Ioannis Mitliagkas

According to our database, Ioannis Mitliagkas authored at least 70 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Understanding Adam Requires Better Rotation Dependent Assumptions.

Lucas Maes, Tianyue H. Zhang, Alexia Jolicoeur-Martineau, Ioannis Mitliagkas, Damien Scieur, Simon Lacoste-Julien, Charles Guille-Escuret

CoRR, 2024

Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching.

Ange-Clément Akazan, Alexia Jolicoeur-Martineau, Ioannis Mitliagkas

CoRR, 2024

Compositional Risk Minimization.

Divyat Mahajan, Mohammad Pezeshki, Ioannis Mitliagkas, Kartik Ahuja, Pascal Vincent

CoRR, 2024

Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition.

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

CoRR, 2024

Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks.

Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala

CoRR, 2024

No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths.

Charles Guille-Escuret, Hiroki Naganuma, Kilian Fatras, Ioannis Mitliagkas

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation.

Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods.

Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam M. Oberman

Trans. Mach. Learn. Res., 2023

Empirical Study on Optimizer Selection for Out-of-Distribution Generalization.

Hiroki Naganuma, Kartik Ahuja, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato, Ioannis Mitliagkas

Trans. Mach. Learn. Res., 2023

LEAD: Min-Max Optimization from a Physical Perspective.

Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas

Trans. Mach. Learn. Res., 2023

Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize.

Ryan D'Orazio, Nicolas Loizou, Issam H. Laradji, Ioannis Mitliagkas

Trans. Mach. Learn. Res., 2023

Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection.

Charles Guille-Escuret, Pierre-André Noël, Ioannis Mitliagkas, David Vázquez, João Monteiro

CoRR, 2023

Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation.

Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, Simon Lacoste-Julien

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning.

Charles Guille-Escuret, Pau Rodríguez, David Vázquez, Ioannis Mitliagkas, João Monteiro

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning.

Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand

Proceedings of the International Conference on Machine Learning, 2023

A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games.

Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Neural Networks Efficiently Learn Low-Dimensional Representations with SGD.

Alireza Mousavi Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Performative Prediction with Neural Networks.

Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective.

Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand

CoRR, 2022

Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation.

Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis

CoRR, 2022

Towards Out-of-Distribution Adversarial Robustness.

Adam Ibrahim, Charles Guille-Escuret, Ioannis Mitliagkas, Irina Rish, David Krueger, Pouya Bashivan

CoRR, 2022

CADet: Fully Self-Supervised Anomaly Detection With Contrastive Learning.

Charles Guille-Escuret, Pau Rodríguez, David Vázquez, Ioannis Mitliagkas, João Monteiro

CoRR, 2022

Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound.

Charles Guille-Escuret, Adam Ibrahim, Baptiste Goujaud, Ioannis Mitliagkas

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Optimal Transport meets Noisy Label Robust Loss and MixUp Regularization for Domain Adaptation.

Kilian Fatras, Hiroki Naganuma, Ioannis Mitliagkas

Proceedings of the Conference on Lifelong Learning Agents, 2022

Towards efficient representation identification in supervised learning.

Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2021

Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks.

Manuela Girotti, Ioannis Mitliagkas, Gauthier Gidel

CoRR, 2021

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization.

Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish

CoRR, 2021

Gotta Go Fast When Generating Data with Score-Based Models.

Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer, Tal Kachman, Ioannis Mitliagkas

CoRR, 2021

Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity.

Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization.

Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Adversarial score matching and improved sampling for image generation.

Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Ioannis Mitliagkas, Remi Tachet des Combes

Proceedings of the 9th International Conference on Learning Representations, 2021

A Study of Condition Numbers for First-Order Optimization.

Charles Guille-Escuret, Manuela Girotti, Baptiste Goujaud, Ioannis Mitliagkas

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

LEAD: Least-Action Dynamics for Min-Max Optimization.

Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas

CoRR, 2020

In search of robust measures of generalization.

Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Stochastic Hamiltonian Gradient Methods for Smooth Games.

Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas

Proceedings of the 37th International Conference on Machine Learning, 2020

Linear Lower Bounds and Conditioning of Differentiable Games.

Adam Ibrahim, Waïss Azizian, Gauthier Gidel, Ioannis Mitliagkas

Proceedings of the 37th International Conference on Machine Learning, 2020

Accelerating Smooth Games by Manipulating Spectral Shapes.

Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games.

Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Adversarial target-invariant representation learning for domain generalization.

Isabela Albuquerque, João Monteiro, Tiago H. Falk, Ioannis Mitliagkas

CoRR, 2019

Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs.

Alexia Jolicoeur-Martineau, Ioannis Mitliagkas

CoRR, 2019

Lower Bounds and Conditioning of Differentiable Games.

Adam Ibrahim, Waïss Azizian, Gauthier Gidel, Ioannis Mitliagkas

CoRR, 2019

A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games.

Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel

CoRR, 2019

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations.

Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Denis Kazakov, Yoshua Bengio, Michael C. Mozer

CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.

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

CoRR, 2019

Reducing the variance in online optimization by transporting past gradients.

Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

YellowFin and the Art of Momentum Tuning.

Jian Zhang, Ioannis Mitliagkas

Proceedings of the Second Conference on Machine Learning and Systems, SysML 2019, 2019

Manifold Mixup: Better Representations by Interpolating Hidden States.

Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio

Proceedings of the 36th International Conference on Machine Learning, 2019

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations.

Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio, Michael Mozer

Proceedings of the 36th International Conference on Machine Learning, 2019

Multi-objective training of Generative Adversarial Networks with multiple discriminators.

Isabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine, Tiago H. Falk, Ioannis Mitliagkas

Proceedings of the 36th International Conference on Machine Learning, 2019

h-detach: Modifying the LSTM Gradient Towards Better Optimization.

Bhargav Kanuparthi, Devansh Arpit, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas, Yoshua Bengio

Proceedings of the 7th International Conference on Learning Representations, 2019

Negative Momentum for Improved Game Dynamics.

Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Rémi Le Priol, Gabriel Huang, Simon Lacoste-Julien, Ioannis Mitliagkas

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

A Modern Take on the Bias-Variance Tradeoff in Neural Networks.

Brady Neal, Sarthak Mittal, Aristide Baratin, Vinayak Tantia, Matthew Scicluna, Simon Lacoste-Julien, Ioannis Mitliagkas

CoRR, 2018

Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer.

Vikas Verma, Alex Lamb, Christopher Beckham, Aaron C. Courville, Ioannis Mitliagkas, Yoshua Bengio

CoRR, 2018

Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations.

Alex Lamb, Jonathan Binas, Anirudh Goyal, Dmitriy Serdyuk, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio

CoRR, 2018

Learning Representations and Generative Models for 3D Point Clouds.

Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas

Proceedings of the 35th International Conference on Machine Learning, 2018

Accelerated Stochastic Power Iteration.

Peng Xu, Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

YellowFin and the Art of Momentum Tuning.

Jian Zhang, Ioannis Mitliagkas, Christopher Ré

CoRR, 2017

Representation Learning and Adversarial Generation of 3D Point Clouds.

Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas

CoRR, 2017

Deep learning at 15PF: supervised and semi-supervised classification for scientific data.

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

Proceedings of the International Conference for High Performance Computing, 2017

Improving Gibbs Sampler Scan Quality with DoGS.

Ioannis Mitliagkas, Lester W. Mackey

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Parallel SGD: When does averaging help?

Jian Zhang, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré

CoRR, 2016

Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs.

Stefan Hadjis, Ce Zhang, Ioannis Mitliagkas, Christopher Ré

CoRR, 2016

Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much.

Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Asynchrony begets momentum, with an application to deep learning.

Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis, Christopher Ré

Proceedings of the 54th Annual Allerton Conference on Communication, 2016

2015

FrogWild! - Fast PageRank Approximations on Graph Engines.

Ioannis Mitliagkas, Michael Borokhovich, Alexandros G. Dimakis, Constantine Caramanis

Proc. VLDB Endow., 2015

2014

Finding Dense Subgraphs via Low-Rank Bilinear Optimization.

Dimitris S. Papailiopoulos, Ioannis Mitliagkas, Alexandros G. Dimakis, Constantine Caramanis

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Memory Limited, Streaming PCA.

Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2011

Joint Power and Admission Control for Ad-Hoc and Cognitive Underlay Networks: Convex Approximation and Distributed Implementation.

Ioannis Mitliagkas, Nicholas D. Sidiropoulos, Ananthram Swami

IEEE Trans. Wirel. Commun., 2011

User rankings from comparisons: Learning permutations in high dimensions.

Ioannis Mitliagkas, Aditya Gopalan, Constantine Caramanis, Sriram Vishwanath

Proceedings of the 49th Annual Allerton Conference on Communication, 2011

2010

Distributed joint power and admission control for ad-hoc and cognitive underlay networks.

Ioannis Mitliagkas, Nicholas D. Sidiropoulos, Ananthram Swami

Proceedings of the IEEE International Conference on Acoustics, 2010