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04/15/2022
Universal approximation property of invertible neural networks
Invertible neural networks (INNs) are neural network architectures with ...
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12/19/2021
Rethinking Importance Weighting for Transfer Learning
A key assumption in supervised learning is that training and test data f...
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02/27/2021
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation
Causal graphs (CGs) are compact representations of the knowledge of the ...
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12/04/2020
Universal Approximation Property of Neural Ordinary Differential Equations
Neural ordinary differential equations (NODEs) is an invertible neural n...
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06/20/2020
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
Invertible neural networks based on coupling flows (CF-INNs) have variou...
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06/13/2020
γ-ABC: Outlier-Robust Approximate Bayesian Computation based on Robust Divergence Estimator
Making a reliable inference in complex models is an essential issue in s...
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06/12/2020
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation
The estimation of the ratio of two probability densities has garnered at...
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02/10/2020
Few-shot Domain Adaptation by Causal Mechanism Transfer
We study few-shot supervised domain adaptation (DA) for regression probl...
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09/13/2018