Timezone: »
Advances in generative modeling and adversarial learning gave rise to a recent surge of interest in differentiable two-players games, with much of the attention falling on generative adversarial networks (GANs). Solving these games introduces distinct challenges compared to the standard minimization tasks that the machine learning (ML) community is used to. A symptom of this issue is ML and deep learning (DL) practitioners using optimization tools on game-theoretic problems. Our NeurIPS 2018 workshop, "Smooth games optimization in ML", aimed to rectify this situation, addressing theoretical aspects of games in machine learning, their special dynamics, and typical challenges. For this year, we significantly expand our scope to tackle questions like the design of game formulations for other classes of ML problems, the integration of learning with game theory as well as their important applications. To that end, we have confirmed talks from Éva Tardos, David Balduzzi and Fei Fang. We will also solicit contributed posters and talks in the area.
Sat 8:15 a.m. - 8:30 a.m.
|
Opening remarks
(
Short presentation
)
|
🔗 |
Sat 8:30 a.m. - 9:10 a.m.
|
Invited talk: Eva Tardos (Cornell)
(
Invited talk
)
|
Eva Tardos 🔗 |
Sat 9:10 a.m. - 9:30 a.m.
|
Morning poster Spotlight
(
Spotlight
)
|
🔗 |
Sat 9:30 a.m. - 11:00 a.m.
|
Morning poster session -- coffee break
(
Poster session
)
|
🔗 |
Sat 11:00 a.m. - 11:40 a.m.
|
Invited talk: David Balduzzi (DeepMind
(
Invited talk
)
|
David Balduzzi 🔗 |
Sat 11:40 a.m. - 12:05 p.m.
|
Contributed talk: What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?
(
Contributed talk
)
Minimax optimization has found extensive applications in modern machine learning, in settings such as generative adversarial networks (GANs), adversarial training and multi-agent reinforcement learning. As most of these applications involve continuous nonconvex-nonconcave formulations, a very basic question arises---``what is a proper definition of local optima?'' Most previous work answers this question using classical notions of equilibria from simultaneous games, where the min-player and the max-player act simultaneously. In contrast, most applications in machine learning, including GANs and adversarial training, correspond to sequential games, where the order of which player acts first is crucial (since minimax is in general not equal to maximin due to the nonconvex-nonconcave nature of the problems). The main contribution of this paper is to propose a proper mathematical definition of local optimality for this sequential setting---local minimax, as well as to present its properties and existence results. Finally, we establish a strong connection to a basic local search algorithm---gradient descent ascent (GDA): under mild conditions, all stable limit points of GDA are exactly local minimax points up to some degenerate points. |
Praneeth Netrapalli 🔗 |
Sat 12:05 p.m. - 12:30 p.m.
|
Contributed talk: Characterizing Equilibria in Stackelberg Games
(
Contributed talk
)
This paper investigates the convergence of learning dynamics in Stackelberg games on continuous action spaces, a class of games distinguished by the hierarchical order of play between agents. We establish connections between the Nash and Stackelberg equilibrium concepts and characterize conditions under which attractors of simultaneous gradient descent are Stackelberg equilibria in zero-sum games. Moreover, we show that the only stable attractors of the Stackelberg gradient dynamics are Stackelberg equilibria in zero-sum games. Using this insight, we develop two-timescale learning dynamics that converge to Stackelberg equilibria in zero-sum games and the set of stable attractors in general-sum games. |
Tanner Fiez 🔗 |
Sat 12:30 p.m. - 2:00 p.m.
|
Lunch break
|
🔗 |
Sat 2:00 p.m. - 2:40 p.m.
|
Invited talk: Fei Fang (CMU)
(
Invited talk
)
|
Fei Fang 🔗 |
Sat 2:40 p.m. - 3:05 p.m.
|
Contributed talk: On Solving Local Minimax Optimization: A Follow-the-Ridge Approach
(
Contributed talk
)
Many tasks in modern machine learning can be formulated as finding equilibria in \emph{sequential} games. In particular, two-player zero-sum sequential games, also known as minimax optimization, have received growing interest. It is tempting to apply gradient descent to solve minimax optimization given its popularity in supervised learning. However, we note that naive application of gradient descent fails to find local minimax -- the analogy of local minima in minimax optimization, since the fixed points of gradient dynamics might not be local minimax. In this paper, we propose \emph{Follow-the-Ridge} (FR), an algorithm that locally converges to and only converges to local minimax. We show theoretically that the algorithm addresses the limit cycling problem around fixed points, and is compatible with preconditioning and \emph{positive} momentum. Empirically, FR solves quadratic minimax problems and improves GAN training on simple tasks. |
Yuanhao Wang 🔗 |
Sat 3:05 p.m. - 3:30 p.m.
|
Contributed talk: Exploiting Uncertain Real-Time Information from Deep Learning in Signaling Games for Security and Sustainability
(
Contributed talk
)
Motivated by real-world deployment of drones for conservation, this paper advances the state-of-the-art in security games with signaling. The well-known defender-attacker security games framework can help in planning for such strategic deployments of sensors and human patrollers, and warning signals to ward off adversaries. However, we show that defenders can suffer significant losses when ignoring real-world uncertainties, such as detection uncertainty resulting from imperfect deep learning models, despite carefully planned security game strategies with signaling. In fact, defenders may perform worse than forgoing drones completely in this case. We address this shortcoming by proposing a novel game model that integrates signaling and sensor uncertainty; perhaps surprisingly, we show that defenders can still perform well via a signaling strategy that exploits the uncertain real-time information primarily from deep learning models. For example, even in the presence of uncertainty, the defender still has an informational advantage in knowing that she has or has not actually detected the attacker; and she can design a signaling scheme to ``mislead'' the attacker who is uncertain as to whether he has been detected. We provide a novel algorithm, scale-up techniques, and experimental results from simulation based on our ongoing deployment of a conservation drone system in South Africa. |
Elizabeth Bondi-Kelly 🔗 |
Sat 3:30 p.m. - 4:00 p.m.
|
Coffee break
|
🔗 |
Sat 4:00 p.m. - 4:40 p.m.
|
Invited talk: Aryan Mokhtari (UT Austin)
(
Invited talk
)
|
Aryan Mokhtari 🔗 |
Sat 4:40 p.m. - 5:00 p.m.
|
Afternoon poster spotlight
(
Poster spotlight
)
|
🔗 |
Sat 5:00 p.m. - 5:30 p.m.
|
Discussion panel
(
Panel
)
|
🔗 |
Sat 5:30 p.m. - 6:30 p.m.
|
Concluding remarks -- afternoon poster session
(
Poster session
)
|
🔗 |
Author Information
Ioannis Mitliagkas (Mila & University of Montreal)
Gauthier Gidel (Mila)
I am a Ph.D student supervised by Simon Lacoste-Julien, I graduated from ENS Ulm and Université Paris-Saclay. I was a visiting PhD student at Sierra. I also worked for 6 months as a freelance Data Scientist for Monsieur Drive (Acquired by Criteo) and I recently co-founded a startup called Krypto. I'm currently pursuing my PhD at Mila. My work focuses on optimization applied to machine learning. More details can be found in my resume. My research is to develop new optimization algorithms and understand the role of optimization in the learning procedure, in short, learn faster and better. I identify to the field of machine learning (NIPS, ICML, AISTATS and ICLR) and optimization (SIAM OP)
Niao He (UIUC)
Reyhane Askari Hemmat (Mila & University of Montreal)
N H (CMU)
Nika Haghtalab (Cornell University)
Simon Lacoste-Julien (Mila, Université de Montréal & SAIL Montreal)
Simon Lacoste-Julien is an associate professor at Mila and DIRO from Université de Montréal, and Canada CIFAR AI Chair holder. He also heads part time the SAIT AI Lab Montreal from Samsung. His research interests are machine learning and applied math, with applications in related fields like computer vision and natural language processing. He obtained a B.Sc. in math., physics and computer science from McGill, a PhD in computer science from UC Berkeley and a post-doc from the University of Cambridge. He spent a few years as a research faculty at INRIA and École normale supérieure in Paris before coming back to his roots in Montreal in 2016 to answer the call from Yoshua Bengio in growing the Montreal AI ecosystem.
More from the Same Authors
-
2021 Spotlight: A single gradient step finds adversarial examples on random two-layers neural networks »
Sebastien Bubeck · Yeshwanth Cherapanamjeri · Gauthier Gidel · Remi Tachet des Combes -
2021 : On the convergence of stochastic extragradient for bilinear games using restarted iteration averaging »
Chris Junchi Li · Yaodong Yu · Nicolas Loizou · Gauthier Gidel · Yi Ma · Nicolas Le Roux perso · Michael Jordan -
2021 : On the convergence of stochastic extragradient for bilinear games using restarted iteration averaging »
Chris Junchi Li · Yaodong Yu · Nicolas Loizou · Gauthier Gidel · Yi Ma · Nicolas Le Roux perso · Michael Jordan -
2022 : Neural Networks Efficiently Learn Low-Dimensional Representations with SGD »
Alireza Mousavi-Hosseini · Sejun Park · Manuela Girotti · Ioannis Mitliagkas · Murat Erdogdu -
2022 : Nesterov Meets Optimism: Rate-Optimal Optimistic-Gradient-Based Method for Stochastic Bilinearly-Coupled Minimax Optimization »
Chris Junchi Li · Angela Yuan · Gauthier Gidel · Michael Jordan -
2022 : Momentum Extragradient is Optimal for Games with Cross-Shaped Spectrum »
Junhyung Lyle Kim · Gauthier Gidel · Anastasios Kyrillidis · Fabian Pedregosa -
2022 : Unlocking Slot Attention by Changing Optimal Transport Costs »
Yan Zhang · David Zhang · Simon Lacoste-Julien · Gertjan Burghouts · Cees Snoek -
2022 : Performative Prediction with Neural Networks »
Mehrnaz Mofakhami · Ioannis Mitliagkas · Gauthier Gidel -
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 -
2022 : A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods »
Tiago Salvador · Kilian FATRAS · Ioannis Mitliagkas · Adam Oberman -
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 -
2022 : Unlocking Slot Attention by Changing Optimal Transport Costs »
Yan Zhang · David Zhang · Simon Lacoste-Julien · Gertjan Burghouts · Cees Snoek -
2023 Poster: Feature Likelihood Score: Evaluating the Generalization of Generative Models Using Samples »
Marco Jiralerspong · Joey Bose · Ian Gemp · Chongli Qin · Yoram Bachrach · Gauthier Gidel -
2023 Poster: Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure »
Angela Yuan · Chris Junchi Li · Gauthier Gidel · Michael Jordan · Quanquan Gu · Simon Du -
2023 Poster: Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation »
Sébastien Lachapelle · Divyat Mahajan · Ioannis Mitliagkas · Simon Lacoste-Julien -
2023 Poster: CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning »
Charles Guille-Escuret · Pau Rodriguez · David Vazquez · Ioannis Mitliagkas · Joao Monteiro -
2023 Oral: Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation »
Sébastien Lachapelle · Divyat Mahajan · Ioannis Mitliagkas · Simon Lacoste-Julien -
2023 Competition: NeurIPS 2023 Machine Unlearning Competition »
Eleni Triantafillou · Fabian Pedregosa · Meghdad Kurmanji · Kairan ZHAO · Gintare Karolina Dziugaite · Peter Triantafillou · Ioannis Mitliagkas · Vincent Dumoulin · Lisheng Sun · Peter Kairouz · Julio C Jacques Junior · Jun Wan · Sergio Escalera · Isabelle Guyon -
2022 : Unlocking Slot Attention by Changing Optimal Transport Costs »
Yan Zhang · David Zhang · Simon Lacoste-Julien · Gertjan Burghouts · Cees Snoek -
2022 Poster: Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints »
Jose Gallego-Posada · Juan Ramirez · Akram Erraqabi · Yoshua Bengio · Simon Lacoste-Julien -
2022 Poster: Data-Efficient Structured Pruning via Submodular Optimization »
Marwa El Halabi · Suraj Srinivas · Simon Lacoste-Julien -
2022 Poster: Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound »
Charles Guille-Escuret · Adam Ibrahim · Baptiste Goujaud · Ioannis Mitliagkas -
2022 Poster: Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise »
Eduard Gorbunov · Marina Danilova · David Dobre · Pavel Dvurechenskii · Alexander Gasnikov · Gauthier Gidel -
2022 Poster: The Curse of Unrolling: Rate of Differentiating Through Optimization »
Damien Scieur · Gauthier Gidel · Quentin Bertrand · Fabian Pedregosa -
2022 Poster: Beyond L1: Faster and Better Sparse Models with skglm »
Quentin Bertrand · Quentin Klopfenstein · Pierre-Antoine Bannier · Gauthier Gidel · Mathurin Massias -
2022 Poster: Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution »
Antonio Orvieto · Simon Lacoste-Julien · Nicolas Loizou -
2022 Poster: Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities »
Eduard Gorbunov · Adrien Taylor · Gauthier Gidel -
2021 Poster: 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 -
2021 Poster: A single gradient step finds adversarial examples on random two-layers neural networks »
Sebastien Bubeck · Yeshwanth Cherapanamjeri · Gauthier Gidel · Remi Tachet des Combes -
2020 : Poster Session 3 (gather.town) »
Denny Wu · Chengrun Yang · Tolga Ergen · sanae lotfi · Charles Guille-Escuret · Boris Ginsburg · Hanbake Lyu · Cong Xie · David Newton · Debraj Basu · Yewen Wang · James Lucas · MAOJIA LI · Lijun Ding · Jose Javier Gonzalez Ortiz · Reyhane Askari Hemmat · Zhiqi Bu · Neal Lawton · Kiran Thekumparampil · Jiaming Liang · Lindon Roberts · Jingyi Zhu · Dongruo Zhou -
2020 Workshop: Machine Learning for Economic Policy »
Stephan Zheng · Alexander Trott · Annie Liang · Jamie Morgenstern · David Parkes · Nika Haghtalab -
2020 Poster: Adversarial Example Games »
Joey Bose · Gauthier Gidel · Hugo Berard · Andre Cianflone · Pascal Vincent · Simon Lacoste-Julien · Will Hamilton -
2020 Poster: Differentiable Causal Discovery from Interventional Data »
Philippe Brouillard · Sébastien Lachapelle · Alexandre Lacoste · Simon Lacoste-Julien · Alexandre Drouin -
2020 Spotlight: Differentiable Causal Discovery from Interventional Data »
Philippe Brouillard · Sébastien Lachapelle · Alexandre Lacoste · Simon Lacoste-Julien · Alexandre Drouin -
2020 Poster: Real World Games Look Like Spinning Tops »
Wojciech Czarnecki · Gauthier Gidel · Brendan Tracey · Karl Tuyls · Shayegan Omidshafiei · David Balduzzi · Max Jaderberg -
2019 : Closing Remarks »
Bo Dai · Niao He · Nicolas Le Roux · Lihong Li · Dale Schuurmans · Martha White -
2019 : Poster and Coffee Break 2 »
Karol Hausman · Kefan Dong · Ken Goldberg · Lihong Li · Lin Yang · Lingxiao Wang · Lior Shani · Liwei Wang · Loren Amdahl-Culleton · Lucas Cassano · Marc Dymetman · Marc Bellemare · Marcin Tomczak · Margarita Castro · Marius Kloft · Marius-Constantin Dinu · Markus Holzleitner · Martha White · Mengdi Wang · Michael Jordan · Mihailo Jovanovic · Ming Yu · Minshuo Chen · Moonkyung Ryu · Muhammad Zaheer · Naman Agarwal · Nan Jiang · Niao He · Nikolaus Yasui · Nikos Karampatziakis · Nino Vieillard · Ofir Nachum · Olivier Pietquin · Ozan Sener · Pan Xu · Parameswaran Kamalaruban · Paul Mineiro · Paul Rolland · Philip Amortila · Pierre-Luc Bacon · Prakash Panangaden · Qi Cai · Qiang Liu · Quanquan Gu · Raihan Seraj · Richard Sutton · Rick Valenzano · Robert Dadashi · Rodrigo Toro Icarte · Roshan Shariff · Roy Fox · Ruosong Wang · Saeed Ghadimi · Samuel Sokota · Sean Sinclair · Sepp Hochreiter · Sergey Levine · Sergio Valcarcel Macua · Sham Kakade · Shangtong Zhang · Sheila McIlraith · Shie Mannor · Shimon Whiteson · Shuai Li · Shuang Qiu · Wai Lok Li · Siddhartha Banerjee · Sitao Luan · Tamer Basar · Thinh Doan · Tianhe Yu · Tianyi Liu · Tom Zahavy · Toryn Klassen · Tuo Zhao · Vicenç Gómez · Vincent Liu · Volkan Cevher · Wesley Suttle · Xiao-Wen Chang · Xiaohan Wei · Xiaotong Liu · Xingguo Li · Xinyi Chen · Xingyou Song · Yao Liu · YiDing Jiang · Yihao Feng · Yilun Du · Yinlam Chow · Yinyu Ye · Yishay Mansour · · Yonathan Efroni · Yongxin Chen · Yuanhao Wang · Bo Dai · Chen-Yu Wei · Harsh Shrivastava · Hongyang Zhang · Qinqing Zheng · SIDDHARTHA SATPATHI · Xueqing Liu · Andreu Vall -
2019 : Poster Spotlight 1 »
David Brandfonbrener · Joan Bruna · Tom Zahavy · Haim Kaplan · Yishay Mansour · Nikos Karampatziakis · John Langford · Paul Mineiro · Donghwan Lee · Niao He -
2019 Workshop: The Optimization Foundations of Reinforcement Learning »
Bo Dai · Niao He · Nicolas Le Roux · Lihong Li · Dale Schuurmans · Martha White -
2019 : Opening Remarks »
Bo Dai · Niao He · Nicolas Le Roux · Lihong Li · Dale Schuurmans · Martha White -
2019 : Poster Session »
Gergely Flamich · Shashanka Ubaru · Charles Zheng · Josip Djolonga · Kristoffer Wickstrøm · Diego Granziol · Konstantinos Pitas · Jun Li · Robert Williamson · Sangwoong Yoon · Kwot Sin Lee · Julian Zilly · Linda Petrini · Ian Fischer · Zhe Dong · Alexander Alemi · Bao-Ngoc Nguyen · Rob Brekelmans · Tailin Wu · Aditya Mahajan · Alexander Li · Kirankumar Shiragur · Yair Carmon · Linara Adilova · SHIYU LIU · Bang An · Sanjeeb Dash · Oktay Gunluk · Arya Mazumdar · Mehul Motani · Julia Rosenzweig · Michael Kamp · Marton Havasi · Leighton P Barnes · Zhengqing Zhou · Yi Hao · Dylan Foster · Yuval Benjamini · Nati Srebro · Michael Tschannen · Paul Rubenstein · Sylvain Gelly · John Duchi · Aaron Sidford · Robin Ru · Stefan Zohren · Murtaza Dalal · Michael A Osborne · Stephen J Roberts · Moses Charikar · Jayakumar Subramanian · Xiaodi Fan · Max Schwarzer · Nicholas Roberts · Simon Lacoste-Julien · Vinay Prabhu · Aram Galstyan · Greg Ver Steeg · Lalitha Sankar · Yung-Kyun Noh · Gautam Dasarathy · Frank Park · Ngai-Man (Man) Cheung · Ngoc-Trung Tran · Linxiao Yang · Ben Poole · Andrea Censi · Tristan Sylvain · R Devon Hjelm · Bangjie Liu · Jose Gallego-Posada · Tyler Sypherd · Kai Yang · Jan Nikolas Morshuis -
2019 : Poster Session »
Jonathan Scarlett · Piotr Indyk · Ali Vakilian · Adrian Weller · Partha P Mitra · Benjamin Aubin · Bruno Loureiro · Florent Krzakala · Lenka Zdeborová · Kristina Monakhova · Joshua Yurtsever · Laura Waller · Hendrik Sommerhoff · Michael Moeller · Rushil Anirudh · Shuang Qiu · Xiaohan Wei · Zhuoran Yang · Jayaraman Thiagarajan · Salman Asif · Michael Gillhofer · Johannes Brandstetter · Sepp Hochreiter · Felix Petersen · Dhruv Patel · Assad Oberai · Akshay Kamath · Sushrut Karmalkar · Eric Price · Ali Ahmed · Zahra Kadkhodaie · Sreyas Mohan · Eero Simoncelli · Carlos Fernandez-Granda · Oscar Leong · Wesam Sakla · Rebecca Willett · Stephan Hoyer · Jascha Sohl-Dickstein · Sam Greydanus · Gauri Jagatap · Chinmay Hegde · Michael Kellman · Jonathan Tamir · Nouamane Laanait · Ousmane Dia · Mirco Ravanelli · Jonathan Binas · Negar Rostamzadeh · Shirin Jalali · Tiantian Fang · Alex Schwing · Sébastien Lachapelle · Philippe Brouillard · Tristan Deleu · Simon Lacoste-Julien · Stella Yu · Arya Mazumdar · Ankit Singh Rawat · Yue Zhao · Jianshu Chen · Xiaoyang Li · Hubert Ramsauer · Gabrio Rizzuti · Nikolaos Mitsakos · Dingzhou Cao · Thomas Strohmer · Yang Li · Pei Peng · Gregory Ongie -
2019 Poster: Reducing Noise in GAN Training with Variance Reduced Extragradient »
Tatjana Chavdarova · Gauthier Gidel · François Fleuret · Simon Lacoste-Julien -
2019 Poster: Exponential Family Estimation via Adversarial Dynamics Embedding »
Bo Dai · Zhen Liu · Hanjun Dai · Niao He · Arthur Gretton · Le Song · Dale Schuurmans -
2019 Poster: Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks »
Gauthier Gidel · Francis Bach · Simon Lacoste-Julien -
2019 Poster: Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates »
Sharan Vaswani · Aaron Mishkin · Issam Laradji · Mark Schmidt · Gauthier Gidel · Simon Lacoste-Julien -
2019 Poster: Toward a Characterization of Loss Functions for Distribution Learning »
Nika Haghtalab · Cameron Musco · Bo Waggoner -
2019 Poster: Reducing the variance in online optimization by transporting past gradients »
Sébastien Arnold · Pierre-Antoine Manzagol · Reza Babanezhad Harikandeh · Ioannis Mitliagkas · Nicolas Le Roux -
2019 Poster: Learning Positive Functions with Pseudo Mirror Descent »
Yingxiang Yang · Haoxiang Wang · Negar Kiyavash · Niao He -
2019 Spotlight: Learning Positive Functions with Pseudo Mirror Descent »
Yingxiang Yang · Haoxiang Wang · Negar Kiyavash · Niao He -
2019 Spotlight: Reducing the variance in online optimization by transporting past gradients »
Sébastien Arnold · Pierre-Antoine Manzagol · Reza Babanezhad Harikandeh · Ioannis Mitliagkas · Nicolas Le Roux -
2019 Poster: Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics »
Giancarlo Kerg · Kyle Goyette · Maximilian Puelma Touzel · Gauthier Gidel · Eugene Vorontsov · Yoshua Bengio · Guillaume Lajoie -
2018 : Smooth Games in Machine Learning Beyond GANs »
Niao He -
2018 : Opening remarks »
Simon Lacoste-Julien · Gauthier Gidel -
2018 Workshop: Smooth Games Optimization and Machine Learning »
Simon Lacoste-Julien · Ioannis Mitliagkas · Gauthier Gidel · Vasilis Syrgkanis · Eva Tardos · Leon Bottou · Sebastian Nowozin -
2018 Poster: Coupled Variational Bayes via Optimization Embedding »
Bo Dai · Hanjun Dai · Niao He · Weiyang Liu · Zhen Liu · Jianshu Chen · Lin Xiao · Le Song -
2018 Poster: Quantifying Learning Guarantees for Convex but Inconsistent Surrogates »
Kirill Struminsky · Simon Lacoste-Julien · Anton Osokin -
2018 Poster: Predictive Approximate Bayesian Computation via Saddle Points »
Yingxiang Yang · Bo Dai · Negar Kiyavash · Niao He -
2018 Poster: Quadratic Decomposable Submodular Function Minimization »
Pan Li · Niao He · Olgica Milenkovic -
2017 : A3T: Adversarially Augmented Adversarial Training »
Aristide Baratin · Simon Lacoste-Julien · Yoshua Bengio · Akram Erraqabi -
2017 : On Structured Prediction Theory with Calibrated Convex Surrogate Losses. »
Simon Lacoste-Julien -
2017 Workshop: Learning in the Presence of Strategic Behavior »
Nika Haghtalab · Yishay Mansour · Tim Roughgarden · Vasilis Syrgkanis · Jennifer Wortman Vaughan -
2017 Poster: Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization »
Fabian Pedregosa · Rémi Leblond · Simon Lacoste-Julien -
2017 Poster: On Structured Prediction Theory with Calibrated Convex Surrogate Losses »
Anton Osokin · Francis Bach · Simon Lacoste-Julien -
2017 Poster: Collaborative PAC Learning »
Avrim Blum · Nika Haghtalab · Ariel Procaccia · Mingda Qiao -
2017 Poster: Online Learning for Multivariate Hawkes Processes »
Yingxiang Yang · Jalal Etesami · Niao He · Negar Kiyavash -
2017 Poster: Online Learning with a Hint »
Ofer Dekel · arthur flajolet · Nika Haghtalab · Patrick Jaillet -
2017 Spotlight: Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization »
Fabian Pedregosa · Rémi Leblond · Simon Lacoste-Julien -
2017 Oral: On Structured Prediction Theory with Calibrated Convex Surrogate Losses »
Anton Osokin · Francis Bach · Simon Lacoste-Julien -
2016 Workshop: OPT 2016: Optimization for Machine Learning »
Suvrit Sra · Francis Bach · Sashank J. Reddi · Niao He -
2016 Poster: PAC-Bayesian Theory Meets Bayesian Inference »
Pascal Germain · Francis Bach · Alexandre Lacoste · Simon Lacoste-Julien -
2015 Poster: On the Global Linear Convergence of Frank-Wolfe Optimization Variants »
Simon Lacoste-Julien · Martin Jaggi -
2015 Poster: Barrier Frank-Wolfe for Marginal Inference »
Rahul G Krishnan · Simon Lacoste-Julien · David Sontag -
2015 Poster: Variance Reduced Stochastic Gradient Descent with Neighbors »
Thomas Hofmann · Aurelien Lucchi · Simon Lacoste-Julien · Brian McWilliams -
2015 Poster: Rethinking LDA: Moment Matching for Discrete ICA »
Anastasia Podosinnikova · Francis Bach · Simon Lacoste-Julien -
2014 Poster: SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives »
Aaron Defazio · Francis Bach · Simon Lacoste-Julien -
2009 Workshop: The Generative and Discriminative Learning Interface »
Simon Lacoste-Julien · Percy Liang · Guillaume Bouchard -
2008 Poster: DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification »
Simon Lacoste-Julien · Fei Sha · Michael Jordan