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Adversarial score matching and improved sampling for image generation

Oct 10, 2020
Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Rémi Tachet des Combes, Ioannis Mitliagkas

* Code at 

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Stochastic Hamiltonian Gradient Methods for Smooth Games

Jul 08, 2020
Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas

* ICML 2020 - Proceedings of the 37th International Conference on Machine Learning 

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Accelerating Smooth Games by Manipulating Spectral Shapes

Jan 02, 2020
Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel

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Adversarial target-invariant representation learning for domain generalization

Nov 03, 2019
Isabela Albuquerque, João Monteiro, Tiago H. Falk, Ioannis Mitliagkas

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Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs

Oct 15, 2019
Alexia Jolicoeur-Martineau, Ioannis Mitliagkas

* Code at 

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A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games

Jun 24, 2019
Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel

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Reducing the variance in online optimization by transporting past gradients

Jun 18, 2019
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux

* Open-source implementation available at: 

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Lower Bounds and Conditioning of Differentiable Games

Jun 17, 2019
Adam Ibrahim, Waïss Azizian, Gauthier Gidel, Ioannis Mitliagkas

* Submitted to NeurIPS 2019 

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State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations

May 26, 2019
Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Denis Kazakov, Yoshua Bengio, Michael C. Mozer

* ICML 2019 [full oral]. arXiv admin note: text overlap with arXiv:1805.08394 

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SysML: The New Frontier of Machine Learning Systems

May 01, 2019
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, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar

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Multi-objective training of Generative Adversarial Networks with multiple discriminators

Jan 24, 2019
Isabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine, Tiago Falk, Ioannis Mitliagkas

* The first two authors contributed equally to this work 

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A Modern Take on the Bias-Variance Tradeoff in Neural Networks

Oct 19, 2018
Brady Neal, Sarthak Mittal, Aristide Baratin, Vinayak Tantia, Matthew Scicluna, Simon Lacoste-Julien, Ioannis Mitliagkas

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Manifold Mixup: Learning Better Representations by Interpolating Hidden States

Oct 04, 2018
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Aaron Courville, Ioannis Mitliagkas, Yoshua Bengio

* ICLR2019 Under Review 

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Negative Momentum for Improved Game Dynamics

Jul 12, 2018
Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Gabriel Huang, Remi Lepriol, Simon Lacoste-Julien, Ioannis Mitliagkas

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Learning Representations and Generative Models for 3D Point Clouds

Jun 12, 2018
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas Guibas

* 35th International Conference on Machine Learning (ICML), 2018 

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Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations

Apr 07, 2018
Alex Lamb, Jonathan Binas, Anirudh Goyal, Dmitriy Serdyuk, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio

* Under Review ICML 2018 

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YellowFin and the Art of Momentum Tuning

Feb 14, 2018
Jian Zhang, Ioannis Mitliagkas

* Updated to reflect improved stability discussion and work for SysML presentation 

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Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data

Aug 17, 2017
Thorsten Kurth, Jian Zhang, Nadathur Satish, Ioannis Mitliagkas, Evan Racah, Mostofa Ali Patwary, Tareq Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey

* 12 pages, 9 figures 

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Improving Gibbs Sampler Scan Quality with DoGS

Jul 18, 2017
Ioannis Mitliagkas, Lester Mackey

* ICML 2017 

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Accelerated Stochastic Power Iteration

Jul 10, 2017
Christopher De Sa, Bryan He, Ioannis Mitliagkas, Christopher Ré, Peng Xu

* 37 pages, 5 figures 

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Asynchrony begets Momentum, with an Application to Deep Learning

Nov 25, 2016
Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis, Christopher Ré

* Full version of a paper published in Annual Allerton Conference on Communication, Control, and Computing (Allerton) 2016 

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Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs

Oct 19, 2016
Stefan Hadjis, Ce Zhang, Ioannis Mitliagkas, Dan Iter, Christopher Ré

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Parallel SGD: When does averaging help?

Jun 23, 2016
Jian Zhang, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré

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Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much

Jun 10, 2016
Bryan He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré

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Memory Limited, Streaming PCA

Jun 28, 2013
Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain

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