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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)
More from the Same Authors
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2020 Workshop: Differential Geometry meets Deep Learning (DiffGeo4DL) »
Joey Bose · Emile Mathieu · Charline Le Lan · Ines Chami · Frederic Sala · Christopher De Sa · Maximillian Nickel · Christopher Ré · Will Hamilton -
2020 Poster: Differentiable Causal Discovery from Interventional Data »
Philippe Brouillard · Sébastien Lachapelle · Alexandre Lacoste · Simon Lacoste-Julien · Alexandre Drouin -
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 »
Wojciech Czarnecki · Gauthier Gidel · Brendan Tracey · Karl Tuyls · Shayegan Omidshafiei · David Balduzzi · Max Jaderberg -
2020 Poster: Learning Dynamic Belief Graphs to Generalize on Text-Based Games »
Ashutosh Adhikari · Xingdi Yuan · Marc-Alexandre Côté · Mikuláš Zelinka · Marc-Antoine Rondeau · Romain Laroche · Pascal Poupart · Jian Tang · Adam Trischler · Will Hamilton -
2019 Workshop: Bridging Game Theory and Deep Learning »
Ioannis Mitliagkas · Gauthier Gidel · Niao He · Reyhane Askari Hemmat · N H · Nika Haghtalab · Simon Lacoste-Julien -
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 »
Tatjana Chavdarova · Gauthier Gidel · François Fleuret · Simon Lacoste-Julien -
2019 Poster: Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks »
Gauthier Gidel · Francis Bach · Simon Lacoste-Julien -
2019 Poster: Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates »
Sharan Vaswani · Aaron Mishkin · Issam Laradji · Mark Schmidt · Gauthier Gidel · Simon Lacoste-Julien -
2019 Poster: Efficient Graph Generation with Graph Recurrent Attention Networks »
Renjie Liao · Yujia Li · Yang Song · Shenlong Wang · Will Hamilton · David Duvenaud · Raquel Urtasun · Richard Zemel -
2019 Poster: Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics »
Giancarlo Kerg · Kyle Goyette · Maximilian Puelma Touzel · Gauthier Gidel · Eugene Vorontsov · Yoshua Bengio · Guillaume Lajoie -
2018 Workshop: Smooth Games Optimization and Machine Learning »
Simon Lacoste-Julien · Ioannis Mitliagkas · Gauthier Gidel · Vasilis Syrgkanis · Eva Tardos · Leon Bottou · Sebastian Nowozin -
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 Poster: Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization »
Fabian Pedregosa · Rémi Leblond · Simon Lacoste-Julien -
2017 Poster: On Structured Prediction Theory with Calibrated Convex Surrogate Losses »
Anton Osokin · Francis Bach · Simon Lacoste-Julien -
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 »
Anton Osokin · Francis Bach · Simon Lacoste-Julien -
2016 Poster: PAC-Bayesian Theory Meets Bayesian Inference »
Pascal Germain · Francis Bach · Alexandre Lacoste · Simon Lacoste-Julien -
2015 Poster: On the Global Linear Convergence of Frank-Wolfe Optimization Variants »
Simon Lacoste-Julien · Martin Jaggi -
2015 Poster: Barrier Frank-Wolfe for Marginal Inference »
Rahul G Krishnan · Simon Lacoste-Julien · David Sontag -
2015 Poster: Variance Reduced Stochastic Gradient Descent with Neighbors »
Thomas Hofmann · Aurelien Lucchi · Simon Lacoste-Julien · Brian McWilliams -
2015 Poster: Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets »
Pascal Vincent · Alexandre de Brébisson · Xavier Bouthillier -
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