Timezone: »
Generative adversarial networks (GANs) are a widely used framework for learning generative models. Wasserstein GANs (WGANs), one of the most successful variants of GANs, require solving a minmax optimization problem to global optimality, but are in practice successfully trained using stochastic gradient descent-ascent. In this paper, we show that, when the generator is a one-layer network, stochastic gradient descent-ascent converges to a global solution with polynomial time and sample complexity.
Author Information
Qi Lei (University of Texas at Austin)
Jason Lee (Princeton)
Alex Dimakis (UT Austin)
Alex Dimakis is an Associate Professor at the Electrical and Computer Engineering department, University of Texas at Austin. He received his Ph.D. in electrical engineering and computer sciences from UC Berkeley. He received an ARO young investigator award in 2014, the NSF Career award in 2011, a Google faculty research award in 2012 and the Eli Jury dissertation award in 2008. He is the co-recipient of several best paper awards including the joint Information Theory and Communications Society Best Paper Award in 2012. His research interests include information theory, coding theory and machine learning.
Constantinos Daskalakis (MIT)
More from the Same Authors
-
2020 Poster: Optimal transport mapping via input convex neural networks »
Ashok Vardhan Makkuva · Amirhossein Taghvaei · Sewoong Oh · Jason Lee -
2019 Poster: Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling »
Shanshan Wu · Alexandros Dimakis · Sujay Sanghavi · Felix Xinnan Yu · Daniel Holtmann-Rice · Dmitry Storcheus · Afshin Rostamizadeh · Sanjiv Kumar -
2019 Oral: Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling »
Shanshan Wu · Alexandros Dimakis · Sujay Sanghavi · Felix Xinnan Yu · Daniel Holtmann-Rice · Dmitry Storcheus · Afshin Rostamizadeh · Sanjiv Kumar -
2018 Poster: Gradient Coding from Cyclic MDS Codes and Expander Graphs »
Netanel Raviv · Rashish Tandon · Alexandros Dimakis · Itzhak Tamo -
2018 Oral: Gradient Coding from Cyclic MDS Codes and Expander Graphs »
Netanel Raviv · Rashish Tandon · Alexandros Dimakis · Itzhak Tamo -
2018 Poster: Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization »
Jiong Zhang · Qi Lei · Inderjit Dhillon -
2018 Oral: Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization »
Jiong Zhang · Qi Lei · Inderjit Dhillon -
2017 Poster: Priv’IT: Private and Sample Efficient Identity Testing »
Bryan Cai · Constantinos Daskalakis · Gautam Kamath -
2017 Poster: Identifying Best Interventions through Online Importance Sampling »
Rajat Sen · Karthikeyan Shanmugam · Alexandros Dimakis · Sanjay Shakkottai -
2017 Poster: Cost-Optimal Learning of Causal Graphs »
Murat Kocaoglu · Alexandros Dimakis · Sriram Vishwanath -
2017 Poster: On Approximation Guarantees for Greedy Low Rank Optimization »
RAJIV KHANNA · Ethan R. Elenberg · Alexandros Dimakis · Joydeep Ghosh · Sahand Negahban -
2017 Talk: Identifying Best Interventions through Online Importance Sampling »
Rajat Sen · Karthikeyan Shanmugam · Alexandros Dimakis · Sanjay Shakkottai -
2017 Talk: On Approximation Guarantees for Greedy Low Rank Optimization »
RAJIV KHANNA · Ethan R. Elenberg · Alexandros Dimakis · Joydeep Ghosh · Sahand Negahban -
2017 Talk: Cost-Optimal Learning of Causal Graphs »
Murat Kocaoglu · Alexandros Dimakis · Sriram Vishwanath -
2017 Talk: Priv’IT: Private and Sample Efficient Identity Testing »
Bryan Cai · Constantinos Daskalakis · Gautam Kamath -
2017 Poster: Exact MAP Inference by Avoiding Fractional Vertices »
Erik Lindgren · Alexandros Dimakis · Adam Klivans -
2017 Poster: Compressed Sensing using Generative Models »
Ashish Bora · Ajil Jalal · Eric Price · Alexandros Dimakis -
2017 Poster: Gradient Coding: Avoiding Stragglers in Distributed Learning »
Rashish Tandon · Qi Lei · Alexandros Dimakis · Nikos Karampatziakis -
2017 Talk: Gradient Coding: Avoiding Stragglers in Distributed Learning »
Rashish Tandon · Qi Lei · Alexandros Dimakis · Nikos Karampatziakis -
2017 Talk: Compressed Sensing using Generative Models »
Ashish Bora · Ajil Jalal · Eric Price · Alexandros Dimakis -
2017 Talk: Exact MAP Inference by Avoiding Fractional Vertices »
Erik Lindgren · Alexandros Dimakis · Adam Klivans -
2017 Poster: Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization »
Qi Lei · En-Hsu Yen · Chao-Yuan Wu · Inderjit Dhillon · Pradeep Ravikumar -
2017 Talk: Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization »
Qi Lei · En-Hsu Yen · Chao-Yuan Wu · Inderjit Dhillon · Pradeep Ravikumar