Mitliagkas, Ioannis

61 publications

TMLR 2025 An Empirical Study of Pre-Trained Model Selection for Out-of-Distribution Generalization and Calibration Hiroki Naganuma, Ryuichiro Hataya, Kotaro Yoshida, Ioannis Mitliagkas

ICML 2025 Compositional Risk Minimization Divyat Mahajan, Mohammad Pezeshki, Charles Arnal, Ioannis Mitliagkas, Kartik Ahuja, Pascal Vincent

ICLRW 2025 Learning to Defer for Causal Discovery with Imperfect Experts Oscar Clivio, Divyat Mahajan, Perouz Taslakian, Sara Magliacane, Ioannis Mitliagkas, Valentina Zantedeschi, Alexandre Drouin

TMLR 2025 Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training Hiroki Naganuma, Xinzhi Zhang, Man-Chung Yue, Ioannis Mitliagkas, Russell J. Hewett, Philipp Andre Witte, Yin Tat Lee

ICLR 2025 Solving Hidden Monotone Variational Inequalities with Surrogate Losses Ryan D'Orazio, Danilo Vucetic, Zichu Liu, Junhyung Lyle Kim, Ioannis Mitliagkas, Gauthier Gidel

NeurIPS 2025 Understanding Adam Requires Better Rotation Dependent Assumptions Tianyue H. Zhang, Lucas Maes, Alan Milligan, Alexia Jolicoeur-Martineau, Ioannis Mitliagkas, Damien Scieur, Simon Lacoste-Julien, Charles Guille-Escuret

NeurIPSW 2024 Compositional Risk Minimization Divyat Mahajan, Mohammad Pezeshki, Ioannis Mitliagkas, Kartik Ahuja, Pascal Vincent

ICLR 2024 Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis

NeurIPS 2024 Expecting the Unexpected: Towards Broad Out-of-Distribution Detection Charles Guille-Escuret, Pierre-André Noël, Ioannis Mitliagkas, David Vazquez, Joao Monteiro

TMLR 2024 Feature Learning as Alignment: A Structural Property of Gradient Descent in Non-Linear Neural Networks Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala

ICMLW 2024 Gradient Descent Induces Alignment Between Weights and the Pre-Activation Tangents for Deep Non-Linear Networks Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala

ICML 2024 No Wrong Turns: The Simple Geometry of Neural Networks Optimization Paths Charles Guille-Escuret, Hiroki Naganuma, Kilian Fatras, Ioannis Mitliagkas

ICMLW 2024 Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones Mehrnaz Mofakhami, Reza Bayat, Ioannis Mitliagkas, Joao Monteiro, Valentina Zantedeschi

NeurIPSW 2024 Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training Hiroki Naganuma, Xinzhi Zhang, Man-Chung Yue, Ioannis Mitliagkas, Russell J. Hewett, Philipp Andre Witte, Yin Tat Lee

ICLRW 2024 Smoothness-Adaptive Sharpness-Aware Minimization for Finding Flatter Minima Hiroki Naganuma, Junhyung Lyle Kim, Anastasios Kyrillidis, Ioannis Mitliagkas

NeurIPSW 2024 Solving Hidden Monotone Variational Inequalities with Surrogate Losses Ryan D'Orazio, Danilo Vucetic, Zichu Liu, Junhyung Lyle Kim, Ioannis Mitliagkas, Gauthier Gidel

NeurIPSW 2024 Understanding Adam Requires Better Rotation Dependent Assumptions Tianyue H. Zhang, Lucas Maes, Alexia Jolicoeur-Martineau, Ioannis Mitliagkas, Damien Scieur, Simon Lacoste-Julien, Charles Guille-Escuret

TMLR 2023 A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam M Oberman

ICLR 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

NeurIPS 2023 Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, Simon Lacoste-Julien

NeurIPS 2023 CADet: Fully Self-Supervised Out-of-Distribution Detection with Contrastive Learning Charles Guille-Escuret, Pau Rodriguez, David Vazquez, Ioannis Mitliagkas, Joao Monteiro

TMLR 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

TMLR 2023 LEAD: Min-Max Optimization from a Physical Perspective Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas

ICMLW 2023 LEAD: Min-Max Optimization from a Physical Perspective Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas

ICLR 2023 Neural Networks Efficiently Learn Low-Dimensional Representations with SGD Alireza Mousavi-Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A Erdogdu

AISTATS 2023 Performative Prediction with Neural Networks Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel

TMLR 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

ICML 2023 Synergies Between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning Sebastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand

ICMLW 2023 Towards Out-of-Distribution Adversarial Robustness Adam Ibrahim, Charles Guille-Escuret, Ioannis Mitliagkas, Irina Rish, David Krueger, Pouya Bashivan

NeurIPSW 2022 A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam M Oberman

NeurIPSW 2022 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

NeurIPSW 2022 Empirical Study on Optimizer Selection for Out-of-Distribution Generalization Hiroki Naganuma, Kartik Ahuja, Ioannis Mitliagkas, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato

NeurIPS 2022 Gradient Descent Is Optimal Under Lower Restricted Secant Inequality and Upper Error Bound Charles Guille-Escuret, Adam Ibrahim, Baptiste Goujaud, Ioannis Mitliagkas

NeurIPSW 2022 Neural Networks Efficiently Learn Low-Dimensional Representations with SGD Alireza Mousavi-Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A Erdogdu

CoLLAs 2022 Optimal Transport Meets Noisy Label Robust Loss and MixUp Regularization for Domain Adaptation Kilian Fatras, Hiroki Naganuma, Ioannis Mitliagkas

NeurIPSW 2022 Performative Prediction with Neural Networks Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel

CLeaR 2022 Towards Efficient Representation Identification in Supervised Learning Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas

AISTATS 2021 A Study of Condition Numbers for First-Order Optimization Charles Guille-Escuret, Manuela Girotti, Baptiste Goujaud, Ioannis Mitliagkas

ICLR 2021 Adversarial Score Matching and Improved Sampling for Image Generation Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Ioannis Mitliagkas, Remi Tachet des Combes

NeurIPSW 2021 Gotta Go Fast with Score-Based Generative Models Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer, Tal Kachman, Ioannis Mitliagkas

NeurIPS 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

NeurIPS 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

AISTATS 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

AISTATS 2020 Accelerating Smooth Games by Manipulating Spectral Shapes Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel

NeurIPS 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

ICML 2020 Linear Lower Bounds and Conditioning of Differentiable Games Adam Ibrahim, Waı̈ss Azizian, Gauthier Gidel, Ioannis Mitliagkas

ICML 2020 Stochastic Hamiltonian Gradient Methods for Smooth Games Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas

ICMLW 2019 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

ICLR 2019 H-Detach: Modifying the LSTM Gradient Towards Better Optimization Bhargav Kanuparthi, Devansh Arpit, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas, Yoshua Bengio

ICMLW 2019 In Support of Over-Parametrization in Deep Reinforcement Learning: An Empirical Study Brady Neal, Ioannis Mitliagkas

ICML 2019 Manifold Mixup: Better Representations by Interpolating Hidden States Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio

ICML 2019 Multi-Objective Training of Generative Adversarial Networks with Multiple Discriminators Isabela Albuquerque, Joao Monteiro, Thang Doan, Breandan Considine, Tiago Falk, Ioannis Mitliagkas

AISTATS 2019 Negative Momentum for Improved Game Dynamics Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Rémi Le Priol, Gabriel Huang, Simon Lacoste-Julien, Ioannis Mitliagkas

NeurIPS 2019 Reducing the Variance in Online Optimization by Transporting past Gradients Sébastien Arnold, Pierre-Antoine Manzagol, Reza Babanezhad Harikandeh, Ioannis Mitliagkas, Nicolas Le Roux

ICML 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

AISTATS 2018 Accelerated Stochastic Power Iteration Peng Xu, Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré

ICML 2018 Learning Representations and Generative Models for 3D Point Clouds Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas Guibas

ICML 2017 Improving Gibbs Sampler Scan Quality with DoGS Ioannis Mitliagkas, Lester Mackey

NeurIPS 2016 Scan Order in Gibbs Sampling: Models in Which It Matters and Bounds on How Much Bryan D He, Christopher M De Sa, Ioannis Mitliagkas, Christopher Ré

ICML 2014 Finding Dense Subgraphs via Low-Rank Bilinear Optimization Dimitris Papailiopoulos, Ioannis Mitliagkas, Alexandros Dimakis, Constantine Caramanis

NeurIPS 2013 Memory Limited, Streaming PCA Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain