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2020 – today
- 2024
[j10]Alexia Jolicoeur-Martineau, Emy Gervais, Kilian Fatras, Yan Zhang, Simon Lacoste-Julien:
PopulAtion Parameter Averaging (PAPA). Trans. Mach. Learn. Res. 2024 (2024)- [j9]Pranshu Malviya, Gonçalo Mordido, Aristide Baratin, Reza Babanezhad Harikandeh, Jerry Huang, Simon Lacoste-Julien, Razvan Pascanu, Sarath Chandar:
Promoting Exploration in Memory-Augmented Adam using Critical Momenta. Trans. Mach. Learn. Res. 2024 (2024) - [c68]Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh, Simon Lacoste-Julien:
Weight-Sharing Regularization. AISTATS 2024: 4204-4212 - [c67]Vitória Barin Pacela, Kartik Ahuja, Simon Lacoste-Julien, Pascal Vincent:
On the Identifiability of Quantized Factors. CLeaR 2024: 384-422 - [c66]Meraj Hashemizadeh, Juan Ramirez, Rohan Sukumaran, Golnoosh Farnadi, Simon Lacoste-Julien, Jose Gallego-Posada:
Balancing Act: Constraining Disparate Impact in Sparse Models. ICLR 2024 - [c65]Motahareh Sohrabi, Juan Ramirez, Tianyue H. Zhang, Simon Lacoste-Julien, Jose Gallego-Posada:
On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization. ICML 2024 - [i83]Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien:
Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies. CoRR abs/2401.04890 (2024) - [i82]Motahareh Sohrabi, Juan Ramirez, Tianyue H. Zhang, Simon Lacoste-Julien, Jose Gallego-Posada:
On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization. CoRR abs/2406.04558 (2024) - [i81]António Góis, Mehrnaz Mofakhami, Fernando P. Santos, Simon Lacoste-Julien, Gauthier Gidel:
Performative Prediction on Games and Mechanism Design. CoRR abs/2408.05146 (2024) - [i80]Boris Knyazev, Abhinav Moudgil, Guillaume Lajoie, Eugene Belilovsky, Simon Lacoste-Julien:
Accelerating Training with Neuron Interaction and Nowcasting Networks. CoRR abs/2409.04434 (2024) - [i79]Lucas Maes, Tianyue H. Zhang, Alexia Jolicoeur-Martineau, Ioannis Mitliagkas, Damien Scieur, Simon Lacoste-Julien, Charles Guille-Escuret:
Understanding Adam Requires Better Rotation Dependent Assumptions. CoRR abs/2410.19964 (2024) - 2023
- [j8]Gabriel Huang
, Issam H. Laradji, David Vázquez, Simon Lacoste-Julien, Pau Rodríguez:
A Survey of Self-Supervised and Few-Shot Object Detection. IEEE Trans. Pattern Anal. Mach. Intell. 45(4): 4071-4089 (2023) - [c64]Jihye Kim, Aristide Baratin, Yan Zhang, Simon Lacoste-Julien:
CrossSplit: Mitigating Label Noise Memorization through Data Splitting. ICML 2023: 16377-16392 - [c63]Boris Knyazev, Doha Hwang, Simon Lacoste-Julien:
Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models? ICML 2023: 17243-17259 - [c62]Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand:
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning. ICML 2023: 18171-18206 - [c61]Yan Zhang, David W. Zhang, Simon Lacoste-Julien, Gertjan J. Burghouts, Cees G. M. Snoek:
Unlocking Slot Attention by Changing Optimal Transport Costs. ICML 2023: 41931-41951 - [c60]Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, Simon Lacoste-Julien:
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation. NeurIPS 2023 - [i78]Yan Zhang, David W. Zhang, Simon Lacoste-Julien, Gertjan J. Burghouts, Cees G. M. Snoek:
Unlocking Slot Attention by Changing Optimal Transport Costs. CoRR abs/2301.13197 (2023) - [i77]Boris Knyazev, Doha Hwang, Simon Lacoste-Julien:
Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models? CoRR abs/2303.04143 (2023) - [i76]Alexia Jolicoeur-Martineau, Emy Gervais, Kilian Fatras, Yan Zhang, Simon Lacoste-Julien:
PopulAtion Parameter Averaging (PAPA). CoRR abs/2304.03094 (2023) - [i75]Vitória Barin Pacela, Kartik Ahuja, Simon Lacoste-Julien, Pascal Vincent:
Identifiability of Discretized Latent Coordinate Systems via Density Landmarks Detection. CoRR abs/2306.16334 (2023) - [i74]Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, Simon Lacoste-Julien:
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation. CoRR abs/2307.02598 (2023) - [i73]Pranshu Malviya, Gonçalo Mordido, Aristide Baratin, Reza Babanezhad Harikandeh, Jerry Huang, Simon Lacoste-Julien, Razvan Pascanu, Sarath Chandar:
Promoting Exploration in Memory-Augmented Adam using Critical Momenta. CoRR abs/2307.09638 (2023) - [i72]Meraj Hashemizadeh, Juan Ramirez, Rohan Sukumaran, Golnoosh Farnadi, Simon Lacoste-Julien, Jose Gallego-Posada:
Balancing Act: Constraining Disparate Impact in Sparse Models. CoRR abs/2310.20673 (2023) - [i71]Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh, Simon Lacoste-Julien:
Weight-Sharing Regularization. CoRR abs/2311.03096 (2023) - 2022
- [j7]Eric Larsen, Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien, Andrea Lodi:
Predicting Tactical Solutions to Operational Planning Problems Under Imperfect Information. INFORMS J. Comput. 34(1): 227-242 (2022) - [j6]Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien:
SVRG meets AdaGrad: painless variance reduction. Mach. Learn. 111(12): 4359-4409 (2022) - [c59]Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang:
On the Convergence of Continuous Constrained Optimization for Structure Learning. AISTATS 2022: 8176-8198 - [c58]Sébastien Lachapelle, Pau Rodríguez, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien:
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA. CLeaR 2022: 428-484 - [c57]Andjela Mladenovic, Avishek Joey Bose, Hugo Berard, William L. Hamilton, Simon Lacoste-Julien, Pascal Vincent, Gauthier Gidel:
Online Adversarial Attacks. ICLR 2022 - [c56]Yan Zhang, David W. Zhang, Simon Lacoste-Julien, Gertjan J. Burghouts, Cees G. M. Snoek:
Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation. ICLR 2022 - [c55]Jose Gallego-Posada, Juan Ramirez, Akram Erraqabi, Yoshua Bengio, Simon Lacoste-Julien:
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints. NeurIPS 2022 - [c54]Marwa El Halabi, Suraj Srinivas, Simon Lacoste-Julien:
Data-Efficient Structured Pruning via Submodular Optimization. NeurIPS 2022 - [c53]Antonio Orvieto, Simon Lacoste-Julien, Nicolas Loizou:
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution. NeurIPS 2022 - [c52]Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, Yoshua Bengio:
Bayesian structure learning with generative flow networks. UAI 2022: 518-528 - [i70]Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, Yoshua Bengio:
Bayesian Structure Learning with Generative Flow Networks. CoRR abs/2202.13903 (2022) - [i69]Marwa El Halabi, Suraj Srinivas, Simon Lacoste-Julien:
Data-Efficient Structured Pruning via Submodular Optimization. CoRR abs/2203.04940 (2022) - [i68]Sébastien Lachapelle, Simon Lacoste-Julien:
Partial Disentanglement via Mechanism Sparsity. CoRR abs/2207.07732 (2022) - [i67]Jose Gallego-Posada, Juan Ramirez, Akram Erraqabi, Yoshua Bengio, Simon Lacoste-Julien:
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints. CoRR abs/2208.04425 (2022) - [i66]Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand:
Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective. CoRR abs/2211.14666 (2022) - [i65]Jihye Kim, Aristide Baratin, Yan Zhang, Simon Lacoste-Julien:
CrossSplit: Mitigating Label Noise Memorization through Data Splitting. CoRR abs/2212.01674 (2022) - 2021
- [c51]Rémi Le Priol, Reza Babanezhad, Yoshua Bengio, Simon Lacoste-Julien:
An Analysis of the Adaptation Speed of Causal Models. AISTATS 2021: 775-783 - [c50]Nicolas Loizou, Sharan Vaswani, Issam Hadj Laradji, Simon Lacoste-Julien:
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence. AISTATS 2021: 1306-1314 - [c49]Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien:
Implicit Regularization via Neural Feature Alignment. AISTATS 2021: 2269-2277 - [c48]Namyeong Kwon, Hwidong Na, Gabriel Huang, Simon Lacoste-Julien:
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning. ICLR 2021 - [c47]Thomas Kerdreux, Lewis Liu, Simon Lacoste-Julien, Damien Scieur:
Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets. ICML 2021: 5398-5408 - [c46]Yassine Yaakoubi, François Soumis, Simon Lacoste-Julien:
Structured Convolutional Kernel Networks for Airline Crew Scheduling. ICML 2021: 11626-11636 - [c45]Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien:
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity. NeurIPS 2021: 19095-19108 - [i64]Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien:
SVRG Meets AdaGrad: Painless Variance Reduction. CoRR abs/2102.09645 (2021) - [i63]Andjela Mladenovic, Avishek Joey Bose, Hugo Berard, William L. Hamilton, Simon Lacoste-Julien, Pascal Vincent, Gauthier Gidel:
Online Adversarial Attacks. CoRR abs/2103.02014 (2021) - [i62]Namyeong Kwon, Hwidong Na, Gabriel Huang, Simon Lacoste-Julien:
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning. CoRR abs/2103.09027 (2021) - [i61]Yassine Yaakoubi, François Soumis, Simon Lacoste-Julien:
Structured Convolutional Kernel Networks for Airline Crew Scheduling. CoRR abs/2105.11646 (2021) - [i60]Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien:
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity. CoRR abs/2107.00052 (2021) - [i59]Sébastien Lachapelle, Pau Rodríguez López, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien:
Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA. CoRR abs/2107.10098 (2021) - [i58]Gabriel Huang, Issam H. Laradji, David Vázquez, Simon Lacoste-Julien, Pau Rodríguez:
A Survey of Self-Supervised and Few-Shot Object Detection. CoRR abs/2110.14711 (2021) - [i57]Rémi Le Priol, Frederik Kunstner, Damien Scieur, Simon Lacoste-Julien:
Convergence Rates for the MAP of an Exponential Family and Stochastic Mirror Descent - an Open Problem. CoRR abs/2111.06826 (2021) - [i56]Yan Zhang, David W. Zhang, Simon Lacoste-Julien, Gertjan J. Burghouts, Cees G. M. Snoek:
Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation. CoRR abs/2111.12193 (2021) - 2020
- [j5]Yassine Yaakoubi, François Soumis, Simon Lacoste-Julien:
Machine learning in airline crew pairing to construct initial clusters for dynamic constraint aggregation. EURO J. Transp. Logist. 9(4): 100020 (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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