527 citations · 1.7k across the 34 of their papers we have counts for
8 papers · 1 filter
Generalizing to unseen domains via distribution matching
Isabela Albuquerque, João Monteiro, Mohammad Darvishi +2
Supervised learning results typically rely on assumptions of i.i.d. data. Unfortunately, those assumptions are commonly violated in practice. In this work, we tackle such problem b…
Gradient penalty from a maximum margin perspective
Alexia Jolicoeur-Martineau, Ioannis Mitliagkas
A popular heuristic for improved performance in Generative adversarial networks (GANs) is to use some form of gradient penalty on the discriminator. This gradient penalty was origi…
Reducing the variance in online optimization by transporting past gradients
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad +2
Most stochastic optimization methods use gradients once before discarding them. While variance reduction methods have shown that reusing past gradients can be beneficial when there…
Linear Lower Bounds and Conditioning of Differentiable Games
Adam Ibrahim, Waïss Azizian, Gauthier Gidel +1
Recent successes of game-theoretic formulations in ML have caused a resurgence of research interest in differentiable games. Overwhelmingly, that research focuses on methods and up…
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Games
Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien +1
We consider differentiable games where the goal is to find a Nash equilibrium. The machine learning community has recently started using variants of the gradient method (GD). Prime…
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Alex Lamb, Jonathan Binas, Anirudh Goyal +5
Machine learning promises methods that generalize well from finite labeled data. However, the brittleness of existing neural net approaches is revealed by notable failures, such as…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…
Multi-objective training of Generative Adversarial Networks with multiple discriminators
Isabela Albuquerque, João Monteiro, Thang Doan +3
Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involvin…