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Learning Generalized Nash Equilibria in a Class of Convex Games

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Fréchet means in Wasserstein space

Yoav Zemel

This work studies the problem of statistical inference for Fréchet means in the Wasserstein space of measures on Euclidean spaces,

\mathcal W_2 ( \mathbb R^d )

. This question arises naturally from the problem of separating amplitude and phase variation i ...

EPFL2017

Learning Generalized Nash Equilibria in a Class of Convex Games

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Non-Discriminatory Machine Learning through Convex Fairness Criteria

Payoff-Based Approach to Learning Nash Equilibria in Convex Games * *This research is partially supported by M. Kamgarpour’s European Union ERC Starting Grant, CONENE.

On Coordinated Primal-Dual Interior-Point Methods for Multi-Agent Optimization

Fréchet means in Wasserstein space

Exact Diffusion for Distributed Optimization and Learning---Part II: Convergence Analysis

A feasible-side globally convergent modifier-adaptation scheme

On aggregative and mean field games with applications to electricity markets

Distributed learning over multitask networks with linearly related tasks

Modelling Resource Dependencies

Real-Time Optimization Based on Adaptation of Surrogate Models

Non-Discriminatory Machine Learning through Convex Fairness Criteria

Exact Diffusion for Distributed Optimization and Learning---Part II: Convergence Analysis

A feasible-side globally convergent modifier-adaptation scheme

Modelling Resource Dependencies

Payoff-Based Approach to Learning Nash Equilibria in Convex Games * *This research is partially supported by M. Kamgarpour’s European Union ERC Starting Grant, CONENE.

On Coordinated Primal-Dual Interior-Point Methods for Multi-Agent Optimization

Fréchet means in Wasserstein space

On aggregative and mean field games with applications to electricity markets

Distributed learning over multitask networks with linearly related tasks

Real-Time Optimization Based on Adaptation of Surrogate Models