Ioannis Mitliagkas

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All published works

Action	Title	Year	Authors
+ PDF Chat	Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones	2024	Mehrnaz Mofakhami Reza Bayat Ioannis Mitliagkas João M. Monteiro Valentina Zantedeschi
+ PDF Chat	Solving Hidden Monotone Variational Inequalities with Surrogate Losses	2024	Ryan D'Orazio Danilo Vucetic Zichu Liu Junhyung Lyle Kim Ioannis Mitliagkas Gauthier Gidel
+ PDF Chat	Understanding Adam Requires Better Rotation Dependent Assumptions	2024	Lucas Maes Tianyue H. Zhang Alexia Jolicoeur‐Martineau Ioannis Mitliagkas Damien Scieur Simon Lacoste-Julien Charles Guille-Escuret
+ PDF Chat	Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching	2024	Ange-Clément Akazan Alexia Jolicoeur‐Martineau Ioannis Mitliagkas
+ PDF Chat	Compositional Risk Minimization	2024	Divyat Mahajan Mohammad Zakaria Pezeshki Ioannis Mitliagkas Kartik Ahuja P. Vincent
+ PDF Chat	Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition	2024	Eleni Triantafillou Peter Kairouz Fabian Pedregosa Jamie Hayes Meghdad Kurmanji Kairan Zhao Vincent Dumoulin Julio Jacques Junior Ioannis Mitliagkas Jun Wan
+ PDF Chat	Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks	2024	Daniel Beaglehole Ioannis Mitliagkas Atish Agarwala
+	Performative Prediction with Neural Networks	2023	Mehrnaz Mofakhami Ioannis Mitliagkas Gauthier Gidel
+	No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths	2023	Charles Guille-Escuret Hiroki Naganuma Kilian Fatras Ioannis Mitliagkas
+	Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation	2023	Sébastien Lachapelle Divyat Mahajan Ioannis Mitliagkas Simon Lacoste-Julien
+	Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection	2023	Charles Guille-Escuret Pierre‐André Noël Ioannis Mitliagkas David Vázquez João Monteiro
+	Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound	2022	Charles Guille-Escuret Baptiste Goujaud Adam Ibrahim Ioannis Mitliagkas
+	Towards efficient representation identification in supervised learning	2022	Kartik Ahuja Divyat Mahajan Vasilis Syrgkanis Ioannis Mitliagkas
+	A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games	2022	Samuel Sokota Ryan D'Orazio J. Zico Kolter Nicolas Loizou Marc Lanctot Ioannis Mitliagkas Noam Brown Christian Kroer
+	Neural Networks Efficiently Learn Low-Dimensional Representations with SGD	2022	Alireza Mousavi-Hosseini Sejun Park Manuela Girotti Ioannis Mitliagkas Murat A. Erdogdu
+	A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods	2022	Tiago Salvador Kilian Fatras Ioannis Mitliagkas Adam M. Oberman
+	CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning	2022	Charles Guille-Escuret Pau Rodríguez David Vázquez Ioannis Mitliagkas João Monteiro
+	Towards Out-of-Distribution Adversarial Robustness	2022	Adam Ibrahim Charles Guille-Escuret Ioannis Mitliagkas Irina Rish David Krueger Pouya Bashivan
+	Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation	2022	Divyat Mahajan Ioannis Mitliagkas Brady Neal Vasilis Syrgkanis
+	Empirical Study on Optimizer Selection for Out-of-Distribution Generalization	2022	Hiroki Naganuma Kartik Ahuja Ioannis Mitliagkas Shiro Takagi Tetsuya Motokawa Rio Yokota Kohta Ishikawa Ikuro Sato
+	Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning	2022	Sébastien Lachapelle Tristan Deleu Divyat Mahajan Ioannis Mitliagkas Yoshua Bengio Simon Lacoste-Julien Quentin Bertrand
+	Optimal transport meets noisy label robust loss and MixUp regularization for domain adaptation	2022	Kilian Fatras Hiroki Naganuma Ioannis Mitliagkas
+	Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity	2021	Nicolas Loizou Hugo Berard Gauthier Gidel Ioannis Mitliagkas Simon Lacoste-Julien
+	Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization	2021	Kartik Ahuja Ethan Caballero Dinghuai Zhang Jean-Christophe Gagnon-Audet Yoshua Bengio Ioannis Mitliagkas Irina Rish
+	Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize	2021	Ryan D'Orazio Nicolas Loizou Issam Laradji Ioannis Mitliagkas
+	Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks.	2021	Manuela Girotti Ioannis Mitliagkas Gauthier Gidel
+	Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity	2021	Nicolas Loizou Hugo Berard Gauthier Gidel Ioannis Mitliagkas Simon Lacoste-Julien
+	Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization	2021	Kartik Ahuja Ethan Caballero Dinghuai Zhang Yoshua Bengio Ioannis Mitliagkas Irina Rish
+	Gotta Go Fast When Generating Data with Score-Based Models.	2021	Alexia Jolicoeur‐Martineau Ke Li Remi Piché-Taillefer Tal Kachman Ioannis Mitliagkas
+	Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize	2021	Ryan D'Orazio Nicolas Loizou Issam Laradji Ioannis Mitliagkas
+	Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks	2021	Manuela Girotti Ioannis Mitliagkas Gauthier Gidel
+	Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity	2021	Nicolas Loizou Hugo Berard Gauthier Gidel Ioannis Mitliagkas Simon Lacoste-Julien
+	Gotta Go Fast When Generating Data with Score-Based Models	2021	Alexia Jolicoeur‐Martineau Ke Li Remi Piché-Taillefer Tal Kachman Ioannis Mitliagkas
+	Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization	2021	Kartik Ahuja Ethan Caballero Dinghuai Zhang Jean-Christophe Gagnon-Audet Yoshua Bengio Ioannis Mitliagkas Irina Rish
+	A Study of Condition Numbers for First-Order Optimization	2020	Charles Guille-Escuret Baptiste Goujaud Manuela Girotti Ioannis Mitliagkas
+	LEAD: Least-Action Dynamics for Min-Max Optimization	2020	Reyhane Askari Hemmat Amartya Mitra Guillaume Lajoie Ioannis Mitliagkas
+	In Search of Robust Measures of Generalization.	2020	Gintare Karolina Dziugaite Alexandre Drouin Brady Neal Nitarshan Rajkumar Ethan Caballero Linbo Wang Ioannis Mitliagkas Daniel M. Roy
+	Adversarial score matching and improved sampling for image generation	2020	Alexia Jolicoeur‐Martineau Remi Piché-Taillefer Rémi Tachet des Combes Ioannis Mitliagkas
+	Stochastic Hamiltonian Gradient Methods for Smooth Games	2020	Nicolas Loizou Hugo Berard Alexia Jolicoeur‐Martineau Pascal Vincent Simon Lacoste-Julien Ioannis Mitliagkas
+	Accelerating Smooth Games by Manipulating Spectral Shapes.	2020	Waïss Azizian Damien Scieur Ioannis Mitliagkas Simon Lacoste-Julien Gauthier Gidel
+	A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games.	2020	Waïss Azizian Ioannis Mitliagkas Simon Lacoste-Julien Gauthier Gidel
+	Accelerating Smooth Games by Manipulating Spectral Shapes	2020	Waïss Azizian Damien Scieur Ioannis Mitliagkas Simon Lacoste-Julien Gauthier Gidel
+	Stochastic Hamiltonian Gradient Methods for Smooth Games	2020	Nicolas Loizou Hugo Berard Alexia Jolicoeur‐Martineau Pascal Vincent Simon Lacoste-Julien Ioannis Mitliagkas
+	In Search of Robust Measures of Generalization	2020	Gintare Karolina Dziugaite Alexandre Drouin Brady Neal Nitarshan Rajkumar Ethan Caballero Linbo Wang Ioannis Mitliagkas Daniel M. Roy
+	Adversarial score matching and improved sampling for image generation	2020	Alexia Jolicoeur‐Martineau Remi Piché-Taillefer Rémi Tachet des Combes Ioannis Mitliagkas
+	LEAD: Min-Max Optimization from a Physical Perspective	2020	Reyhane Askari Hemmat Amartya Mitra Guillaume Lajoie Ioannis Mitliagkas
+	A Study of Condition Numbers for First-Order Optimization	2020	Charles Guille-Escuret Baptiste Goujaud Manuela Girotti Ioannis Mitliagkas
+	Adversarial target-invariant representation learning for domain generalization	2019	Isabela Albuquerque João Monteiro Mohammad Javad Darvishi Bayazi Tiago H. Falk Ioannis Mitliagkas
+	Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs	2019	Alexia Jolicoeur‐Martineau Ioannis Mitliagkas
+	Lower Bounds and Conditioning of Differentiable Games.	2019	Adam Ibrahim Waïss Azizian Gauthier Gidel Ioannis Mitliagkas
+	A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games.	2019	Waïss Azizian Ioannis Mitliagkas Simon Lacoste-Julien Gauthier Gidel
+	A Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Games	2019	Waïss Azizian Ioannis Mitliagkas Simon Lacoste-Julien Gauthier Gidel
+	Multi-objective training of Generative Adversarial Networks with multiple discriminators	2019	Isabela Albuquerque João Monteiro Thang Doan Breandan Considine Tiago H. Falk Ioannis Mitliagkas
+	MLSys: The New Frontier of Machine Learning Systems	2019	Alexander Ratner Dan Alistarh Gustavo Alonso David G. Andersen Peter Bailis Sarah Bird Nicholas Carlini Bryan Catanzaro Jennifer Chayes Eric Chung
+	State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations	2019	Alex Lamb Jonathan Binas Anirudh Goyal Sandeep Subramanian Ioannis Mitliagkas Denis Kazakov Yoshua Bengio Michael C. Mozer
+	Reducing the variance in online optimization by transporting past gradients	2019	Sébastien M. R. Arnold Pierre-Antoine Manzagol Reza Babanezhad Ioannis Mitliagkas Nicolas Le Roux
+	Linear Lower Bounds and Conditioning of Differentiable Games	2019	Adam Ibrahim Waïss Azizian Gauthier Gidel Ioannis Mitliagkas
+	Generalizing to unseen domains via distribution matching	2019	Isabela Albuquerque João Monteiro Mohammad Javad Darvishi Bayazi Tiago H. Falk Ioannis Mitliagkas
+	A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Games	2019	Waïss Azizian Ioannis Mitliagkas Simon Lacoste-Julien Gauthier Gidel
+	Gradient penalty from a maximum margin perspective	2019	Alexia Jolicoeur‐Martineau Ioannis Mitliagkas
+	Multi-objective training of Generative Adversarial Networks with multiple discriminators	2019	Isabela Albuquerque João Monteiro Thang Doan Breandan Considine Tiago H. Falk Ioannis Mitliagkas
+	A Modern Take on the Bias-Variance Tradeoff in Neural Networks	2018	Brady Neal Sarthak Mittal Aristide Baratin Vinayak Tantia Matthew Scicluna Simon Lacoste-Julien Ioannis Mitliagkas
+	h-detach: Modifying the LSTM Gradient Towards Better Optimization	2018	Devansh Arpit Bhargav Kanuparthi Giancarlo Kerg Nan Rosemary Ke Ioannis Mitliagkas Yoshua Bengio
+	Negative Momentum for Improved Game Dynamics	2018	Gauthier Gidel Reyhane Askari Hemmat Mohammad Pezeshki Remi Lepriol Gabriel Huang Simon Lacoste-Julien Ioannis Mitliagkas
+	Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer.	2018	Vikas Verma Alex Lamb Christopher Beckham Aaron Courville Ioannis Mitliagkas Yoshua Bengio
+	Manifold Mixup: Better Representations by Interpolating Hidden States.	2018	Vikas Verma Alex Lamb Christopher Beckham Amir Najafi Ioannis Mitliagkas Aaron Courville David López-Paz Yoshua Bengio
+	Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations	2018	Alex Lamb Jonathan Binas Anirudh Goyal Dmitriy Serdyuk Sandeep Subramanian Ioannis Mitliagkas Yoshua Bengio
+	h-detach: Modifying the LSTM Gradient Towards Better Optimization	2018	Devansh Arpit Bhargav Kanuparthi Giancarlo Kerg Nan Rosemary Ke Ioannis Mitliagkas Yoshua Bengio
+	Negative Momentum for Improved Game Dynamics	2018	Gauthier Gidel Reyhane Askari Hemmat Mohammad Zakaria Pezeshki Remi Lepriol Gabriel Huang Simon Lacoste-Julien Ioannis Mitliagkas
+	Manifold Mixup: Better Representations by Interpolating Hidden States	2018	Vikas Verma Alex Lamb Christopher Beckham Amir Najafi Ioannis Mitliagkas Aaron Courville David López-Paz Yoshua Bengio
+	A Modern Take on the Bias-Variance Tradeoff in Neural Networks	2018	Brady Neal Sarthak Mittal Aristide Baratin Vinayak Tantia Matthew Scicluna Simon Lacoste-Julien Ioannis Mitliagkas
+	Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data	2017	Thorsten Kurth Jian Zhang Satish Nadathur Ioannis Mitliagkas Evan Racah Mostofa Patwary Tareq B. Malas Narayanan Sundaram W. Bhimji Mikhail E. Smorkalov
+	Improving Gibbs Sampler Scan Quality with DoGS	2017	Ioannis Mitliagkas Lester Mackey
+	Accelerated Stochastic Power Iteration	2017	Christopher De Bryan He Ioannis Mitliagkas Christopher Ré Peng Xu
+	Representation Learning and Adversarial Generation of 3D Point Clouds	2017	Panos Achlioptas Olga Diamanti Ioannis Mitliagkas Leonidas Guibas
+	YellowFin and the Art of Momentum Tuning	2017	Jian Zhang Ioannis Mitliagkas
+	Improving Gibbs Sampler Scan Quality with DoGS	2017	Ioannis Mitliagkas Lester Mackey
+	Learning Representations and Generative Models for 3D Point Clouds	2017	Panos Achlioptas Olga Diamanti Ioannis Mitliagkas Leonidas Guibas
+	Accelerated Stochastic Power Iteration	2017	Christopher De Bryan He Ioannis Mitliagkas Christopher Ré Peng Xu
+	Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data	2017	Thorsten Kurth Jian Zhang Nadathur Satish Ioannis Mitliagkas Evan Racah Mostofa Patwary Tareq B. Malas Narayanan Sundaram W. Bhimji Mikhail E. Smorkalov
+ PDF Chat	Asynchrony begets momentum, with an application to deep learning	2016	Ioannis Mitliagkas Ce Zhang Stefan Hadjis Christopher Ré
+	Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much	2016	Bryan He Christopher De Ioannis Mitliagkas Christopher Ré
+	Asynchrony begets Momentum, with an Application to Deep Learning	2016	Ioannis Mitliagkas Ce Zhang Stefan Hadjis Christopher Ré
+	Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs	2016	Stefan Hadjis Ce Zhang Ioannis Mitliagkas Christopher Ré
+	Parallel SGD: When does averaging help?	2016	Jian Zhang Christopher De Ioannis Mitliagkas Christopher Ré
+	Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much	2016	Bryan He Christopher De Ioannis Mitliagkas Christopher Ré
+	Asynchrony begets Momentum, with an Application to Deep Learning	2016	Ioannis Mitliagkas Ce Zhang Stefan Hadjis Christopher Ré
+	FrogWild! -- Fast PageRank Approximations on Graph Engines	2015	Ioannis Mitliagkas Michael Borokhovich Alexandros G. Dimakis Constantine Caramanis
+	Memory Limited, Streaming PCA	2013	Ioannis Mitliagkas Constantine Caramanis Prateek Jain
+	Memory Limited, Streaming PCA	2013	Ioannis Mitliagkas Constantine Caramanis Prateek Jain
+	Streaming, Memory-limited PCA	2013	Ioannis Mitliagkas Constantine Caramanis Prateek Jain
+	Memory Limited, Streaming PCA	2013	Ioannis Mitliagkas Constantine Caramanis Prateek Jain
+	Sum Capacity of Gaussian Interfering Multiple Access Channels in the Low Interference Regime	2011	Jubin Jose Ioannis Mitliagkas Sriram Vishwanath
+	Sum Capacity of Gaussian Interfering Multiple Access Channels in the Low Interference Regime	2011	Jubin Jose Ioannis Mitliagkas Sriram Vishwanath

Common Coauthors

Coauthor	Papers Together
Simon Lacoste-Julien	18
Gauthier Gidel	17
Yoshua Bengio	11
Alexia Jolicoeur‐Martineau	10
Christopher Ré	9
Nicolas Loizou	8
Charles Guille-Escuret	8
Waïss Azizian	8
Christopher De	6
Kartik Ahuja	6
Manuela Girotti	5
Constantine Caramanis	5
Hugo Berard	5
Ethan Caballero	5
Brady Neal	5
Alex Lamb	5
Divyat Mahajan	5
Reyhane Askari Hemmat	4
Stefan Hadjis	4
Bryan He	4
Remi Piché-Taillefer	4
Isabela Albuquerque	4
Tiago H. Falk	4
João Monteiro	4
Ryan D'Orazio	4
Irina Rish	4
Aaron Courville	3
Hiroki Naganuma	3
Gintare Karolina Dziugaite	3
Jian Zhang	3
Pradeep Dubey	3
Ce Zhang	3
Prateek Jain	3
Damien Scieur	3
Christopher Beckham	3
Dinghuai Zhang	3
Baptiste Goujaud	3
Kilian Fatras	3
Anirudh Goyal	2
Thorsten Kurth	2
Remi Lepriol	2
Alexandros G. Dimakis	2
Sandeep Subramanian	2
Guillaume Lajoie	2
Jack Deslippe	2
Breandan Considine	2
Olga Diamanti	2
Panos Achlioptas	2
Giancarlo Kerg	2
Adam Ibrahim	2

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Some methods of speeding up the convergence of iteration methods	1964	B. T. Polyak	10
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	9
+	Improved Techniques for Training GANs	2016	Tim Salimans Ian Goodfellow Wojciech Zaremba Vicki Cheung Alec Radford Xi Chen	7
+	Introductory Lectures on Convex Optimization: A Basic Course	2014	Ю Е Нестеров	7
+	GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium	2017	Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler Sepp Hochreiter	6
+	A Variational Inequality Perspective on Generative Adversarial Networks	2018	Gauthier Gidel Hugo Berard Gaëtan Vignoud Pascal Vincent Simon Lacoste-Julien	6
+	Spectral Normalization for Generative Adversarial Networks	2018	Takeru Miyato Toshiki Kataoka Masanori Koyama Yuichi Yoshida	5
+	Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks	2018	Tengyuan Liang James Stokes	4
+	Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks	2015	Alec Radford Luke Metz Soumith Chintala	4
+	Adam: A Method for Stochastic Optimization	2014	Diederik P. Kingma Jimmy Ba	4
+ PDF Chat	Identity Mappings in Deep Residual Networks	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	4
+	Connecting Generative Adversarial Networks and Actor-Critic Methods	2016	David Pfau Oriol Vinyals	4
+	HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent	2011	Feng Niu Benjamin Recht Christopher Ré Stephen J. Wright	4
+	The Mechanics of n-Player Differentiable Games	2018	David Balduzzi Sébastien Racanière James Martens Jakob Foerster Karl Tuyls Thore Graepel	4
+	Towards Deep Learning Models Resistant to Adversarial Attacks.	2018	Aleksander Mądry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu	4
+	On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games	2019	Eric Mazumdar Michael I. Jordan S. Shankar Sastry	4
+	Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs	2016	Stefan Hadjis Ce Zhang Ioannis Mitliagkas Christopher Ré	4
+	The extragradient method for finding saddle points and other problems	1976	G. M. Korpelevich	4
+	Asynchrony begets Momentum, with an Application to Deep Learning	2016	Ioannis Mitliagkas Ce Zhang Stefan Hadjis Christopher Ré	3
+	A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games.	2020	Waïss Azizian Ioannis Mitliagkas Simon Lacoste-Julien Gauthier Gidel	3
+	Revisiting Distributed Synchronous SGD	2017	Jianmin Chen Rajat Monga Samy Bengio Rafał Józefowicz	3
+	On lower and upper bounds in smooth and strongly convex optimization	2016	Yossi Arjevani Shai Shalev‐Shwartz Ohad Shamir	3
+	Adversarial target-invariant representation learning for domain generalization	2019	Isabela Albuquerque João Monteiro Mohammad Javad Darvishi Bayazi Tiago H. Falk Ioannis Mitliagkas	3
+ PDF Chat	Least Squares Generative Adversarial Networks	2017	Xudong Mao Qing Li Haoran Xie Raymond Y.K. Lau Zhen Wang Stephen Paul Smolley	3
+	A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach.	2020	Aryan Mokhtari Asuman Ozdaglar Sarath Pattathil	3
+ PDF Chat	Linear convergence of first order methods for non-strongly convex optimization	2018	Ion Necoara Yu. Nesterov François Glineur	3
+ PDF Chat	Rethinking the Inception Architecture for Computer Vision	2016	Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jon Shlens Zbigniew Wojna	3
+	Stochastic Variance Reduction Methods for Saddle-Point Problems	2016	Palaniappan Balamurugan Francis Bach	3
+	Invariant Risk Minimization	2019	Martín Arjovsky Léon Bottou Ishaan Gulrajani David López-Paz	3
+ PDF Chat	Prox-Method with Rate of Convergence <i>O</i>(1/<i>t</i>) for Variational Inequalities with Lipschitz Continuous Monotone Operators and Smooth Convex-Concave Saddle Point Problems	2004	Arkadi Nemirovski	3
+ PDF Chat	A Style-Based Generator Architecture for Generative Adversarial Networks	2019	Tero Karras Samuli Laine Timo Aila	3
+	Last-iterate convergence rates for min-max optimization	2019	Jacob Abernethy Kevin A. Lai Andre Wibisono	3
+	On linear convergence of iterative methods for the variational inequality problem	1995	Paul Tseng	3
+	Large Scale GAN Training for High Fidelity Natural Image Synthesis	2018	Andrew Brock Jeff Donahue Karen Simonyan	3
+	Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods	2017	Nicolas Loizou Peter Richtárik	3
+	Nonlinear Programming	1995	Dimitri P. Bertsekas	3
+	Robust Stochastic Approximation Approach to Stochastic Programming	2009	Arkadi Nemirovski Anatoli Juditsky Guanghui Lan Alexander Shapiro	3
+ PDF Chat	SGD: General Analysis and Improved Rates	2019	Robert M. Gower Nicolas Loizou Xun Qian Alibek Sailanbayev Egor Shulgin Peter Richtárik	3
+	The relativistic discriminator: a key element missing from standard GAN	2018	Alexia Jolicoeur‐Martineau	3
+	TensorFlow: A system for large-scale machine learning	2016	Martı́n Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy Davis Jay B. Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving Michael Isard	3
+ PDF Chat	Minimizing finite sums with the stochastic average gradient	2016	Mark Schmidt Nicolas Le Roux Francis Bach	3
+ PDF Chat	Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition	2016	Hamed Karimi Julie Nutini Mark Schmidt	3
+	Unrolled Generative Adversarial Networks	2016	Luke Metz Ben Poole David Pfau Jascha Sohl‐Dickstein	2
+ PDF Chat	Relatively Smooth Convex Optimization by First-Order Methods, and Applications	2018	Haihao Lu Robert M. Freund Yurii Nesterov	2
+ PDF Chat	Stochastic optimization for PCA and PLS	2012	Raman Arora Andrew Cotter Karen Livescu Nathan Srebro	2
+ PDF Chat	Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation	2017	Benjamin Scellier Yoshua Bengio	2
+	Train faster, generalize better: Stability of stochastic gradient descent	2015	Moritz Hardt Benjamin Recht Yoram Singer	2
+	NIPS 2016 Tutorial: Generative Adversarial Networks	2017	Ian Goodfellow	2
+	Regularized Nonlinear Acceleration	2016	Damien Scieur Alexandre d’Aspremont Francis Bach	2
+	Dobrushin Conditions and Systematic Scan	2008	Martin Dyer Leslie Ann Goldberg Mark Jerrum	2