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Found 6 papers, 4 papers with code
On the nonconvexity of some push-forward constraints and its consequences in machine learning
The push-forward operation enables one to redistribute a probability measure through a deterministic map.
Improved learning theory for kernel distribution regression with two-stage sampling
In this paper, we improve the learning theory of kernel distribution regression.
GAN Estimation of Lipschitz Optimal Transport Maps
This paper introduces the first statistically consistent estimator of the optimal transport map between two probability distributions, based on neural networks.
Transport-based Counterfactual Models
We address the problem of designing realistic and feasible counterfactuals in the absence of a causal model.
Pay attention to your loss: understanding misconceptions about 1-Lipschitz neural networks
However they remain commonly considered as less accurate, and their properties in learning are still not fully understood.
Achieving robustness in classification using optimal transport with hinge regularization
This loss function has a direct interpretation in terms of adversarial robustness together with certifiable robustness bound.
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