Hugo Berard

According to our database1, Hugo Berard authored at least 13 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces.
CoRR, 2024

From Efficiency to Equity: Measuring Fairness in Preference Learning.
CoRR, 2024

2023
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Online Adversarial Attacks.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Stochastic Extragradient: General Analysis and Improved Rates.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Adversarial Example Games.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Stochastic Hamiltonian Gradient Methods for Smooth Games.
Proceedings of the 37th International Conference on Machine Learning, 2020

A Closer Look at the Optimization Landscapes of Generative Adversarial Networks.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
A Variational Inequality Perspective on Generative Adversarial Networks.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
A Variational Inequality Perspective on Generative Adversarial Nets.
CoRR, 2018

Parametric Adversarial Divergences are Good Task Losses for Generative Modeling.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Adversarial Divergences are Good Task Losses for Generative Modeling.
CoRR, 2017


  Loading...