Distributed joint power and admission control for ad-hoc and cognitive underlay networks. - Ioannis Mitliagkas; Nicholas D. Sidiropoulos; Ananthram Swami (ICASSP, 2010)

User rankings from comparisons: Learning permutations in high dimensions. - Ioannis Mitliagkas; Aditya Gopalan; Constantine Caramanis; Sriram Vishwanath (Allerton, 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)

Memory Limited, Streaming PCA. - Ioannis Mitliagkas; Constantine Caramanis; Prateek Jain 002 (NIPS, 2013)

Finding Dense Subgraphs via Low-Rank Bilinear Optimization. - Dimitris S. Papailiopoulos; Ioannis Mitliagkas; Alexandros G. Dimakis; Constantine Caramanis (ICML, 2014)

FrogWild! - Fast PageRank Approximations on Graph Engines. - Ioannis Mitliagkas; Michael Borokhovich; Alexandros G. Dimakis; Constantine Caramanis (Proc. VLDB Endow., 2015)

Asynchrony begets momentum, with an application to deep learning. - Ioannis Mitliagkas; Ce Zhang 001; Stefan Hadjis; Christopher Ré (Allerton, 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 (NIPS, 2016)

Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs. - Stefan Hadjis; Ce Zhang 001; Ioannis Mitliagkas; Christopher Ré (CoRR, 2016)

Parallel SGD: When does averaging help? - Jian Zhang 049; Christopher De Sa; Ioannis Mitliagkas; Christopher Ré (CoRR, 2016)

Improving Gibbs Sampler Scan Quality with DoGS. - Ioannis Mitliagkas; Lester W. Mackey (ICML, 2017)

Deep learning at 15PF: supervised and semi-supervised classification for scientific data. - Thorsten Kurth; Jian Zhang 049; Nadathur Satish; Evan Racah; Ioannis Mitliagkas; Md. Mostofa Ali Patwary; Tareq M. Malas; Narayanan Sundaram; Wahid Bhimji; Mikhail Smorkalov; Jack Deslippe; Mikhail Shiryaev; et al. (SC, 2017)

Representation Learning and Adversarial Generation of 3D Point Clouds. - Panos Achlioptas; Olga Diamanti; Ioannis Mitliagkas; Leonidas J. Guibas (CoRR, 2017)

YellowFin and the Art of Momentum Tuning. - Jian Zhang 049; Ioannis Mitliagkas; Christopher Ré (CoRR, 2017)

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

Learning Representations and Generative Models for 3D Point Clouds. - Panos Achlioptas; Olga Diamanti; Ioannis Mitliagkas; Leonidas J. Guibas (ICML, 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)

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)

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)

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

h-detach: Modifying the LSTM Gradient Towards Better Optimization. - Bhargav Kanuparthi; Devansh Arpit; Giancarlo Kerg; Nan Rosemary Ke; Ioannis Mitliagkas; Yoshua Bengio (ICLR (Poster), 2019)

Multi-objective training of Generative Adversarial Networks with multiple discriminators. - Isabela Albuquerque; João Monteiro 002; Thang Doan; Breandan Considine; Tiago H. Falk; Ioannis Mitliagkas (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 (ICML, 2019)

Manifold Mixup: Better Representations by Interpolating Hidden States. - Vikas Verma; Alex Lamb; Christopher Beckham; Amir Najafi 002; Ioannis Mitliagkas; David Lopez-Paz; Yoshua Bengio (ICML, 2019)

YellowFin and the Art of Momentum Tuning. - Jian Zhang 049; Ioannis Mitliagkas (SysML, 2019)

Reducing the variance in online optimization by transporting past gradients. - Sébastien M. R. Arnold; Pierre-Antoine Manzagol; Reza Babanezhad 001; Ioannis Mitliagkas; Nicolas Le Roux (NeurIPS, 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; et al. (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)

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)

Lower Bounds and Conditioning of Differentiable Games. - Adam Ibrahim; Waïss Azizian; Gauthier Gidel; Ioannis Mitliagkas (CoRR, 2019)

Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs. - Alexia Jolicoeur-Martineau; Ioannis Mitliagkas (CoRR, 2019)

Adversarial target-invariant representation learning for domain generalization. - Isabela Albuquerque; João Monteiro 002; Tiago H. Falk; Ioannis Mitliagkas (CoRR, 2019)

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 (AISTATS, 2020)

Linear Lower Bounds and Conditioning of Differentiable Games. - Adam Ibrahim; Waïss 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 (ICML, 2020)

In search of robust measures of generalization. - Gintare Karolina Dziugaite; Alexandre Drouin; Brady Neal; Nitarshan Rajkumar; Ethan Caballero; Linbo Wang 003; Ioannis Mitliagkas; Daniel M. Roy 001 (NeurIPS, 2020)

LEAD: Least-Action Dynamics for Min-Max Optimization. - Reyhane Askari Hemmat; Amartya Mitra; Guillaume Lajoie; Ioannis Mitliagkas (CoRR, 2020)

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

Adversarial score matching and improved sampling for image generation. - Alexia Jolicoeur-Martineau; Rémi Piché-Taillefer; Ioannis Mitliagkas; Remi Tachet des Combes (ICLR, 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 (NeurIPS, 2021)

Gotta Go Fast When Generating Data with Score-Based Models. - Alexia Jolicoeur-Martineau; Ke Li 011; Rémi Piché-Taillefer; Tal Kachman; Ioannis Mitliagkas (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)

Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks. - Manuela Girotti; Ioannis Mitliagkas; Gauthier Gidel (CoRR, 2021)

Towards efficient representation identification in supervised learning. - Kartik Ahuja; Divyat Mahajan; Vasilis Syrgkanis; Ioannis Mitliagkas (CLeaR, 2022)

Optimal Transport meets Noisy Label Robust Loss and MixUp Regularization for Domain Adaptation. - Kilian Fatras; Hiroki Naganuma; Ioannis Mitliagkas (CoLLAs, 2022)

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

CADet: Fully Self-Supervised Anomaly Detection With Contrastive Learning. - Charles Guille-Escuret; Pau Rodríguez; David Vázquez 001; Ioannis Mitliagkas; João Monteiro 002 (CoRR, 2022)

Towards Out-of-Distribution Adversarial Robustness. - Adam Ibrahim; Charles Guille-Escuret; Ioannis Mitliagkas; Irina Rish; David Krueger 001; Pouya Bashivan (CoRR, 2022)

Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation. - Divyat Mahajan; Ioannis Mitliagkas; Brady Neal; Vasilis Syrgkanis (CoRR, 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)

Performative Prediction with Neural Networks. - Mehrnaz Mofakhami; Ioannis Mitliagkas; Gauthier Gidel (AISTATS, 2023)

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

CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning. - Charles Guille-Escuret; Pau Rodríguez; David Vázquez 001; Ioannis Mitliagkas; João Monteiro 002 (NeurIPS, 2023)

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

LEAD: Min-Max Optimization from a Physical Perspective. - Reyhane Askari Hemmat; Amartya Mitra; Guillaume Lajoie; Ioannis Mitliagkas (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)

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 Analysis of Model Selection for Heterogeneous Causal Effect Estimation. - Divyat Mahajan; Ioannis Mitliagkas; Brady Neal; Vasilis Syrgkanis (ICLR, 2024)

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

Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection. - Charles Guille-Escuret; Pierre-André Noël; Ioannis Mitliagkas; David Vázquez 001; João Monteiro 002 (NeurIPS, 2024)

Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks. - Daniel Beaglehole; Ioannis Mitliagkas; Atish Agarwala (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 001; Lisheng Sun-Hosoya; Sergio Escalera; et al. (CoRR, 2024)

Compositional Risk Minimization. - Divyat Mahajan; Mohammad Pezeshki; Ioannis Mitliagkas; Kartik Ahuja; Pascal Vincent (CoRR, 2024)

Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching. - Ange-Clément Akazan; Alexia Jolicoeur-Martineau; Ioannis Mitliagkas (CoRR, 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)

Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones. - Mehrnaz Mofakhami; Reza Bayat; Ioannis Mitliagkas; João Monteiro 002; Valentina Zantedeschi (CoRR, 2024)

Feature learning as alignment: a structural property of gradient descent in non-linear neural networks. - Daniel Beaglehole; Ioannis Mitliagkas; Atish Agarwala (Trans. Mach. Learn. Res., 2024)

Solving hidden monotone variational inequalities with surrogate losses. - Ryan D'Orazio; Danilo Vucetic; Zichu Liu; Junhyung Lyle Kim; Ioannis Mitliagkas; Gauthier Gidel (ICLR, 2025)

An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration. - Hiroki Naganuma; Ryuichiro Hataya; Kotaro Yoshida; Ioannis Mitliagkas (Trans. Mach. Learn. Res., 2025)

Learning to Defer for Causal Discovery with Imperfect Experts. - Oscar Clivio; Divyat Mahajan; Perouz Taslakian; Sara Magliacane; Ioannis Mitliagkas; Valentina Zantedeschi; Alexandre Drouin (CoRR, 2025)

Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training. - Hiroki Naganuma; Xinzhi Zhang 002; Man-Chung Yue; Ioannis Mitliagkas; Philipp A. Witte; Russell J. Hewett; Yin Tat Lee (CoRR, 2025)