Ioannis (Yannis) Mitliagkas

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Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
Simon Lacoste-JulienAssociate Professor - Canada CIFAR AI Chair, University of Montreal / MilaVerified email at iro.umontreal.ca
Alex LambUniversité de Montréal, Google Brain, Amazon, Twitch PhD FellowVerified email at amazon.com
Constantine CaramanisProfessor of Electrical and Computer Engineering, UT AustinVerified email at utexas.edu
Gauthier GidelAssistant professor at Mila, University of Montréal (DIRO)Verified email at umontreal.ca
Jian ZhangPhD student, Stanford UniversityVerified email at stanford.edu
Christopher RéComputer Science, Stanford UniversityVerified email at cs.stanford.edu
Leonidas GuibasProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Olga DiamantiAssistant ProfessorVerified email at tugraz.at
Christopher BeckhamInstitut Québécois d'Intelligence ArtificielleVerified email at polymtl.ca
Vikas VermaAalto UniversityVerified email at aalto.fi
Panos AchlioptasStanfordVerified email at stanford.edu
Brady NealPhD Student, Mila - Quebec Artificial Intelligence InstituteVerified email at mila.quebec
Prateek JainMicrosoft Research IndiaVerified email at microsoft.com
Christopher De SaAssistant Professor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
Nikolaos SidiropoulosLouis T. Rader Professor and Chair, Electrical & Computer Engineering, University of VirginiaVerified email at virginia.edu
Reyhane Askari HemmatMila, University of MontrealVerified email at umontreal.ca
Ce ZhangETH ZurichVerified email at inf.ethz.ch
Alexia Jolicoeur-MartineauMilaVerified email at mail.mcgill.ca
Waïss AzizianStudent, Ecole Normale Supérieure, ParisVerified email at ens.fr

Ioannis (Yannis) Mitliagkas

Assistant Professor at Mila, University of Montréal

Verified email at iro.umontreal.ca - Homepage

Machine Learning Optimization


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Learning Representations and Generative Models for 3D Point Clouds P Achlioptas, O Diamanti, I Mitliagkas, L Guibas International Conference on Machine Learning, 2018	471	2018
Manifold mixup: Better representations by interpolating hidden states V Verma, A Lamb, C Beckham, A Najafi, I Mitliagkas, A Courville, ... arXiv preprint arXiv:1806.05236, 2018	253	2018
Manifold mixup: Better representations by interpolating hidden states V Verma, A Lamb, C Beckham, A Najafi, I Mitliagkas, A Courville, ... arXiv preprint arXiv:1806.05236, 2018	253	2018
Memory limited, streaming PCA I Mitliagkas, C Caramanis, P Jain arXiv preprint arXiv:1307.0032, 2013	164	2013
Asynchrony begets momentum, with an application to deep learning I Mitliagkas, C Zhang, S Hadjis, C Ré 2016 54th Annual Allerton Conference on Communication, Control, and …, 2016	112	2016
Negative momentum for improved game dynamics G Gidel, RA Hemmat, M Pezeshki, G Huang, R Lepriol, S Lacoste-Julien, ... Artificial Intelligence and Statistics, 2019	103	2019
Yellowfin and the art of momentum tuning J Zhang, I Mitliagkas SysML, 2019	82	2019
Joint power and admission control for ad-hoc and cognitive underlay networks: Convex approximation and distributed implementation I Mitliagkas, ND Sidiropoulos, A Swami IEEE Transactions on Wireless Communications 10 (12), 4110-4121, 2011	82	2011
A modern take on the bias-variance tradeoff in neural networks B Neal, S Mittal, A Baratin, V Tantia, M Scicluna, S Lacoste-Julien, ... arXiv preprint arXiv:1810.08591, 2018	73	2018
Deep learning at 15pf: supervised and semi-supervised classification for scientific data T Kurth, J Zhang, N Satish, E Racah, I Mitliagkas, MMA Patwary, T Malas, ... Proceedings of the International Conference for High Performance Computing …, 2017	70	2017
Representation learning and adversarial generation of 3D point clouds P Achlioptas, O Diamanti, I Mitliagkas, L Guibas arXiv preprint arXiv:1707.02392, 2017	67	2017
Parallel SGD: When does averaging help? J Zhang, C De Sa, I Mitliagkas, C Ré arXiv preprint arXiv:1606.07365, 2016	67	2016
Manifold mixup: Learning better representations by interpolating hidden states V Verma, A Lamb, C Beckham, A Najafi, A Courville, I Mitliagkas, ...	61*	2018
Omnivore: An optimizer for multi-device deep learning on cpus and gpus S Hadjis, C Zhang, I Mitliagkas, D Iter, C Ré arXiv preprint arXiv:1606.04487, 2016	53	2016
Accelerated stochastic power iteration P Xu, B He, C De Sa, I Mitliagkas, C Re International Conference on Artificial Intelligence and Statistics, 58-67, 2018	48	2018
A tight and unified analysis of gradient-based methods for a whole spectrum of differentiable games W Azizian, I Mitliagkas, S Lacoste-Julien, G Gidel International Conference on Artificial Intelligence and Statistics, 2863-2873, 2020	46	2020
FrogWild!--fast PageRank approximations on graph engines I Mitliagkas, M Borokhovich, AG Dimakis, C Caramanis arXiv preprint arXiv:1502.04281, 2015	37	2015
Fortified networks: Improving the robustness of deep networks by modeling the manifold of hidden representations A Lamb, J Binas, A Goyal, D Serdyuk, S Subramanian, I Mitliagkas, ... arXiv preprint arXiv:1804.02485, 2018	35	2018
Convex approximation-based joint power and admission control for cognitive underlay networks I Mitliagkas, ND Sidiropoulos, A Swami 2008 International Wireless Communications and Mobile Computing Conference …, 2008	31	2008
Streaming PCA with many missing entries I Mitliagkas, C Caramanis, P Jain Preprint, 2014	29	2014

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