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2020 – today
- 2020
[c23]Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel:
Accelerating Smooth Games by Manipulating Spectral Shapes. AISTATS 2020: 1705-1715- [c22]Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel:
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games. AISTATS 2020: 2863-2873 - [c21]Adam Ibrahim, Waïss Azizian, Gauthier Gidel, Ioannis Mitliagkas:
Linear Lower Bounds and Conditioning of Differentiable Games. ICML 2020: 4583-4593 - [c20]Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas:
Stochastic Hamiltonian Gradient Methods for Smooth Games. ICML 2020: 6370-6381 - [c19]Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy:
In search of robust measures of generalization. NeurIPS 2020 - [i30]Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel:
Accelerating Smooth Games by Manipulating Spectral Shapes. CoRR abs/2001.00602 (2020) - [i29]Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas:
Stochastic Hamiltonian Gradient Methods for Smooth Games. CoRR abs/2007.04202 (2020) - [i28]Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Rémi Tachet des Combes, Ioannis Mitliagkas:
Adversarial score matching and improved sampling for image generation. CoRR abs/2009.05475 (2020) - [i27]Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy:
In Search of Robust Measures of Generalization. CoRR abs/2010.11924 (2020) - [i26]Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas:
LEAD: Least-Action Dynamics for Min-Max Optimization. CoRR abs/2010.13846 (2020) - [i25]Charles Guille-Escuret, Baptiste Goujaud, Manuela Girotti, Ioannis Mitliagkas:
A Study of Condition Numbers for First-Order Optimization. CoRR abs/2012.05782 (2020)
2010 – 2019
- 2019
- [c18]Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Rémi Le Priol, Gabriel Huang, Simon Lacoste-Julien, Ioannis Mitliagkas:
Negative Momentum for Improved Game Dynamics. AISTATS 2019: 1802-1811 - [c17]Bhargav Kanuparthi, Devansh Arpit, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas, Yoshua Bengio:
h-detach: Modifying the LSTM Gradient Towards Better Optimization. ICLR (Poster) 2019 - [c16]Isabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine, Tiago H. Falk, Ioannis Mitliagkas:
Multi-objective training of Generative Adversarial Networks with multiple discriminators. ICML 2019: 202-211 - [c15]Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio, Michael Mozer:
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. ICML 2019: 3622-3631 - [c14]Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio:
Manifold Mixup: Better Representations by Interpolating Hidden States. ICML 2019: 6438-6447 - [c13]Jian Zhang, Ioannis Mitliagkas:
YellowFin and the Art of Momentum Tuning. MLSys 2019 - [c12]Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux:
Reducing the variance in online optimization by transporting past gradients. NeurIPS 2019: 5392-5403 - [i24]Isabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine, Tiago H. Falk, Ioannis Mitliagkas:
Multi-objective training of Generative Adversarial Networks with multiple discriminators. CoRR abs/1901.08680 (2019) - [i23]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. 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 M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan R. Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i22]Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Denis Kazakov, Yoshua Bengio, Michael C. Mozer:
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. CoRR abs/1905.11382 (2019) - [i21]Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux:
Reducing the variance in online optimization by transporting past gradients. CoRR abs/1906.03532 (2019) - [i20]Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel:
A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games. CoRR abs/1906.05945 (2019) - [i19]Adam Ibrahim, Waïss Azizian, Gauthier Gidel, Ioannis Mitliagkas:
Lower Bounds and Conditioning of Differentiable Games. CoRR abs/1906.07300 (2019) - [i18]Alexia Jolicoeur-Martineau, Ioannis Mitliagkas:
Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs. CoRR abs/1910.06922 (2019) - [i17]Isabela Albuquerque, João Monteiro, Tiago H. Falk, Ioannis Mitliagkas:
Adversarial target-invariant representation learning for domain generalization. CoRR abs/1911.00804 (2019) - 2018
- [c11]Peng Xu, Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré:
Accelerated Stochastic Power Iteration. AISTATS 2018: 58-67 - [c10]Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas:
Learning Representations and Generative Models for 3D Point Clouds. ICLR (Workshop) 2018 - [c9]Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas:
Learning Representations and Generative Models for 3D Point Clouds. ICML 2018: 40-49 - [i16]Alex Lamb, Jonathan Binas, Anirudh Goyal, Dmitriy Serdyuk, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio:
Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations. CoRR abs/1804.02485 (2018) - [i15]Vikas Verma, Alex Lamb, Christopher Beckham, Aaron C. Courville, Ioannis Mitliagkas, Yoshua Bengio:
Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer. CoRR abs/1806.05236 (2018) - [i14]Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Gabriel Huang, Rémi Le Priol, Simon Lacoste-Julien, Ioannis Mitliagkas:
Negative Momentum for Improved Game Dynamics. CoRR abs/1807.04740 (2018) - [i13]Devansh Arpit, Bhargav Kanuparthi, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas, Yoshua Bengio:
h-detach: Modifying the LSTM Gradient Towards Better Optimization. CoRR abs/1810.03023 (2018) - [i12]Brady Neal, Sarthak Mittal, Aristide Baratin, Vinayak Tantia, Matthew Scicluna, Simon Lacoste-Julien, Ioannis Mitliagkas:
A Modern Take on the Bias-Variance Tradeoff in Neural Networks. CoRR abs/1810.08591 (2018) - 2017
- [c8]Ioannis Mitliagkas, Lester W. Mackey:
Improving Gibbs Sampler Scan Quality with DoGS. ICML 2017: 2469-2477 - [c7]Thorsten Kurth, Jian Zhang, Nadathur Satish, Evan Racah, Ioannis Mitliagkas, Md. Mostofa Ali Patwary, Tareq M. Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey:
Deep learning at 15PF: supervised and semi-supervised classification for scientific data. SC 2017: 7:1-7:11 - [i11]Jian Zhang, Ioannis Mitliagkas, Christopher Ré:
YellowFin and the Art of Momentum Tuning. CoRR abs/1706.03471 (2017) - [i10]Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas:
Representation Learning and Adversarial Generation of 3D Point Clouds. CoRR abs/1707.02392 (2017) - [i9]Christopher De Sa, Bryan D. He, Ioannis Mitliagkas, Christopher Ré, Peng Xu:
Accelerated Stochastic Power Iteration. CoRR abs/1707.02670 (2017) - [i8]Ioannis Mitliagkas, Lester W. Mackey:
Improving Gibbs Sampler Scan Quality with DoGS. CoRR abs/1707.05807 (2017) - [i7]Thorsten Kurth, Jian Zhang, Nadathur Satish, Ioannis Mitliagkas, Evan Racah, Md. Mostofa Ali Patwary, Tareq M. Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey:
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data. CoRR abs/1708.05256 (2017) - 2016
- [c6]Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis, Christopher Ré:
Asynchrony begets momentum, with an application to deep learning. Allerton 2016: 997-1004 - [c5]Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré:
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much. NIPS 2016: 1-9 - [i6]Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis, Christopher Ré:
Asynchrony begets Momentum, with an Application to Deep Learning. CoRR abs/1605.09774 (2016) - [i5]Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré:
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much. CoRR abs/1606.03432 (2016) - [i4]Stefan Hadjis, Ce Zhang, Ioannis Mitliagkas, Christopher Ré:
Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs. CoRR abs/1606.04487 (2016) - [i3]Jian Zhang, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré:
Parallel SGD: When does averaging help? CoRR abs/1606.07365 (2016) - 2015
- [j2]Ioannis Mitliagkas, Michael Borokhovich, Alexandros G. Dimakis, Constantine Caramanis:
FrogWild! - Fast PageRank Approximations on Graph Engines. Proc. VLDB Endow. 8(8): 874-885 (2015) - [i2]Ioannis Mitliagkas, Michael Borokhovich, Alexandros G. Dimakis, Constantine Caramanis:
FrogWild! - Fast PageRank Approximations on Graph Engines. CoRR abs/1502.04281 (2015) - 2014
- [c4]Dimitris S. Papailiopoulos, Ioannis Mitliagkas, Alexandros G. Dimakis, Constantine Caramanis:
Finding Dense Subgraphs via Low-Rank Bilinear Optimization. ICML 2014: 1890-1898 - 2013
- [c3]Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain:
Memory Limited, Streaming PCA. NIPS 2013: 2886-2894 - [i1]Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain:
Memory Limited, Streaming PCA. CoRR abs/1307.0032 (2013) - 2011
- [j1]Ioannis Mitliagkas, Nicholas D. Sidiropoulos
, Ananthram Swami:
Joint Power and Admission Control for Ad-Hoc and Cognitive Underlay Networks: Convex Approximation and Distributed Implementation. IEEE Trans. Wirel. Commun. 10(12): 4110-4121 (2011) - [c2]Ioannis Mitliagkas, Aditya Gopalan, Constantine Caramanis, Sriram Vishwanath:
User rankings from comparisons: Learning permutations in high dimensions. Allerton 2011: 1143-1150 - 2010
- [c1]Ioannis Mitliagkas, Nicholas D. Sidiropoulos, Ananthram Swami:
Distributed joint power and admission control for ad-hoc and cognitive underlay networks. ICASSP 2010: 3014-3017
Coauthor Index
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