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Found 7 papers, 2 papers with code
Mirror Descent-Ascent for mean-field min-max problems
We study two variants of the mirror descent-ascent algorithm for solving min-max problems on the space of measures: simultaneous and sequential.
Generalization Error of Graph Neural Networks in the Mean-field Regime
This work provides a theoretical framework for assessing the generalization error of graph classification tasks via graph neural networks in the over-parameterized regime, where the number of parameters surpasses the quantity of data points.
Mean-field Analysis of Generalization Errors
We propose a novel framework for exploring weak and $L_2$ generalization errors of algorithms through the lens of differential calculus on the space of probability measures.
Inefficiency of CFMs: hedging perspective and agent-based simulations
We investigate whether the fee income from trades on the CFM is sufficient for the liquidity providers to hedge away the exposure to market risk.
Gradient Flows for Regularized Stochastic Control Problems
This paper studies stochastic control problems with the action space taken to be probability measures, with the objective penalised by the relative entropy.
Multi-index Antithetic Stochastic Gradient Algorithm
In this paper, we construct a Multi-index Antithetic Stochastic Gradient Algorithm (MASGA) whose implementation is independent of the structure of the target measure and which achieves performance on par with Monte Carlo estimators that have access to unbiased samples from the distribution of interest.
Mean-Field Neural ODEs via Relaxed Optimal Control
We develop a framework for the analysis of deep neural networks and neural ODE models that are trained with stochastic gradient algorithms.
