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Found 10 papers, 3 papers with code
Universal approximation property of invertible neural networks
Invertible neural networks (INNs) are neural network architectures with invertibility by design.
Rethinking Importance Weighting for Transfer Learning
A key assumption in supervised learning is that training and test data follow the same probability distribution.
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation
Causal graphs (CGs) are compact representations of the knowledge of the data generating processes behind the data distributions.
Universal Approximation Property of Neural Ordinary Differential Equations
Neural ordinary differential equations (NODEs) is an invertible neural network architecture promising for its free-form Jacobian and the availability of a tractable Jacobian determinant estimator.
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
We answer this question by showing a convenient criterion: a CF-INN is universal if its layers contain affine coupling and invertible linear functions as special cases.
$γ$-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator
Approximate Bayesian computation (ABC) is a likelihood-free inference method that has been employed in various applications.
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation
Density ratio estimation (DRE) is at the core of various machine learning tasks such as anomaly detection and domain adaptation.
Few-shot Domain Adaptation by Causal Mechanism Transfer
We take the structural equations in causal modeling as an example and propose a novel DA method, which is shown to be useful both theoretically and experimentally.
Learning from Positive and Unlabeled Data with a Selection Bias
However, this assumption is unrealistic in many instances of PU learning because it fails to capture the existence of a selection bias in the labeling process.
Clipped Matrix Completion: A Remedy for Ceiling Effects
On the other hand, matrix completion (MC) methods can recover a low-rank matrix from various information deficits by using the principle of low-rank completion.
