Ioannis Mitliagkas

Membre Académique Principal
Ioannis Mitliagkas
Professeur adjoint, Université de Montréal
Assistant professor Ioannis Mitliagkas
Ioannis Mitliagkas

Ioannis Mitliagkas est professeur adjoint au Département d’informatique et de recherche opérationnelle (DIRO) de l’Université de Montréal. Auparavant, il a été boursier postdoctoral au Département de statistique et d’informatique de l’Université Stanford. Il a obtenu son doctorat au département de génie électrique et informatique de l’Université du Texas à Austin. Ses recherches portent sur les problèmes d’apprentissage statistique et d’inférence à grande échelle, sur les algorithmes efficaces à grande échelle et distribués, sur les garanties théoriques et dépendantes des données et sur les systèmes complexes d’optimisation.

Publications

2020-12

A Study of Condition Numbers for First-Order Optimization
Charles Guille-Escuret, Baptiste Goujaud, Manuela Girotti and Ioannis Mitliagkas
arXiv preprint arXiv:2012.05782
(2020-12-10)
arxiv.orgPDF

2020-11

Gradient penalty from a maximum margin perspective.
Alexia Jolicoeur-Martineau and Ioannis Mitliagkas
arXiv: Learning
(2020-11-24)

2020-10

LEAD: Least-Action Dynamics for Min-Max Optimization
Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie and Ioannis Mitliagkas
arXiv preprint arXiv:2010.13846
(2020-10-26)
arxiv.orgPDF
In Search of Robust Measures of Generalization.
Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas and Daniel M. Roy
arXiv preprint arXiv:2010.11924
(2020-10-22)
arxiv.orgPDF

2020-09

Adversarial score matching and improved sampling for image generation
Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Rémi Tachet des Combes and Ioannis Mitliagkas
arXiv preprint arXiv:2009.05475
(2020-09-11)
aps.arxiv.orgPDF

2020-07

Linear Lower Bounds and Conditioning of Differentiable Games
Adam Ibrahim, Waïss Azizian, Gauthier Gidel and Ioannis Mitliagkas
Stochastic Hamiltonian Gradient Methods for Smooth Games
Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien and Ioannis Mitliagkas

2020-01

In search of robust measures of generalization
Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas and Daniel M. Roy
NEURIPS 2020
(2020-01-01)
papers.nips.ccPDF
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games.
AISTATS 2020
(2020-01-01)
proceedings.mlr.press
Accelerating Smooth Games by Manipulating Spectral Shapes.
Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien and Gauthier Gidel

2019-12

Reducing the variance in online optimization by transporting past gradients
Sébastien Arnold, Pierre-Antoine Manzagol, Reza Harikandeh, Ioannis Mitliagkas and Nicolas Le Roux
NEURIPS 2019
(2019-12-08)
papers.nips.ccPDF

2019-11

Generalizing to unseen domains via distribution matching
Isabela Albuquerque, João Monteiro, Mohammad Darvishi, Tiago H. Falk and Ioannis Mitliagkas
arXiv: Learning
(2019-11-03)
arxiv.orgPDF
Adversarial target-invariant representation learning for domain generalization
Isabela Albuquerque, João Monteiro, Mohammad Darvishi, Tiago H. Falk and Ioannis Mitliagkas
arXiv preprint arXiv:1911.00804
(2019-11-03)
ui.adsabs.harvard.edu

2019-10

Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs
Alexia Jolicoeur-Martineau and Ioannis Mitliagkas
arXiv preprint arXiv:1910.06922
(2019-10-15)
dblp.uni-trier.dePDF

2019-06

Lower Bounds and Conditioning of Differentiable Games.
Adam Ibrahim, Waïss Azizian, Gauthier Gidel and Ioannis Mitliagkas
arXiv preprint arXiv:1906.07300
(2019-06-17)
dblp.uni-trier.dePDF
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Games
arXiv preprint arXiv:1906.05945
(2019-06-13)
aps.arxiv.org
A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games.
arXiv: Learning
(2019-06-13)
dblp.uni-trier.dePDF
Multi-objective training of Generative Adversarial Networks with multiple discriminators
Isabela Albuquerque, Joao Monteiro, Thang Doan, Breandan Considine, Tiago Falk and Ioannis Mitliagkas
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio and Michael Mozer
ICML 2019
(2019-06-09)
proceedings.mlr.pressPDF
Manifold Mixup: Better Representations by Interpolating Hidden States
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz and Yoshua Bengio
ICML 2019
(2019-06-09)
proceedings.mlr.pressPDF
Reducing the variance in online optimization by transporting past gradients
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas and Nicolas Le Roux
arXiv preprint arXiv:1906.03532
(2019-06-08)
arxiv.orgPDF

2019-05

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Denis Kazakov, Yoshua Bengio and Michael C. Mozer
arXiv preprint arXiv:1905.11382
(2019-05-26)
arxiv.orgPDF
In Support of Over-Parametrization in Deep Reinforcement Learning: an Empirical Study
Brady Neal and Ioannis Mitliagkas
(venue unknown)
(2019-05-17)
openreview.net
h-detach: Modifying the LSTM Gradient Towards Better Optimization
Bhargav Kanuparthi, Devansh Arpit, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas and Yoshua Bengio
ICLR 2019
(2019-05-06)
iclr.ccPDF

2019-04

Negative Momentum for Improved Game Dynamics
Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Gabriel Huang, Rémi Le Priol, Simon Lacoste-Julien and Ioannis Mitliagkas
AISTATS 2019
(2019-04-11)
proceedings.mlr.pressPDF

2019-03

MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons... (49 more)
arXiv preprint arXiv:1904.03257
(2019-03-29)
ui.adsabs.harvard.eduPDF

2018-10

h-detach: Modifying the LSTM Gradient Towards Better Optimization.
Devansh Arpit, Bhargav Kanuparthi, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas and Yoshua Bengio
arXiv preprint arXiv:1810.03023
(2018-10-06)
arxiv.org

2018-09

A Modern Take on the Bias-Variance Tradeoff in Neural Networks
Brady Neal, Sarthak Mittal, Aristide Baratin, Vinayak Tantia, Matthew Scicluna, Simon Lacoste-Julien and Ioannis Mitliagkas
arXiv preprint arXiv:1810.08591
(2018-09-27)
ui.adsabs.harvard.eduPDF
Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations
Alex Lamb, Jonathan Binas, Anirudh Goyal, Dmitriy Serdyuk, Sandeep Subramanian, Ioannis Mitliagkas and Yoshua Bengio
arXiv preprint arXiv:1804.02485
(2018-09-27)
dblp.uni-trier.dePDF
Manifold Mixup: Learning Better Representations by Interpolating Hidden States
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Aaron Courville, Ioannis Mitliagkas and Yoshua Bengio
(venue unknown)
(2018-09-27)
arxiv.org

2018-07

Negative Momentum for Improved Game Dynamics.
Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Remi Lepriol, Gabriel Huang, Simon Lacoste-Julien and Ioannis Mitliagkas
arXiv preprint arXiv:1807.04740
(2018-07-12)
export.arxiv.orgPDF

2018-06

Manifold Mixup: Better Representations by Interpolating Hidden States.
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, Aaron Courville, David Lopez-Paz and Yoshua Bengio
arXiv preprint arXiv:1806.05236
(2018-06-13)
arxiv.orgPDF
Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer.
Vikas Verma, Alex Lamb, Christopher Beckham, Aaron C. Courville, Ioannis Mitliagkas and Yoshua Bengio
(venue unknown)
(2018-06-13)
dblp.uni-trier.dePDF

2018-03

Accelerated Stochastic Power Iteration
Peng Xu, Bryan D. He, Christopher De Sa, Ioannis Mitliagkas and Christopher Ré
AISTATS 2018
(2018-03-31)
proceedings.mlr.pressPDF

2018-02

YellowFin and the Art of Momentum Tuning
Jian Zhang, Ioannis Mitliagkas and Christopher Re
MLSys
(2018-02-15)
proceedings.mlsys.orgPDF
Learning Representations and Generative Models for 3D Point Clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas and Leonidas Guibas
Learning Generative Models with Locally Disentangled Latent Factors
Brady Neal, Alex Lamb, Sherjil Ozair, Devon Hjelm, Aaron Courville, Yoshua Bengio and Ioannis Mitliagkas
(venue unknown)
(2018-02-15)
openreview.netPDF

Publications collected and formatted using Paperoni

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