, Simon Lacoste-Julien:
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs. CoRR abs/1605.09346 (2016) - [i14]Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien:
Asaga: Asynchronous Parallel Saga. CoRR abs/1606.04809 (2016) - [i13]Simon Lacoste-Julien:
Convergence Rate of Frank-Wolfe for Non-Convex Objectives. CoRR abs/1607.00345 (2016) - [i12]Gauthier Gidel, Tony Jebara, Simon Lacoste-Julien:
Frank-Wolfe Algorithms for Saddle Point Problems. CoRR abs/1610.07797 (2016) - 2015
- [c15]Simon Lacoste-Julien, Fredrik Lindsten, Francis R. Bach:
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering. AISTATS 2015 - [c14]Visesh Chari, Simon Lacoste-Julien, Ivan Laptev, Josef Sivic:
On pairwise costs for network flow multi-object tracking. CVPR 2015: 5537-5545 - [c13]Simon Lacoste-Julien, Martin Jaggi:
On the Global Linear Convergence of Frank-Wolfe Optimization Variants. NIPS 2015: 496-504 - [c12]Anastasia Podosinnikova, Francis R. Bach, Simon Lacoste-Julien:
Rethinking LDA: Moment Matching for Discrete ICA. NIPS 2015: 514-522 - [c11]Rahul G. Krishnan, Simon Lacoste-Julien, David A. Sontag:
Barrier Frank-Wolfe for Marginal Inference. NIPS 2015: 532-540 - [c10]Thomas Hofmann, Aurélien Lucchi, Simon Lacoste-Julien, Brian McWilliams:
Variance Reduced Stochastic Gradient Descent with Neighbors. NIPS 2015: 2305-2313 - [i11]Simon Lacoste-Julien, Fredrik Lindsten, Francis R. Bach:
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering. CoRR abs/1501.02056 (2015) - [i10]Jean-Baptiste Alayrac, Piotr Bojanowski, Nishant Agrawal, Josef Sivic, Ivan Laptev, Simon Lacoste-Julien:
Learning from narrated instruction videos. CoRR abs/1506.09215 (2015) - [i9]Anastasia Podosinnikova, Francis R. Bach, Simon Lacoste-Julien:
Rethinking LDA: moment matching for discrete ICA. CoRR abs/1507.01784 (2015) - [i8]Rahul G. Krishnan, Simon Lacoste-Julien, David A. Sontag:
Barrier Frank-Wolfe for Marginal Inference. CoRR abs/1511.02124 (2015) - [i7]Simon Lacoste-Julien, Martin Jaggi:
On the Global Linear Convergence of Frank-Wolfe Optimization Variants. CoRR abs/1511.05932 (2015) - 2014
- [c9]Aaron Defazio, Francis R. Bach, Simon Lacoste-Julien:
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives. NIPS 2014: 1646-1654 - [i6]Aaron Defazio, Francis R. Bach, Simon Lacoste-Julien:
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives. CoRR abs/1407.0202 (2014) - [i5]Visesh Chari, Simon Lacoste-Julien, Ivan Laptev, Josef Sivic:
On Pairwise Cost for Multi-Object Network Flow Tracking. CoRR abs/1408.3304 (2014) - 2013
- [c8]Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher:
Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. ICML (1) 2013: 53-61 - [c7]Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, Zoubin Ghahramani:
SIGMa: simple greedy matching for aligning large knowledge bases. KDD 2013: 572-580 - 2012
- [c6]Francis R. Bach, Simon Lacoste-Julien, Guillaume Obozinski:
On the Equivalence between Herding and Conditional Gradient Algorithms. ICML 2012 - [i4]Francis R. Bach, Simon Lacoste-Julien, Guillaume Obozinski:
On the Equivalence between Herding and Conditional Gradient Algorithms. CoRR abs/1203.4523 (2012) - [i3]Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, Zoubin Ghahramani:
SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases. CoRR abs/1207.4525 (2012) - [i2]Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, Patrick Pletscher:
Stochastic Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. CoRR abs/1207.4747 (2012) - [i1]Simon Lacoste-Julien, Mark Schmidt, Francis R. Bach:
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method. CoRR abs/1212.2002 (2012) - 2011
- [c5]Simon Lacoste-Julien, Ferenc Huszar, Zoubin Ghahramani:
Approximate inference for the loss-calibrated Bayesian. AISTATS 2011: 416-424
2000 – 2009
- 2008
- [c4]Simon Lacoste-Julien, Fei Sha, Michael I. Jordan:
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification. NIPS 2008: 897-904 - 2006
- [j1]Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan:
Structured Prediction, Dual Extragradient and Bregman Projections. J. Mach. Learn. Res. 7: 1627-1653 (2006) - [c3]Simon Lacoste-Julien, Benjamin Taskar, Dan Klein, Michael I. Jordan:
Word Alignment via Quadratic Assignment. HLT-NAACL 2006 - 2005
- [c2]Benjamin Taskar, Simon Lacoste-Julien, Dan Klein:
A Discriminative Matching Approach to Word Alignment. HLT/EMNLP 2005: 73-80 - [c1]Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan:
Structured Prediction via the Extragradient Method. NIPS 2005: 1345-1352
Coauthor Index
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