Documentdetail
ID kaart
oai:arXiv.org:2406.02317
Onderwerp
Computer Science - Machine Learnin... Computer Science - Artificial Inte... Statistics - Machine LearningAuteur
Nguyen, Bao Nguyen, Binh Nguyen, Hieu Trung Nguyen, Viet AnhCategorie
Computer Science
Jaar
2024
vermelding datum
12-06-2024
Trefwoorden
neural network generative learningBeschrijving
Learning conditional distributions is challenging because the desired outcome is not a single distribution but multiple distributions that correspond to multiple instances of the covariates.
We introduce a novel neural entropic optimal transport method designed to effectively learn generative models of conditional distributions, particularly in scenarios characterized by limited sample sizes.
Our method relies on the minimax training of two neural networks: a generative network parametrizing the inverse cumulative distribution functions of the conditional distributions and another network parametrizing the conditional Kantorovich potential.
To prevent overfitting, we regularize the objective function by penalizing the Lipschitz constant of the network output.
Our experiments on real-world datasets show the effectiveness of our algorithm compared to state-of-the-art conditional distribution learning techniques.
Our implementation can be found at https://github.com/nguyenngocbaocmt02/GENTLE.
;Comment: 15 pages, 8 figures
Nguyen, Bao,Nguyen, Binh,Nguyen, Hieu Trung,Nguyen, Viet Anh, 2024, Generative Conditional Distributions by Neural (Entropic) Optimal Transport
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