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
Gauthier GidelAssistant professor at Mila, University of Montréal (DIRO)Verified email at umontreal.ca
Alex LambMicrosoft Research (NYC), Université de Montréal, Google Brain, Amazon, Twitch PhD FellowVerified email at microsoft.com
Constantine CaramanisProfessor of Electrical and Computer Engineering, UT AustinVerified email at utexas.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 University/ MILA/ Google BrainVerified email at google.com
Panos AchlioptasSnap Inc., StanfordVerified email at stanford.edu
Brady NealPhD Student, Mila - Quebec Artificial Intelligence InstituteVerified email at mila.quebec
Alexia Jolicoeur-MartineauMilaVerified email at mail.mcgill.ca
Prateek JainGoogle Research IndiaVerified email at google.com
Christopher De SaAssistant Professor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
Reyhane Askari HemmatMila, University of MontrealVerified email at umontreal.ca
Nicolas LoizouJohns Hopkins UniversityVerified email at jhu.edu
Nikolaos SidiropoulosLouis T. Rader Professor and Chair, Electrical & Computer Engineering, University of VirginiaVerified email at virginia.edu
Stefan HadjisStanford UniversityVerified email at stanford.edu
Ce ZhangETH ZurichVerified email at inf.ethz.ch

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	736	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	515	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	515	2018
Memory limited, streaming PCA I Mitliagkas, C Caramanis, P Jain Advances in neural information processing systems 26, 2013	162	2013
Negative momentum for improved game dynamics G Gidel, RA Hemmat, M Pezeshki, G Huang, R Lepriol, S Lacoste-Julien, ... Artificial Intelligence and Statistics, 2019	136	2019
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	136	2016
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	111	2018
Yellowfin and the art of momentum tuning J Zhang, I Mitliagkas SysML, 2019	96	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	83	2011
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	80	2017
Representation learning and adversarial generation of 3D point clouds P Achlioptas, O Diamanti, I Mitliagkas, L Guibas arXiv preprint arXiv:1707.02392, 2017	79	2017
Parallel SGD: When does averaging help? J Zhang, C De Sa, I Mitliagkas, C Ré arXiv preprint arXiv:1606.07365, 2016	78	2016
Manifold mixup: Learning better representations by interpolating hidden states V Verma, A Lamb, C Beckham, A Najafi, A Courville, I Mitliagkas, ...	75*	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	65	2020
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	63	2016
Generalizing to unseen domains via distribution matching I Albuquerque, J Monteiro, M Darvishi, TH Falk, I Mitliagkas arXiv preprint arXiv:1911.00804, 2019	60*	2019
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	57	2018
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	47	2018
Linear Lower Bounds and Conditioning of Differentiable Games A Ibrahim, W Azizian, G Gidel, I Mitliagkas arXiv preprint arXiv:1906.07300, 2019	38	2019
FrogWild!--fast PageRank approximations on graph engines I Mitliagkas, M Borokhovich, AG Dimakis, C Caramanis arXiv preprint arXiv:1502.04281, 2015	38	2015

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