Ioannis Mitliagkas

112 papers A* 24A 7Misc 1Journal 71Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Adaptive Batch Sizes Using Non-Euclidean Gradient Noise Scales for Stochastic Sign and Spectral Descent. CoRR Hiroki Naganuma, Shagun Gupta, Youssef Briki, Ioannis Mitliagkas, Irina Rish, Parameswaran Raman, Hao-Jun Michael Shi
2026	J	`jnl`	Revisiting Generalization Measures Beyond IID: An Empirical Study under Distributional Shift. CoRR Sora Nakai, Youssef Fadhloun, Kacem Mathlouthi, Kotaro Yoshida, Ganesh Talluri, Ioannis Mitliagkas, Hiroki Naganuma
2025	J	`jnl`	A Derandomization Framework for Structure Discovery: Applications in Neural Networks and Beyond. CoRR Nikos Tsikouras, Yorgos Pantis, Ioannis Mitliagkas, Christos Tzamos
2025	J	`jnl`	An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration. Trans. Mach. Learn. Res. Hiroki Naganuma, Ryuichiro Hataya, Kotaro Yoshida, Ioannis Mitliagkas
2025	J	`jnl`	Beyond Multi-Token Prediction: Pretraining LLMs with Future Summaries. CoRR Divyat Mahajan, Sachin Goyal, Badr Youbi Idrissi, Mohammad Pezeshki, Ioannis Mitliagkas, David Lopez-Paz, Kartik Ahuja
2025	A*	`conf`	Compositional Risk Minimization. ICML Divyat Mahajan, Mohammad Pezeshki, Charles Arnal, Ioannis Mitliagkas, Kartik Ahuja, Pascal Vincent
2025	J	`jnl`	Learning to Defer for Causal Discovery with Imperfect Experts. CoRR Oscar Clivio, Divyat Mahajan, Perouz Taslakian, Sara Magliacane, Ioannis Mitliagkas, Valentina Zantedeschi, Alexandre Drouin
2025	J	`jnl`	Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training. CoRR Hiroki Naganuma, Xinzhi Zhang, Man-Chung Yue, Ioannis Mitliagkas, Philipp A. Witte, Russell J. Hewett, Yin Tat Lee
2025	J	`jnl`	Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training. Trans. Mach. Learn. Res. Hiroki Naganuma, Xinzhi Zhang, Man-Chung Yue, Ioannis Mitliagkas, Russell J. Hewett, Philipp A. Witte, Yin Tat Lee
2025	A*	`conf`	Solving hidden monotone variational inequalities with surrogate losses. ICLR Ryan D'Orazio, Danilo Vucetic, Zichu Liu, Junhyung Lyle Kim, Ioannis Mitliagkas, Gauthier Gidel
2024	J	`jnl`	Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition. CoRR 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
2024	J	`jnl`	Compositional Risk Minimization. CoRR Divyat Mahajan, Mohammad Pezeshki, Ioannis Mitliagkas, Kartik Ahuja, Pascal Vincent
2024	A*	`conf`	Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation. ICLR Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis
2024	A*	`conf`	Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection. NeurIPS Charles Guille-Escuret, Pierre-André Noël, Ioannis Mitliagkas, David Vázquez, João Monteiro
2024	J	`jnl`	Feature learning as alignment: a structural property of gradient descent in non-linear neural networks. Trans. Mach. Learn. Res. Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala
2024	J	`jnl`	Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching. CoRR Ange-Clément Akazan, Alexia Jolicoeur-Martineau, Ioannis Mitliagkas
2024	J	`jnl`	Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks. CoRR Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala
2024	A*	`conf`	No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths. ICML Charles Guille-Escuret, Hiroki Naganuma, Kilian Fatras, Ioannis Mitliagkas
2024	J	`jnl`	Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones. CoRR Mehrnaz Mofakhami, Reza Bayat, Ioannis Mitliagkas, João Monteiro, Valentina Zantedeschi
2024	J	`jnl`	Solving Hidden Monotone Variational Inequalities with Surrogate Losses. CoRR Ryan D'Orazio, Danilo Vucetic, Zichu Liu, Junhyung Lyle Kim, Ioannis Mitliagkas, Gauthier Gidel
2024	J	`jnl`	Understanding Adam Requires Better Rotation Dependent Assumptions. CoRR Lucas Maes, Tianyue H. Zhang, Alexia Jolicoeur-Martineau, Ioannis Mitliagkas, Damien Scieur, Simon Lacoste-Julien, Charles Guille-Escuret
2023	J	`jnl`	A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods. Trans. Mach. Learn. Res. Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam M. Oberman
2023	A*	`conf`	A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games. ICLR Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer
2023	A*	`conf`	Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation. NeurIPS Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, Simon Lacoste-Julien
2023	J	`jnl`	Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation. CoRR Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, Simon Lacoste-Julien
2023	A*	`conf`	CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning. NeurIPS Charles Guille-Escuret, Pau Rodríguez, David Vázquez, Ioannis Mitliagkas, João Monteiro
2023	J	`jnl`	Empirical Study on Optimizer Selection for Out-of-Distribution Generalization. Trans. Mach. Learn. Res. Hiroki Naganuma, Kartik Ahuja, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato, Ioannis Mitliagkas
2023	J	`jnl`	Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection. CoRR Charles Guille-Escuret, Pierre-André Noël, Ioannis Mitliagkas, David Vázquez, João Monteiro
2023	J	`jnl`	LEAD: Min-Max Optimization from a Physical Perspective. Trans. Mach. Learn. Res. Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas
2023	A*	`conf`	Neural Networks Efficiently Learn Low-Dimensional Representations with SGD. ICLR Alireza Mousavi Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu
2023	J	`jnl`	No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths. CoRR Charles Guille-Escuret, Hiroki Naganuma, Kilian Fatras, Ioannis Mitliagkas
2023	A	`conf`	Performative Prediction with Neural Networks. AISTATS Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel
2023	J	`jnl`	Performative Prediction with Neural Networks. CoRR Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel
2023	J	`jnl`	Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize. Trans. Mach. Learn. Res. Ryan D'Orazio, Nicolas Loizou, Issam H. Laradji, Ioannis Mitliagkas
2023	A*	`conf`	Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning. ICML Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand
2022	J	`jnl`	A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods. CoRR Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam M. Oberman
2022	J	`jnl`	A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games. CoRR Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer
2022	J	`jnl`	CADet: Fully Self-Supervised Anomaly Detection With Contrastive Learning. CoRR Charles Guille-Escuret, Pau Rodríguez, David Vázquez, Ioannis Mitliagkas, João Monteiro
2022	J	`jnl`	Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation. CoRR Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis
2022	J	`jnl`	Empirical Study on Optimizer Selection for Out-of-Distribution Generalization. CoRR Hiroki Naganuma, Kartik Ahuja, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato, Ioannis Mitliagkas
2022	A*	`conf`	Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound. NeurIPS Charles Guille-Escuret, Adam Ibrahim, Baptiste Goujaud, Ioannis Mitliagkas
2022	J	`jnl`	Neural Networks Efficiently Learn Low-Dimensional Representations with SGD. CoRR Alireza Mousavi Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu
2022	—	`conf`	Optimal Transport meets Noisy Label Robust Loss and MixUp Regularization for Domain Adaptation. CoLLAs Kilian Fatras, Hiroki Naganuma, Ioannis Mitliagkas
2022	J	`jnl`	Optimal transport meets noisy label robust loss and MixUp regularization for domain adaptation. CoRR Kilian Fatras, Hiroki Naganuma, Ioannis Mitliagkas
2022	J	`jnl`	Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective. CoRR Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand
2022	J	`jnl`	Towards Out-of-Distribution Adversarial Robustness. CoRR Adam Ibrahim, Charles Guille-Escuret, Ioannis Mitliagkas, Irina Rish, David Krueger, Pouya Bashivan
2022	—	`conf`	Towards efficient representation identification in supervised learning. CLeaR Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas
2022	J	`jnl`	Towards efficient representation identification in supervised learning. CoRR Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas
2021	A	`conf`	A Study of Condition Numbers for First-Order Optimization. AISTATS Charles Guille-Escuret, Manuela Girotti, Baptiste Goujaud, Ioannis Mitliagkas
2021	A*	`conf`	Adversarial score matching and improved sampling for image generation. ICLR Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Ioannis Mitliagkas, Remi Tachet des Combes
2021	J	`jnl`	Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks. CoRR Manuela Girotti, Ioannis Mitliagkas, Gauthier Gidel
2021	J	`jnl`	Gotta Go Fast When Generating Data with Score-Based Models. CoRR Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer, Tal Kachman, Ioannis Mitliagkas
2021	A*	`conf`	Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. NeurIPS Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish
2021	J	`jnl`	Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. CoRR Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish
2021	A*	`conf`	Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity. NeurIPS Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien
2021	J	`jnl`	Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity. CoRR Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien
2021	J	`jnl`	Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize. CoRR Ryan D'Orazio, Nicolas Loizou, Issam H. Laradji, Ioannis Mitliagkas
2020	J	`jnl`	A Study of Condition Numbers for First-Order Optimization. CoRR Charles Guille-Escuret, Baptiste Goujaud, Manuela Girotti, Ioannis Mitliagkas
2020	A	`conf`	A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games. AISTATS Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel
2020	A	`conf`	Accelerating Smooth Games by Manipulating Spectral Shapes. AISTATS Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel
2020	J	`jnl`	Accelerating Smooth Games by Manipulating Spectral Shapes. CoRR Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel
2020	J	`jnl`	Adversarial score matching and improved sampling for image generation. CoRR Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Rémi Tachet des Combes, Ioannis Mitliagkas
2020	J	`jnl`	In Search of Robust Measures of Generalization. CoRR Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy
2020	A*	`conf`	In search of robust measures of generalization. NeurIPS Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy
2020	J	`jnl`	LEAD: Least-Action Dynamics for Min-Max Optimization. CoRR Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas
2020	A*	`conf`	Linear Lower Bounds and Conditioning of Differentiable Games. ICML Adam Ibrahim, Waïss Azizian, Gauthier Gidel, Ioannis Mitliagkas
2020	A*	`conf`	Stochastic Hamiltonian Gradient Methods for Smooth Games. ICML Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas
2020	J	`jnl`	Stochastic Hamiltonian Gradient Methods for Smooth Games. CoRR Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas
2019	J	`jnl`	A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games. CoRR Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel
2019	J	`jnl`	Adversarial target-invariant representation learning for domain generalization. CoRR Isabela Albuquerque, João Monteiro, Tiago H. Falk, Ioannis Mitliagkas
2019	J	`jnl`	Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs. CoRR Alexia Jolicoeur-Martineau, Ioannis Mitliagkas
2019	J	`jnl`	Lower Bounds and Conditioning of Differentiable Games. CoRR Adam Ibrahim, Waïss Azizian, Gauthier Gidel, Ioannis Mitliagkas
2019	A*	`conf`	Manifold Mixup: Better Representations by Interpolating Hidden States. ICML Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio
2019	A*	`conf`	Multi-objective training of Generative Adversarial Networks with multiple discriminators. ICML Isabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine, Tiago H. Falk, Ioannis Mitliagkas
2019	J	`jnl`	Multi-objective training of Generative Adversarial Networks with multiple discriminators. CoRR Isabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine, Tiago H. Falk, Ioannis Mitliagkas
2019	A	`conf`	Negative Momentum for Improved Game Dynamics. AISTATS Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Rémi Le Priol, Gabriel Huang, Simon Lacoste-Julien, Ioannis Mitliagkas
2019	A*	`conf`	Reducing the variance in online optimization by transporting past gradients. NeurIPS Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux
2019	J	`jnl`	Reducing the variance in online optimization by transporting past gradients. CoRR Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux
2019	A*	`conf`	State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. ICML Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio, Michael Mozer
2019	J	`jnl`	State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. CoRR Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Denis Kazakov, Yoshua Bengio, Michael C. Mozer
2019	J	`jnl`	SysML: The New Frontier of Machine Learning Systems. CoRR 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 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
2019	—	`conf`	YellowFin and the Art of Momentum Tuning. SysML Jian Zhang, Ioannis Mitliagkas
2019	—	`conf`	h-detach: Modifying the LSTM Gradient Towards Better Optimization. ICLR (Poster) Bhargav Kanuparthi, Devansh Arpit, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas, Yoshua Bengio
2018	J	`jnl`	A Modern Take on the Bias-Variance Tradeoff in Neural Networks. CoRR Brady Neal, Sarthak Mittal, Aristide Baratin, Vinayak Tantia, Matthew Scicluna, Simon Lacoste-Julien, Ioannis Mitliagkas
2018	A	`conf`	Accelerated Stochastic Power Iteration. AISTATS Peng Xu, Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré
2018	J	`jnl`	Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations. CoRR Alex Lamb, Jonathan Binas, Anirudh Goyal, Dmitriy Serdyuk, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio
2018	—	`conf`	Learning Representations and Generative Models for 3D Point Clouds. ICLR (Workshop) Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas
2018	A*	`conf`	Learning Representations and Generative Models for 3D Point Clouds. ICML Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas
2018	J	`jnl`	Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer. CoRR Vikas Verma, Alex Lamb, Christopher Beckham, Aaron C. Courville, Ioannis Mitliagkas, Yoshua Bengio
2018	J	`jnl`	Negative Momentum for Improved Game Dynamics. CoRR Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Gabriel Huang, Rémi Le Priol, Simon Lacoste-Julien, Ioannis Mitliagkas
2018	J	`jnl`	h-detach: Modifying the LSTM Gradient Towards Better Optimization. CoRR Devansh Arpit, Bhargav Kanuparthi, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas, Yoshua Bengio
2017	J	`jnl`	Accelerated Stochastic Power Iteration. CoRR Christopher De Sa, Bryan D. He, Ioannis Mitliagkas, Christopher Ré, Peng Xu
2017	J	`jnl`	Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data. CoRR 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
2017	A	`conf`	Deep learning at 15PF: supervised and semi-supervised classification for scientific data. SC 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
2017	A*	`conf`	Improving Gibbs Sampler Scan Quality with DoGS. ICML Ioannis Mitliagkas, Lester W. Mackey
2017	J	`jnl`	Improving Gibbs Sampler Scan Quality with DoGS. CoRR Ioannis Mitliagkas, Lester W. Mackey
2017	J	`jnl`	Representation Learning and Adversarial Generation of 3D Point Clouds. CoRR Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas
2017	J	`jnl`	YellowFin and the Art of Momentum Tuning. CoRR Jian Zhang, Ioannis Mitliagkas, Christopher Ré
2016	J	`jnl`	Asynchrony begets Momentum, with an Application to Deep Learning. CoRR Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis, Christopher Ré
2016	—	`conf`	Asynchrony begets momentum, with an application to deep learning. Allerton Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis, Christopher Ré
2016	J	`jnl`	Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs. CoRR Stefan Hadjis, Ce Zhang, Ioannis Mitliagkas, Christopher Ré
2016	J	`jnl`	Parallel SGD: When does averaging help? CoRR Jian Zhang, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré
2016	—	`conf`	Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much. NIPS Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré
2016	J	`jnl`	Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much. CoRR Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré
2015	J	`jnl`	FrogWild! - Fast PageRank Approximations on Graph Engines. CoRR Ioannis Mitliagkas, Michael Borokhovich, Alexandros G. Dimakis, Constantine Caramanis
2015	J	`jnl`	FrogWild! - Fast PageRank Approximations on Graph Engines. Proc. VLDB Endow. Ioannis Mitliagkas, Michael Borokhovich, Alexandros G. Dimakis, Constantine Caramanis
2014	A*	`conf`	Finding Dense Subgraphs via Low-Rank Bilinear Optimization. ICML Dimitris S. Papailiopoulos, Ioannis Mitliagkas, Alexandros G. Dimakis, Constantine Caramanis
2013	—	`conf`	Memory Limited, Streaming PCA. NIPS Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain
2013	J	`jnl`	Memory Limited, Streaming PCA. CoRR Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain
2011	J	`jnl`	Joint Power and Admission Control for Ad-Hoc and Cognitive Underlay Networks: Convex Approximation and Distributed Implementation. IEEE Trans. Wirel. Commun. Ioannis Mitliagkas, Nicholas D. Sidiropoulos, Ananthram Swami
2011	—	`conf`	User rankings from comparisons: Learning permutations in high dimensions. Allerton Ioannis Mitliagkas, Aditya Gopalan, Constantine Caramanis, Sriram Vishwanath
2010	Misc	`conf`	Distributed joint power and admission control for ad-hoc and cognitive underlay networks. ICASSP Ioannis Mitliagkas, Nicholas D. Sidiropoulos, Ananthram Swami