Hugo Berard

According to our database, Hugo Berard authored at least 18 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

WeDesign: Generative AI-Facilitated Community Consultations for Urban Public Space Design.

Rashid Mushkani, Hugo Berard, Shin Koseki

CoRR, August, 2025

Co-Producing AI: Toward an Augmented, Participatory Lifecycle.

Rashid Mushkani, Hugo Berard, Toumadher Ammar, Cassandre Chatonnier, Shin Koseki

CoRR, August, 2025

Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes - Insights from Urban Studies.

Rashid Mushkani, Hugo Berard, Shin Koseki

CoRR, March, 2025

LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces.

Rashid Mushkani, Shravan Nayak, Hugo Berard, Allison Cohen, Shin Koseki, Hadrien Bertrand

CoRR, March, 2025

The Right to AI.

Rashid Mushkani, Hugo Berard, Allison Cohen, Shin Koeski

CoRR, January, 2025

2024

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

Shreeyash Gowaikar, Hugo Berard, Rashid Mushkani, Emmanuel Beaudry Marchand, Toumadher Ammar, Shin Koseki

CoRR, 2024

From Efficiency to Equity: Measuring Fairness in Preference Learning.

Shreeyash Gowaikar, Hugo Berard, Rashid Mushkani, Shin Koseki

CoRR, 2024

2023

Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods.

Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Online Adversarial Attacks.

Andjela Mladenovic, Avishek Joey Bose, Hugo Berard, William L. Hamilton, Simon Lacoste-Julien, Pascal Vincent, Gauthier Gidel

Proceedings of the Tenth International Conference on Learning Representations, 2022

Stochastic Extragradient: General Analysis and Improved Rates.

Eduard Gorbunov, Hugo Berard, Gauthier Gidel, Nicolas Loizou

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.

Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Adversarial Example Games.

Avishek Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton

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.

Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas

Proceedings of the 37th International Conference on Machine Learning, 2020

A Closer Look at the Optimization Landscapes of Generative Adversarial Networks.

Hugo Berard, Gauthier Gidel, Amjad Almahairi, Pascal Vincent, Simon Lacoste-Julien

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

A Variational Inequality Perspective on Generative Adversarial Networks.

Gauthier Gidel, Hugo Berard, Gaëtan Vignoud, Pascal Vincent, Simon Lacoste-Julien

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

A Variational Inequality Perspective on Generative Adversarial Nets.

Gauthier Gidel, Hugo Berard, Pascal Vincent, Simon Lacoste-Julien

CoRR, 2018

Parametric Adversarial Divergences are Good Task Losses for Generative Modeling.

Gabriel Huang, Hugo Berard, Ahmed Touati, Gauthier Gidel, Pascal Vincent, Simon Lacoste-Julien

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Adversarial Divergences are Good Task Losses for Generative Modeling.

Gabriel Huang, Gauthier Gidel, Hugo Berard, Ahmed Touati, Simon Lacoste-Julien

CoRR, 2017