Pages that link to "Item:Q2103876"
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The following pages link to Tackling algorithmic bias in neural-network classifiers using Wasserstein-2 regularization (Q2103876):
Displaying 5 items.
- Wasserstein-based fairness interpretability framework for machine learning models (Q2102385) (← links)
- A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data (Q2138799) (← links)
- An Improved Central Limit Theorem and Fast Convergence Rates for Entropic Transportation Costs (Q6136231) (← links)
- Central limit theorems for general transportation costs (Q6596218) (← links)
- Nonlinear inverse optimal transport: identifiability of the transport cost from its marginals and optimal values (Q6652062) (← links)
