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2020 – today
- 2024
[c38]Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis:
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation. ICLR 2024- [c37]Charles Guille-Escuret, Hiroki Naganuma, Kilian Fatras, Ioannis Mitliagkas:
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths. ICML 2024 - [i51]Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala:
Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks. CoRR abs/2402.05271 (2024) - [i50]Eleni Triantafillou, Peter Kairouz, Fabian Pedregosa, Jamie Hayes, Meghdad Kurmanji, Kairan Zhao, Vincent Dumoulin, Júlio C. S. Jacques Júnior, Ioannis Mitliagkas, Jun Wan, Lisheng Sun-Hosoya, Sergio Escalera, Gintare Karolina Dziugaite, Peter Triantafillou, Isabelle Guyon:
Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition. CoRR abs/2406.09073 (2024) - 2023
- [j6]Ryan D'Orazio, Nicolas Loizou, Issam H. Laradji, Ioannis Mitliagkas:
Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize. Trans. Mach. Learn. Res. 2023 (2023) - [j5]Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas:
LEAD: Min-Max Optimization from a Physical Perspective. Trans. Mach. Learn. Res. 2023 (2023) - [j4]Hiroki Naganuma, Kartik Ahuja, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato, Ioannis Mitliagkas:
Empirical Study on Optimizer Selection for Out-of-Distribution Generalization. Trans. Mach. Learn. Res. 2023 (2023) - [j3]Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam M. Oberman:
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods. Trans. Mach. Learn. Res. 2023 (2023) - [c36]Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel:
Performative Prediction with Neural Networks. AISTATS 2023: 11079-11093 - [c35]Alireza Mousavi Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu:
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD. ICLR 2023 - [c34]Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer:
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games. ICLR 2023 - [c33]Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand:
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning. ICML 2023: 18171-18206 - [c32]Charles Guille-Escuret, Pau Rodríguez, David Vázquez, Ioannis Mitliagkas, João Monteiro:
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning. NeurIPS 2023 - [c31]Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, Simon Lacoste-Julien:
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation. NeurIPS 2023 - [i49]Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel:
Performative Prediction with Neural Networks. CoRR abs/2304.06879 (2023) - [i48]Charles Guille-Escuret, Hiroki Naganuma, Kilian Fatras, Ioannis Mitliagkas:
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths. CoRR abs/2306.11922 (2023) - [i47]Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, Simon Lacoste-Julien:
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation. CoRR abs/2307.02598 (2023) - [i46]Charles Guille-Escuret, Pierre-André Noël, Ioannis Mitliagkas, David Vázquez, João Monteiro:
Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection. CoRR abs/2308.11480 (2023) - 2022
- [c30]Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas:
Towards efficient representation identification in supervised learning. CLeaR 2022: 19-43 - [c29]Kilian Fatras, Hiroki Naganuma, Ioannis Mitliagkas:
Optimal Transport meets Noisy Label Robust Loss and MixUp Regularization for Domain Adaptation. CoLLAs 2022: 966-981 - [c28]Charles Guille-Escuret, Adam Ibrahim, Baptiste Goujaud, Ioannis Mitliagkas:
Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound. NeurIPS 2022 - [i45]Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas:
Towards efficient representation identification in supervised learning. CoRR abs/2204.04606 (2022) - [i44]Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer:
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games. CoRR abs/2206.05825 (2022) - [i43]Kilian Fatras, Hiroki Naganuma, Ioannis Mitliagkas:
Optimal transport meets noisy label robust loss and MixUp regularization for domain adaptation. CoRR abs/2206.11180 (2022) - [i42]Alireza Mousavi Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu:
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD. CoRR abs/2209.14863 (2022) - [i41]Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam M. Oberman:
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods. CoRR abs/2210.01210 (2022) - [i40]Charles Guille-Escuret, Pau Rodríguez, David Vázquez, Ioannis Mitliagkas, João Monteiro:
CADet: Fully Self-Supervised Anomaly Detection With Contrastive Learning. CoRR abs/2210.01742 (2022) - [i39]Adam Ibrahim, Charles Guille-Escuret, Ioannis Mitliagkas, Irina Rish, David Krueger, Pouya Bashivan:
Towards Out-of-Distribution Adversarial Robustness. CoRR abs/2210.03150 (2022) - [i38]Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis:
Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation. CoRR abs/2211.01939 (2022) - [i37]Hiroki Naganuma
, Kartik Ahuja, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato, Ioannis Mitliagkas:
Empirical Study on Optimizer Selection for Out-of-Distribution Generalization. CoRR abs/2211.08583 (2022) - [i36]Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand:
Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective. CoRR abs/2211.14666 (2022) - 2021
- [c27]Charles Guille-Escuret, Manuela Girotti, Baptiste Goujaud, Ioannis Mitliagkas:
A Study of Condition Numbers for First-Order Optimization. AISTATS 2021: 1261-1269 - [c26]Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Ioannis Mitliagkas, Remi Tachet des Combes:
Adversarial score matching and improved sampling for image generation. ICLR 2021 - [c25]Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish:
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. NeurIPS 2021: 3438-3450 - [c24]Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien:
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity. NeurIPS 2021: 19095-19108 - [i35]Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer, Tal Kachman, Ioannis Mitliagkas:
Gotta Go Fast When Generating Data with Score-Based Models. CoRR abs/2105.14080 (2021) - [i34]Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish:
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. CoRR abs/2106.06607 (2021) - [i33]Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien:
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity. CoRR abs/2107.00052 (2021) - [i32]Manuela Girotti, Ioannis Mitliagkas, Gauthier Gidel:
Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks. CoRR abs/2110.10815 (2021) - [i31]Ryan D'Orazio, Nicolas Loizou, Issam H. Laradji, Ioannis Mitliagkas:
Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize. CoRR abs/2110.15412 (2021) - 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. SysML 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 S. 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 Randall 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 - [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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