Ioannis Mitliagkas

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 86

Top Topics

Convergence Analysis

Top Venues

Publications

Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala
Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks. CoRR (2024)
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 (2024)
Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation. ICLR (2024)
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)
Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel
Performative Prediction with Neural Networks. CoRR (2023)
Charles Guille-Escuret, Pierre-André Noël, Ioannis Mitliagkas, David Vázquez, João Monteiro
Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection. CoRR (2023)
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)
Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel
Performative Prediction with Neural Networks. AISTATS (2023)
Charles Guille-Escuret, Pau Rodríguez, David Vázquez, Ioannis Mitliagkas, João Monteiro
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning. NeurIPS (2023)
Charles Guille-Escuret, Hiroki Naganuma, Kilian Fatras, Ioannis Mitliagkas
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths. CoRR (2023)
Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas
LEAD: Min-Max Optimization from a Physical Perspective. Trans. Mach. Learn. Res. 2023 (2023)
Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, Simon Lacoste-Julien
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation. CoRR (2023)
Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, Simon Lacoste-Julien
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation. NeurIPS (2023)
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)
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)
Alireza Mousavi Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD. ICLR (2023)
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)
Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis
Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation. CoRR (2022)
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 (2022)
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 (2022)
Kilian Fatras, Hiroki Naganuma, Ioannis Mitliagkas
Optimal Transport meets Noisy Label Robust Loss and MixUp Regularization for Domain Adaptation. CoLLAs (2022)
Alireza Mousavi Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD. CoRR (2022)
Kilian Fatras, Hiroki Naganuma, Ioannis Mitliagkas
Optimal transport meets noisy label robust loss and MixUp regularization for domain adaptation. CoRR (2022)
Charles Guille-Escuret, Adam Ibrahim, Baptiste Goujaud, Ioannis Mitliagkas
Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound. NeurIPS (2022)
Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas
Towards efficient representation identification in supervised learning. CLeaR (2022)
Charles Guille-Escuret, Pau Rodríguez, David Vázquez, Ioannis Mitliagkas, Joao Monteiro
CADet: Fully Self-Supervised Anomaly Detection With Contrastive Learning. CoRR (2022)
Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam M. Oberman
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods. CoRR (2022)
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 (2022)
Adam Ibrahim, Charles Guille-Escuret, Ioannis Mitliagkas, Irina Rish, David Krueger, Pouya Bashivan
Towards Out-of-Distribution Adversarial Robustness. CoRR (2022)
Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas
Towards efficient representation identification in supervised learning. CoRR (2022)
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 (2021)
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)
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)
Charles Guille-Escuret, Manuela Girotti, Baptiste Goujaud, Ioannis Mitliagkas
A Study of Condition Numbers for First-Order Optimization. AISTATS (2021)
Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Ioannis Mitliagkas, Remi Tachet des Combes
Adversarial score matching and improved sampling for image generation. ICLR (2021)
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 (2021)
Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer, Tal Kachman, Ioannis Mitliagkas
Gotta Go Fast When Generating Data with Score-Based Models. CoRR (2021)
Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. CoRR (2021)
Manuela Girotti, Ioannis Mitliagkas, Gauthier Gidel
Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks. CoRR (2021)
Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas
Stochastic Hamiltonian Gradient Methods for Smooth Games. ICML (2020)
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)
Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel
Accelerating Smooth Games by Manipulating Spectral Shapes. AISTATS (2020)
Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Rémi Tachet des Combes, Ioannis Mitliagkas
Adversarial score matching and improved sampling for image generation. CoRR (2020)
Charles Guille-Escuret, Baptiste Goujaud, Manuela Girotti, Ioannis Mitliagkas
A Study of Condition Numbers for First-Order Optimization. CoRR (2020)
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 (2020)
Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas
LEAD: Least-Action Dynamics for Min-Max Optimization. CoRR (2020)
Adam Ibrahim, Waïss Azizian, Gauthier Gidel, Ioannis Mitliagkas
Linear Lower Bounds and Conditioning of Differentiable Games. ICML (2020)
Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas
Stochastic Hamiltonian Gradient Methods for Smooth Games. CoRR (2020)
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)
Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel
Accelerating Smooth Games by Manipulating Spectral Shapes. CoRR (2020)
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)
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 (2019)
Isabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine, Tiago H. Falk, Ioannis Mitliagkas
Multi-objective training of Generative Adversarial Networks with multiple discriminators. CoRR (2019)
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)
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)
Jian Zhang, Ioannis Mitliagkas
YellowFin and the Art of Momentum Tuning. MLSys (2019)
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)
Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel
A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games. CoRR (2019)
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)
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 (2019)
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 (2019)
Alexia Jolicoeur-Martineau, Ioannis Mitliagkas
Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs. CoRR (2019)
Adam Ibrahim, Waïss Azizian, Gauthier Gidel, Ioannis Mitliagkas
Lower Bounds and Conditioning of Differentiable Games. CoRR (2019)
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)
Isabela Albuquerque, João Monteiro, Tiago H. Falk, Ioannis Mitliagkas
Adversarial target-invariant representation learning for domain generalization. CoRR (2019)
Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Gabriel Huang, Rémi Le Priol, Simon Lacoste-Julien, Ioannis Mitliagkas
Negative Momentum for Improved Game Dynamics. CoRR (2018)
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 (2018)
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas
Learning Representations and Generative Models for 3D Point Clouds. ICML (2018)
Vikas Verma, Alex Lamb, Christopher Beckham, Aaron C. Courville, Ioannis Mitliagkas, Yoshua Bengio
Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer. CoRR (2018)
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 (2018)
Peng Xu, Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré
Accelerated Stochastic Power Iteration. AISTATS (2018)
Devansh Arpit, Bhargav Kanuparthi, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas, Yoshua Bengio
h-detach: Modifying the LSTM Gradient Towards Better Optimization. CoRR (2018)
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas
Learning Representations and Generative Models for 3D Point Clouds. ICLR (Workshop) (2018)
Ioannis Mitliagkas, Lester W. Mackey
Improving Gibbs Sampler Scan Quality with DoGS. CoRR (2017)
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)
Jian Zhang, Ioannis Mitliagkas, Christopher Ré
YellowFin and the Art of Momentum Tuning. CoRR (2017)
Ioannis Mitliagkas, Lester W. Mackey
Improving Gibbs Sampler Scan Quality with DoGS. ICML (2017)
Christopher De Sa, Bryan D. He, Ioannis Mitliagkas, Christopher Ré, Peng Xu
Accelerated Stochastic Power Iteration. CoRR (2017)
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas
Representation Learning and Adversarial Generation of 3D Point Clouds. CoRR (2017)
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 (2017)
Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis, Christopher Ré
Asynchrony begets Momentum, with an Application to Deep Learning. CoRR (2016)
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)
Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis, Christopher Ré
Asynchrony begets momentum, with an application to deep learning. Allerton (2016)
Stefan Hadjis, Ce Zhang, Ioannis Mitliagkas, Christopher Ré
Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs. CoRR (2016)
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 (2016)
Jian Zhang, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré
Parallel SGD: When does averaging help? CoRR (2016)
Ioannis Mitliagkas, Michael Borokhovich, Alexandros G. Dimakis, Constantine Caramanis
FrogWild! - Fast PageRank Approximations on Graph Engines. CoRR (2015)
Ioannis Mitliagkas, Michael Borokhovich, Alexandros G. Dimakis, Constantine Caramanis
FrogWild! - Fast PageRank Approximations on Graph Engines. Proc. VLDB Endow. 8 (8) (2015)
Dimitris S. Papailiopoulos, Ioannis Mitliagkas, Alexandros G. Dimakis, Constantine Caramanis
Finding Dense Subgraphs via Low-Rank Bilinear Optimization. ICML (2014)
Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain
Memory Limited, Streaming PCA. CoRR (2013)
Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain
Memory Limited, Streaming PCA. NIPS (2013)
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) (2011)
Ioannis Mitliagkas, Aditya Gopalan, Constantine Caramanis, Sriram Vishwanath
User rankings from comparisons: Learning permutations in high dimensions. Allerton (2011)
Ioannis Mitliagkas, Nicholas D. Sidiropoulos, Ananthram Swami
Distributed joint power and admission control for ad-hoc and cognitive underlay networks. ICASSP (2010)