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Author Information
Joey Bose (McGill/MILA)
I’m a PhD student at the RLLab at McGill/MILA where I work on Adversarial Machine Learning on Graphs. Previously, I was a Master’s student at the University of Toronto where I researched crafting Adversarial Attacks on Computer Vision models using GAN’s. I also interned at Borealis AI where I was working on applying adversarial learning principles to learn better embeddings i.e. Word Embeddings for Machine Learning models.
Gauthier Gidel (Mila)
I am a Ph.D student supervised by Simon Lacoste-Julien, I graduated from ENS Ulm and Université Paris-Saclay. I was a visiting PhD student at Sierra. I also worked for 6 months as a freelance Data Scientist for Monsieur Drive (Acquired by Criteo) and I recently co-founded a startup called Krypto. I'm currently pursuing my PhD at Mila. My work focuses on optimization applied to machine learning. More details can be found in my resume. My research is to develop new optimization algorithms and understand the role of optimization in the learning procedure, in short, learn faster and better. I identify to the field of machine learning (NIPS, ICML, AISTATS and ICLR) and optimization (SIAM OP)
Hugo Berard (Mila & Facebook AI Research)
Andre Cianflone (Mila/McGill)
I am a PhD student at McGill University and part of the RLLab and Mila lab. I research machine learning, specifically Theory of Mind, Reinforcement Learning, and Emergent Communication.
Pascal Vincent (Facebook and U. Montreal)
Simon Lacoste-Julien (Mila, Université de Montréal & SAIL Montreal)
Simon Lacoste-Julien is an associate professor at Mila and DIRO from Université de Montréal, and Canada CIFAR AI Chair holder. He also heads part time the SAIT AI Lab Montreal from Samsung. His research interests are machine learning and applied math, with applications in related fields like computer vision and natural language processing. He obtained a B.Sc. in math., physics and computer science from McGill, a PhD in computer science from UC Berkeley and a post-doc from the University of Cambridge. He spent a few years as a research faculty at INRIA and École normale supérieure in Paris before coming back to his roots in Montreal in 2016 to answer the call from Yoshua Bengio in growing the Montreal AI ecosystem.
Will Hamilton (McGill)
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2021 Spotlight: A single gradient step finds adversarial examples on random two-layers neural networks »
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2021 : On the convergence of stochastic extragradient for bilinear games using restarted iteration averaging »
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2021 : On the convergence of stochastic extragradient for bilinear games using restarted iteration averaging »
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2022 : Nesterov Meets Optimism: Rate-Optimal Optimistic-Gradient-Based Method for Stochastic Bilinearly-Coupled Minimax Optimization »
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2022 : Momentum Extragradient is Optimal for Games with Cross-Shaped Spectrum »
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2022 : Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods »
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2022 : Performative Prediction with Neural Networks »
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2022 : Unlocking Slot Attention by Changing Optimal Transport Costs »
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2022 : Unlocking Slot Attention by Changing Optimal Transport Costs »
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2022 Poster: 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 -
2022 Poster: Riemannian Diffusion Models »
Chin-Wei Huang · Milad Aghajohari · Joey Bose · Prakash Panangaden · Aaron Courville -
2022 Poster: Data-Efficient Structured Pruning via Submodular Optimization »
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2022 Poster: Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise »
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2022 Poster: The Curse of Unrolling: Rate of Differentiating Through Optimization »
Damien Scieur · Gauthier Gidel · Quentin Bertrand · Fabian Pedregosa -
2022 Poster: Beyond L1: Faster and Better Sparse Models with skglm »
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2022 Poster: Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution »
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2022 Poster: Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities »
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2021 Poster: 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 -
2021 Poster: A single gradient step finds adversarial examples on random two-layers neural networks »
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2020 : Focused Breakout Session »
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2020 : Panel Discussion »
Joey Bose · Emile Mathieu · Charline Le Lan · Ines Chami -
2020 : Poster Session 1 on Gather.Town »
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2020 : Contributed Talk 4: Directional Graph Networks »
Dominique Beaini · Saro Passaro · Vincent Létourneau · Will Hamilton · Gabriele Corso · Pietro Liò -
2020 Workshop: Differential Geometry meets Deep Learning (DiffGeo4DL) »
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2020 : Opening Remarks »
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2020 Poster: Differentiable Causal Discovery from Interventional Data »
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2020 Spotlight: Differentiable Causal Discovery from Interventional Data »
Philippe Brouillard · Sébastien Lachapelle · Alexandre Lacoste · Simon Lacoste-Julien · Alexandre Drouin -
2020 Poster: Real World Games Look Like Spinning Tops »
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2020 Poster: Learning Dynamic Belief Graphs to Generalize on Text-Based Games »
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2019 Workshop: Bridging Game Theory and Deep Learning »
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2019 : Poster Session »
Gergely Flamich · Shashanka Ubaru · Charles Zheng · Josip Djolonga · Kristoffer Wickstrøm · Diego Granziol · Konstantinos Pitas · Jun Li · Robert Williamson · Sangwoong Yoon · Kwot Sin Lee · Julian Zilly · Linda Petrini · Ian Fischer · Zhe Dong · Alexander Alemi · Bao-Ngoc Nguyen · Rob Brekelmans · Tailin Wu · Aditya Mahajan · Alexander Li · Kirankumar Shiragur · Yair Carmon · Linara Adilova · SHIYU LIU · Bang An · Sanjeeb Dash · Oktay Gunluk · Arya Mazumdar · Mehul Motani · Julia Rosenzweig · Michael Kamp · Marton Havasi · Leighton P Barnes · Zhengqing Zhou · Yi Hao · Dylan Foster · Yuval Benjamini · Nati Srebro · Michael Tschannen · Paul Rubenstein · Sylvain Gelly · John Duchi · Aaron Sidford · Robin Ru · Stefan Zohren · Murtaza Dalal · Michael A Osborne · Stephen J Roberts · Moses Charikar · Jayakumar Subramanian · Xiaodi Fan · Max Schwarzer · Nicholas Roberts · Simon Lacoste-Julien · Vinay Prabhu · Aram Galstyan · Greg Ver Steeg · Lalitha Sankar · Yung-Kyun Noh · Gautam Dasarathy · Frank Park · Ngai-Man (Man) Cheung · Ngoc-Trung Tran · Linxiao Yang · Ben Poole · Andrea Censi · Tristan Sylvain · R Devon Hjelm · Bangjie Liu · Jose Gallego-Posada · Tyler Sypherd · Kai Yang · Jan Nikolas Morshuis -
2019 : Poster Session »
Jonathan Scarlett · Piotr Indyk · Ali Vakilian · Adrian Weller · Partha P Mitra · Benjamin Aubin · Bruno Loureiro · Florent Krzakala · Lenka Zdeborová · Kristina Monakhova · Joshua Yurtsever · Laura Waller · Hendrik Sommerhoff · Michael Moeller · Rushil Anirudh · Shuang Qiu · Xiaohan Wei · Zhuoran Yang · Jayaraman Thiagarajan · Salman Asif · Michael Gillhofer · Johannes Brandstetter · Sepp Hochreiter · Felix Petersen · Dhruv Patel · Assad Oberai · Akshay Kamath · Sushrut Karmalkar · Eric Price · Ali Ahmed · Zahra Kadkhodaie · Sreyas Mohan · Eero Simoncelli · Carlos Fernandez-Granda · Oscar Leong · Wesam Sakla · Rebecca Willett · Stephan Hoyer · Jascha Sohl-Dickstein · Sam Greydanus · Gauri Jagatap · Chinmay Hegde · Michael Kellman · Jonathan Tamir · Nouamane Laanait · Ousmane Dia · Mirco Ravanelli · Jonathan Binas · Negar Rostamzadeh · Shirin Jalali · Tiantian Fang · Alex Schwing · Sébastien Lachapelle · Philippe Brouillard · Tristan Deleu · Simon Lacoste-Julien · Stella Yu · Arya Mazumdar · Ankit Singh Rawat · Yue Zhao · Jianshu Chen · Xiaoyang Li · Hubert Ramsauer · Gabrio Rizzuti · Nikolaos Mitsakos · Dingzhou Cao · Thomas Strohmer · Yang Li · Pei Peng · Gregory Ongie -
2019 : Poster Session #1 »
Adarsh Jamadandi · Sophia Sanborn · Huaxiu Yao · Chen Cai · Yu Chen · Jean-Marc Andreoli · Niklas Stoehr · Shih-Yang Su · Tony Duan · Fábio Ferreira · Davide Belli · Amit Boyarski · Ze Ye · Elahe Ghalebi · Arindam Sarkar · MAHMOUD KHADEMI · Evgeniy Faerman · Joey Bose · Jiaqi Ma · Lin Meng · Seyed Mehran Kazemi · Guangtao Wang · Tong Wu · Yuexin Wu · Chaitanya K. Joshi · Marc Brockschmidt · Daniele Zambon · Colin Graber · Rafaël Van Belle · Osman Asif Malik · Xavier Glorot · Mario Krenn · Chris Cameron · Binxuan Huang · George Stoica · Alexia Toumpa -
2019 : Opening remarks »
Will Hamilton -
2019 Workshop: Graph Representation Learning »
Will Hamilton · Rianne van den Berg · Michael Bronstein · Stefanie Jegelka · Thomas Kipf · Jure Leskovec · Renjie Liao · Yizhou Sun · Petar Veličković -
2019 Poster: Reducing Noise in GAN Training with Variance Reduced Extragradient »
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2019 Poster: Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks »
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2019 Poster: Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates »
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2019 Poster: Efficient Graph Generation with Graph Recurrent Attention Networks »
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2019 Poster: Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics »
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2018 : Poster spotlight »
Tianbao Yang · Pavel Dvurechenskii · Panayotis Mertikopoulos · Hugo Berard -
2018 : Opening remarks »
Simon Lacoste-Julien · Gauthier Gidel -
2018 Workshop: Smooth Games Optimization and Machine Learning »
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2018 Poster: Quantifying Learning Guarantees for Convex but Inconsistent Surrogates »
Kirill Struminsky · Simon Lacoste-Julien · Anton Osokin -
2018 Poster: Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis »
Thomas George · César Laurent · Xavier Bouthillier · Nicolas Ballas · Pascal Vincent -
2017 : A3T: Adversarially Augmented Adversarial Training »
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2017 : On Structured Prediction Theory with Calibrated Convex Surrogate Losses. »
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2017 Poster: Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization »
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2017 Poster: On Structured Prediction Theory with Calibrated Convex Surrogate Losses »
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2017 Spotlight: Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization »
Fabian Pedregosa · Rémi Leblond · Simon Lacoste-Julien -
2017 Oral: On Structured Prediction Theory with Calibrated Convex Surrogate Losses »
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2016 Poster: PAC-Bayesian Theory Meets Bayesian Inference »
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2015 Poster: On the Global Linear Convergence of Frank-Wolfe Optimization Variants »
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2015 Poster: Barrier Frank-Wolfe for Marginal Inference »
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2015 Poster: Variance Reduced Stochastic Gradient Descent with Neighbors »
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2015 Poster: Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets »
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2015 Oral: Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets »
Pascal Vincent · Alexandre de Brébisson · Xavier Bouthillier -
2015 Poster: Rethinking LDA: Moment Matching for Discrete ICA »
Anastasia Podosinnikova · Francis Bach · Simon Lacoste-Julien -
2014 Poster: SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives »
Aaron Defazio · Francis Bach · Simon Lacoste-Julien -
2013 Poster: Generalized Denoising Auto-Encoders as Generative Models »
Yoshua Bengio · Li Yao · Guillaume Alain · Pascal Vincent -
2011 Oral: The Manifold Tangent Classifier »
Salah Rifai · Yann N Dauphin · Pascal Vincent · Yoshua Bengio · Xavier Muller -
2011 Poster: The Manifold Tangent Classifier »
Salah Rifai · Yann N Dauphin · Pascal Vincent · Yoshua Bengio · Xavier Muller -
2009 Workshop: The Generative and Discriminative Learning Interface »
Simon Lacoste-Julien · Percy Liang · Guillaume Bouchard -
2008 Poster: DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification »
Simon Lacoste-Julien · Fei Sha · Michael Jordan