[Version française]
Prospective students: *do not email* me directly about doing a PhD or internship with me. Unfortunately, I receive hundreds of requests like these and cannot reply individually. If you are interested in doing a PhD, Master or internship with me, please apply first through the Mila supervision request website and put in your selection there that you are interested in working with me. If your application is succesful, I will contact you. I will be taking new students for Fall 2025.
News:
2019: I started heading part-time a new machine learning research lab, SAIT AI Lab (SAIL) Montreal, from Samsung Advanced Institute of Technology (SAIT) located in Mila's corporate labs and was hiring research scientists.
[May 1st 2019 inauguration video]
NeurIPS 2018 and 2019 Workshops on Smooth Games Optimization and Machine Learning.
Update August 2016: I just moved to the Université de Montréal.
(Until August 2016) I was 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.
CV (November 2023) | Google Scholar citation profile
Research Interests
- Structured prediction
- Optimization and machine learning
- Theory of deep learning
- Modern generative models
- Causality
- Applications: NLP, information retrieval, computer vision, computational biology
Students and Postdocs
Alumni
- Jean-Baptiste Alayrac, Research Scientist, Deepmind, London
- Reza Babanezhad, Research Scientist, SAIL Montreal
- Aristide Baratin, Research Scientist, SAIL Montreal
- Hugo Berard, Postdoctoral Researcher, Université de Montréal
- Quentin Bertrand, Researcher (chargé de recherche), Inria Lyon
- Tatjana Chavdarova, Assistant Professor, Saarland University
- Golnoosh Farnadi, Assistant Professor, McGill University
- Gauthier Gidel, Assistant Professor, Université de Montréal
- Gabriel Huang, Research Scientist, ServiceNow
- Sébastien Lachapelle, Research Scientist, SAIL Montreal
- Rémi Leblond, Research Scientist, Deepmind, Paris
- Rémi Le Priol, Analytics Engineer, Padam Mobility
- Lewis Liu, PhD Student, Stanford University
- Isabella Lukasewitz, Associate Partner, QuantumBlack / McKinsey
- Nicolas Loizou, Assistant Professor, Johns Hopkins University
- Anton Osokin, Assistant Professor, CS HSE, Moscow, Russia
- Anastasia Podosinnikova, Postdoctoral Researcher, MIT
- Damien Scieur, Research Scientist, SAIL Montreal
- Sharan Vaswani, Assistant Professor, Simon Fraser University
- Yassine Yaakoubi, Assistant Professor, Concordia University
Teaching
Grad classes at Université de Montréal:
Previously:
Papers
Technical Reports
- [new!] Accelerating Training with Neuron Interaction and Nowcasting Networks,
Boris Knyazev, Abhinav Moudgil, Guillaume Lajoie, Eugene Belilovsky, Simon Lacoste-Julien,
arXiv:2409.04434 [CS.LG],
Sept 2024.
- [new!] Performative Prediction on Games and Mechanism Design,
António Góis, Mehrnaz Mofakhami, Fernando P. Santos, Simon Lacoste-Julien, Gauthier Gidel,
arXiv:2408.05146 [CS.LG],
ICML 2024 Workshop on Agentic Markets, Vienna, Austria,
Aug 2024.
- [new!] Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies,
Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien,
arXiv:2401.04890 [CS.LG],
Jan 2024.
- Partial Disentanglement via Mechanism Sparsity,
Sébastien Lachapelle, Simon Lacoste-Julien,
arXiv:2207.07732 [stat.ML],
July 2022.
- Convergence Rates for the MAP of an Exponential Family and Stochastic Mirror Descent -- an Open Problem,
Rémi Le Priol, Frederik Kunstner, Damien Scieur, Simon Lacoste-Julien,
arXiv:2111.06826 [stat.ML],
Nov 2021.
- To Each Optimizer a Norm, To Each Norm its Generalization,
Sharan Vaswani, Reza Babanezhad, Jose Gallego-Posada, Aaron Mishkin, Simon Lacoste-Julien, Nicolas Le Roux,
arXiv:2006.06821
[cs.LG], June 2020.
- Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search),
Sharan Vaswani, Frederik Kunstner, Issam Laradji, Si Yi Meng, Mark Schmidt, Simon Lacoste-Julien,
arXiv:2006.06835
[cs.LG], June 2020. [code]
- Flight-Connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer,
Y. Yaakoubi, S. Lacoste-Julien, F. Soumis,
arXiv:2009.12501 [cs.LG],
Les Cahiers du GERAD G-2019-26,
April 2019.
- Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-Time,
G. Huang, H. Larochelle, S. Lacoste-Julien,
arXiv:1902.08605
[cs.LG], Feb 2019.
- A Modern Take on the Bias-Variance Tradeoff in Neural Networks,
B. Neal, S. Mittal, A. Baratin, V. Tantia, M. Scicluna, S. Lacoste-Julien, I. Mitliagkas,
arXiv:1810.08591
[cs.LG], Oct 2018.
- Parametric Adversarial Divergences are Good Task Losses for Generative Modeling,
G. Huang,
H. Berard, A. Touati,
G. Gidel,
P. Vincent and
S. Lacoste-Julien,
arXiv:1708.02511
[cs.LG], August 2017.
- Convergence Rate of Frank-Wolfe for Non-Convex Objectives,
S. Lacoste-Julien,
arXiv:1607.00345 [math.OC], June 2016.
- 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.
- 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.
- 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.
- Discriminative Machine Learning with Structure, S. Lacoste-Julien, PhD Thesis, University of California, Berkeley, 2009.
- 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.
Published Papers
- [new!] On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization,
Motahareh Sohrabi*, Juan Ramirez*, Tianyue H. Zhang, Simon Lacoste-Julien, Jose Gallego-Posada,
arXiv:2406.04558 [CS.LG],
International Conference on Machine Learning (ICML 2024), Vienna, Austria, July 2024.
[open reviews]
- [new!] Promoting Exploration in Memory-Augmented Adam using Critical Momenta,
Pranshu Malviya, Gonçalo Mordido, Aristide Baratin, Reza Babanezhad Harikandeh, Jerry Huang, Simon Lacoste-Julien, Razvan Pascanu, Sarath Chandar,
arXiv:2307.09638 [CS.LG],
Transactions on Machine Learning Research (TMLR), June 2024.
June 2024.
[open reviews]
- [new!] Balancing Act: Constraining Disparate Impact in Sparse Models,
Meraj Hashemizadeh*, Juan Ramirez*, Rohan Sukumaran, Golnoosh Farnadi, Simon Lacoste-Julien, Jose Gallego-Posada,
arXiv:2310.20673 [CS.LG],
International Conference on Learning Representations (ICLR 2024), Vienna, Austria, May 2024.
[open reviews]
- [new!] Weight-Sharing Regularization,
Mehran Shakerinava*, Motahareh Sohrabi*, Siamak Ravanbakhsh, Simon Lacoste-Julien,
arXiv:2311.03096 [CS.LG],
International Conference on Artificial Intelligence and Statistics (AISTATS 2024), Valencia, Spain, May 2024.
- [new!] PopulAtion Parameter Averaging (PAPA),
Alexia Jolicoeur-Martineau, Emy Gervais, Kilian Fatras, Yan Zhang, Simon Lacoste-Julien,
arXiv:2304.03094 [CS.LG],
Transactions on Machine Learning Research (TMLR), April 2024.
- [new!] On the Identifiability of Quantized Factors,
Vitória Barin-Pacela, Kartik Ahuja, Simon Lacoste-Julien, Pascal Vincent,
arXiv:2306.16334 [CS.LG],
Third Conference on Causal Learning and Reasoning (CLeaR 2024), Los Angeles, CA, USA,
April 2024.
- [new!] Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation,
Sébastien Lachapelle*, Divyat Mahajan*, Ioannis Mitliagkas, Simon Lacoste-Julien,
arXiv:2307.02598 [CS.LG], Neural Information Processing Systems Conference (NeurIPS 2023), New Orleans, LA, USA, December 2023. (NeurIPS oral!)
- [new!] Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective,
Sébastien Lachapelle*, Tristan Deleu*, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand,
arXiv:2211.14666 [CS.LG],
International Conference on Machine Learning (ICML 2023), Hawai, USA, July 2023.
- [new!] Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?,
Boris Knyazev, Doha Hwang, Simon Lacoste-Julien,
arXiv:2303.04143 [CS.LG],
International Conference on Machine Learning (ICML 2023), Hawai, USA, July 2023. [code]
- [new!] Unlocking Slot Attention by Changing Optimal Transport Costs,
Yan Zhang*, David W. Zhang*, Simon Lacoste-Julien, Gertjan J. Burghouts, Cees G. M. Snoek,
arXiv:2301.13197 [CS.LG],
International Conference on Machine Learning (ICML 2023), Hawai, USA, July 2023.
- [new!] CrossSplit: Mitigating Label Noise Memorization through Data Splitting,
Jihye Kim, Aristide Baratin, Yan Zhang, Simon Lacoste-Julien,
arXiv:2212.01674 [CS.CV],
International Conference on Machine Learning (ICML 2023), Hawai, USA, July 2023.
- Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints,
Jose Gallego-Posada, Juan Ramirez, Akram Erraqabi, Yoshua Bengio, Simon Lacoste-Julien,
arXiv:2208.04425 [CS.LG],
Neural Information Processing Systems Conference (NeurIPS 2022), New Orleans, LA, USA, November 2022.
- Data-Efficient Structured Pruning via Submodular Optimization,
Marwa El Halabi, Suraj Srinivas, Simon Lacoste-Julien,
arXiv:2203.04940 [cs.LG],
Neural Information Processing Systems Conference (NeurIPS 2022), New Orleans, LA, USA, November 2022.
- Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution,
Antonio Orvieto, Simon Lacoste-Julien, Nicolas Loizou,
arXiv:2205.04583 [math.OC],
Neural Information Processing Systems Conference (NeurIPS 2022), New Orleans, LA, USA, November 2022.
- SVRG Meets AdaGrad: Painless Variance Reduction,
Benjamin Dubois-Taine*, Sharan Vaswani*, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien,
Machine Learning Journal, 111, 4359–4409, 2022 (Special Issue of the ECML PKDD 2022 Journal Track
).
arXiv:2102.09645 [cs.LG], [code]
- A Survey of Self-Supervised and Few-Shot Object Detection,
Gabriel Huang, Issam Laradji, David Vazquez, Simon Lacoste-Julien, Pau Rodriguez, arXiv:2110.14711 [cs.CV],
IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI), August 2022.
- Bayesian Structure Learning with Generative Flow Networks,
Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, Yoshua Bengio,
arXiv:2202.13903 [cs.LG],
Uncertainty in Artificial Intelligence (UAI 2022),
Eindhoven, Netherlands, August 2022. [open reviews]
- Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA,
Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien,
arXiv:2107.10098 [stat.ML],
First Conference on Causal Learning and Reasoning (CLeaR 2022), Eureka, CA, USA,
April 2022. [open reviews]
- Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation,
Yan Zhang*, David W Zhang*, Simon Lacoste-Julien, Gertjan J. Burghouts, Cees G. M. Snoek,
arXiv:2111.12193 [cs.LG],
International Conference on Learning Representations (ICLR 2022), online conference, April 2022.
[open reviews]
- Online Adversarial Attacks,
Andjela Mladenovic*, Avishek Joey Bose*, Hugo Berard*, William L. Hamilton, Simon Lacoste-Julien, Pascal Vincent, Gauthier Gidel,
arXiv:2103.02014 [cs.LG],
International Conference on Learning Representations (ICLR 2022), online conference, April 2022.
[open reviews]
- On the Convergence of Continuous Constrained Optimization for Bayesian Network Structure Learning,
Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang,
arXiv:2011.11150
[cs.LG],
International Conference on Artificial Intelligence and Statistics (AISTATS 2022), online conference, March 2022.
- Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity,
Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien,
arXiv:2107.00052, Neural Information Processing Systems Conference (NeurIPS 2021), online conference, December 2021.
- Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information,
E. Larsen, S. Lachapelle, Y. Bengio, E. Frejinger, S. Lacoste-Julien, A. Lodi,
arXiv:1807.11876
[cs.LG], INFORMS Journal on Computing, September 2021.
- Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets,
Thomas Kerdreux*, Lewis Liu*, Simon Lacoste-Julien, Damien Scieur,
arXiv:2011.03351 [math.OC],
International Conference on Machine Learning (ICML 2021), online conference, July 2021.
- Structured Convolutional Kernel Networks for Airline Crew Scheduling,
Yassine Yaakoubi, François Soumis, Simon Lacoste-Julien,
arXiv:2105.11646 [cs.LG],
International Conference on Machine Learning (ICML 2021), online conference, July 2021.
- Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning,
Namyeong Kwon, Hwidong Na, Gabriel Huang, Simon Lacoste-Julien,
International Conference on Learning Representations (ICLR 2021), online conference, May 2021.
[open reviews]
- Implicit Regularization via Neural Feature Alignment,
Aristide Baratin, Thomas George, César Laurent, R Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien,
arXiv:2008.00938
[cs.LG],
International Conference on Artificial Intelligence and Statistics (AISTATS 2021), online conference, April 2021.
- An Analysis of the Adaptation Speed of Causal Models,
Rémi Le Priol, Reza Babanezhad Harikandeh, Yoshua Bengio, Simon Lacoste-Julien,
arXiv:2005.09136
[stat.ML],
International Conference on Artificial Intelligence and Statistics (AISTATS 2021), online conference, April 2021.
- Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence,
Nicolas Loizou, Sharan Vaswani, Issam Laradji, Simon Lacoste-Julien,
arXiv:2002.10542
[math.OC], International Conference on Artificial Intelligence and Statistics (AISTATS 2021), online conference, April 2021. [code]
- Differentiable Causal Discovery from Interventional Data,
Philippe Brouillard*, Sébastien Lachapelle*, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin,
arXiv:2007.01754, Neural Information Processing Systems Conference (NeurIPS 2020), Vancouver, Canada, December 2020. (NeurIPS spotlight!)
- Adversarial Example Games,
Avishek Joey Bose*, Gauthier Gidel*, Hugo Berard*, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton,
arXiv:2007.00720
Neural Information Processing Systems Conference (NeurIPS 2020), Vancouver, Canada, December 2020.
- Machine Learning in Airline Crew Pairing to Construct Initial Clusters for Dynamic Constraint Aggregation,
Yassine Yaakoubis, François Soumis, Simon Lacoste-Julien,
arXiv:2010.00134 [cs.AI],
EURO Journal on Transportation and Logistics, (in press) Sept 2020.
- Stochastic Hamiltonian Gradient Methods for Smooth Games,
Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas,
arXiv:2007.04202 [cs.LG],
International Conference on Machine Learning (ICML 2020), online conference, July 2020. [code]
- GAIT: A Geometric Approach to Information Theory,
J. Gallego-Posada, A. Vani, M. Schwarzer, S. Lacoste-Julien
arXiv:1906.08325, International Conference on Artificial Intelligence and Statistics (AISTATS 2020), Palermo, Sicily, Italy, June 2020.
- Accelerating Smooth Games by Manipulating Spectral Shapes,
W. Azizian, D. Scieur, I. Mitliagkas, S. Lacoste-Julien, G. Gidel
arXiv:2001.00602, International Conference on Artificial Intelligence and Statistics (AISTATS 2020), Palermo, Sicily, Italy, June 2020.
- A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games,
W. Azizian, I. Mitliagkas, S. Lacoste-Julien, G. Gidel
arXiv:1906.05945, International Conference on Artificial Intelligence and Statistics (AISTATS 2020), Palermo, Sicily, Italy, June 2020.
- Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation,
S. Y. Meng, S. Vaswani, I. Laradji, M. Schmidt, S. Lacoste-Julien
arXiv:1910.04920, International Conference on Artificial Intelligence and Statistics (AISTATS 2020), Palermo, Sicily, Italy, June 2020.
[code]
- Gradient-Based Neural DAG Learning,
S. Lachapelle, P. Brouillard, T. Deleu, S. Lacoste-Julien,
arXiv:1906.02226, International Conference on Learning Representations (ICLR 2020), Addis Adaba, Ethiopia, April 2020.
[open reviews]
- A Closer Look at the Optimization Landscapes of Generative Adversarial Networks,
H. Berard*, G. Gidel*, A. Almahairi, P. Vincent, S. Lacoste-Julien
arXiv:1906.04848,
International Conference on Learning Representations (ICLR 2020), Addis Adaba, Ethiopia, April 2020.
[open reviews]
- Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates,
S. Vaswani, A. Mishkin, I. Laradji, M. Schmidt, G. Gidel, S. Lacoste-Julien,
Neural Information Processing Systems Conference (NeurIPS 2019), Vancouver, Canada, December 2019.
[code]
- Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks,
G. Gidel, F. Bach, S. Lacoste-Julien, arXiv:1904.13262
[cs.LG],
Neural Information Processing Systems Conference (NeurIPS 2019), Vancouver, Canada, December 2019.
- Reducing Noise in GAN Training with Variance Reduced Extragradient,
T. Chavdarova*, G. Gidel*, F. Fleuret, S. Lacoste-Julien,
arXiv:1904.08598
[stat.ML],
Neural Information Processing Systems Conference (NeurIPS 2019), Vancouver, Canada, December 2019.
- A Variational Inequality Perspective on Generative Adversarial Networks,
G. Gidel*,
H. Berard*,
P. Vincent and
S. Lacoste-Julien,
arXiv:1802.10551,
International Conference on Learning Representations (ICLR 2019), New Orleans, USA, May 2019.
[open reviews]
- Negative Momentum for Improved Game Dynamics,
G. Gidel*,
R. Askari*,
M. Pezeshki,
R. Le Priol,
G. Huang,
S. Lacoste-Julien and
I. Mitliagkas,
arXiv:1807.04740,
International Conference on Artificial Intelligence and Statistics (AISTATS 2019), Naha, Okinawa, Japan, April 2019.
- Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods,
R. Leblond,
F. Pedregosa,
and
S. Lacoste-Julien,
Journal of Machine Learning Research (JMLR), 19(81): 1--68, December 2018.
- Quantifying Learning Guarantees for Convex but Inconsistent Surrogates,
K. Struminsky,
S. Lacoste-Julien and
A. Osokin,
Neural Information Processing Systems Conference (NIPS18),
Montreal, Canada, December 2018.
- Learning from Narrated Instruction Videos,
J.-B. Alayrac,
P. Bojanowski,
N. Agrawal,
I. Laptev,
J. Sivic and
S. Lacoste-Julien,
IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI), 40(1), 2194 - 2208, September 2018.
[project website]
- Scattering Networks for Hybrid Representation Learning,
E. Oyallon,
S. Zagoruyko,
G. Huang,
N. Komodakis,
S. Lacoste-Julien and
M. Blaschko,
IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI), July 2018.
- Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields,
R. Le Priol,
A. Piché and
S. Lacoste-Julien,
Uncertainty in Artificial Intelligence (UAI 2018),
Monterey, CA, USA, August 2018.
[project website]
- SEARNN: Training RNNs with Global-Local Losses,
R. Leblond*,
J.-B. Alayrac*,
A. Osokin and
S. Lacoste-Julien,
International Conference on Learning Representations (ICLR 2018),
Vancouver, Canada, April 2018.
[open reviews]
[project website]
*Both authors contributed equally
- Frank-Wolfe Splitting via Augmented Lagrangian Method,
G. Gidel,
F. Pedregosa and
S. Lacoste-Julien,
International Conference on Artificial Intelligence and Statistics (AISTATS 2018), Lanzarote, Canary Islands, Spain, April 2018. (AISTATS oral!)
- On Structured Prediction Theory with Calibrated Convex Surrogate Losses,
A. Osokin,
F. Bach and
S. Lacoste-Julien,
Neural Information Processing Systems Conference (NIPS17),
Long Beach, USA, December 2017.
[code]
(NIPS oral!)
- Breaking the Nonsmooth Barrier: a Scalable Parallel Method for Composite Optimization,
F. Pedregosa,
R. Leblond and
S. Lacoste-Julien,
Neural Information Processing Systems Conference (NIPS17),
Long Beach, USA, December 2017.
[project website]
(NIPS spotlight!)
- Joint Discovery of Object States and Manipulating Actions,
J.-B. Alayrac,
J. Sivic,
I. Laptev and
S. Lacoste-Julien,
International Conference on Computer Vision
(ICCV 2017), Venice, Italy, October 2017.
[project website]
- A Closer Look at Memorization in Deep Networks,
D. Arpit*, S. Jastrzebski*, N. Ballas*, D. Krueger*, E. Bengio, M. S. Kanwal, T. Maharaj, A. Fischer, A. Courville, Y. Bengio and S. Lacoste-Julien,
International Conference on Machine Learning (ICML 2017),
Sydney, Australia, August 2017. *Equal contribution.
- Frank-Wolfe Algorithms for Saddle Point Problems,
G. Gidel,
T. Jebara and
S. Lacoste-Julien,
International Conference on Artificial Intelligence and Statistics (AISTATS 2017), Fort Lauderdale, Florida, USA, April 2017.
[project website]
- ASAGA: Asynchronous Parallel SAGA,
R. Leblond,
F. Pedregosa and
S. Lacoste-Julien, International Conference on Artificial Intelligence and Statistics (AISTATS 2017), Fort Lauderdale, Florida, USA, April 2017. [project website]
- PAC-Bayesian Theory Meets Bayesian Inference,
P. Germain,
F. Bach and
S. Lacoste-Julien,
Neural Information Processing Systems Conference (NIPS16), Barcelona, Spain, December 2016.
- Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs,
A. Osokin*,
J.-B. Alayrac*,
I. Lukasewitz,
P. Dokania
and
S. Lacoste-Julien,
International Conference on Machine Learning (ICML 2016),
New York City, USA, June 2016. *Both authors contributed equally. [project website]
- Beyond CCA: Moment Matching for Multi-View Models,
A. Podosinnikova,
F. Bach
and
S. Lacoste-Julien,
International Conference on Machine Learning (ICML 2016),
New York City, USA, June 2016.
[code]
- Unsupervised Learning from Narrated Instruction Videos,
J.-B. Alayrac,
P. Bojanowski,
N. Agrawal,
I. Laptev,
J. Sivic and
S. Lacoste-Julien,
Conference on Computer Vision and Pattern Recognition
(CVPR 2016), Las Vega, USA, June 2016.
[project website]
(oral!)
- 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]
- 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]
- 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.
- 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.
- 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)]
- 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 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.
-
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.