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Refining generative process with discriminator guidance in score-based diffusion models
The proposed method, Discriminator Guidance, aims to improve sample generation of pre-
trained diffusion models. The approach introduces a discriminator that gives explicit …
trained diffusion models. The approach introduces a discriminator that gives explicit …
Adapting to continuous covariate shift via online density ratio estimation
Dealing with distribution shifts is one of the central challenges for modern machine learning.
One fundamental situation is the covariate shift, where the input distributions of data change …
One fundamental situation is the covariate shift, where the input distributions of data change …
Towards a unified analysis of kernel-based methods under covariate shift
Covariate shift occurs prevalently in practice, where the input distributions of the source and
target data are substantially different. Despite its practical importance in various learning …
target data are substantially different. Despite its practical importance in various learning …
Estimating the density ratio between distributions with high discrepancy using multinomial logistic regression
Functions of the ratio of the densities $ p/q $ are widely used in machine learning to quantify
the discrepancy between the two distributions $ p $ and $ q $. For high-dimensional …
the discrepancy between the two distributions $ p $ and $ q $. For high-dimensional …
Overcoming saturation in density ratio estimation by iterated regularization
Estimating the ratio of two probability densities from finitely many samples, is a central task
in machine learning and statistics. In this work, we show that a large class of kernel methods …
in machine learning and statistics. In this work, we show that a large class of kernel methods …
Meta-learning for relative density-ratio estimation
The ratio of two probability densities, called a density-ratio, is a vital quantity in machine
learning. In particular, a relative density-ratio, which is a bounded extension of the density …
learning. In particular, a relative density-ratio, which is a bounded extension of the density …
Revealing Distribution Discrepancy by Sampling Transfer in Unlabeled Data
There are increasing cases where the class labels of test samples are unavailable, creating
a significant need and challenge in measuring the discrepancy between training and test …
a significant need and challenge in measuring the discrepancy between training and test …
Computing high-dimensional optimal transport by flow neural networks
Flow-based models are widely used in generative tasks, including normalizing flow, where a
neural network transports from a data distribution $ P $ to a normal distribution. This work …
neural network transports from a data distribution $ P $ to a normal distribution. This work …
Deep bregman divergence for self-supervised representations learning
Neural Bregman divergence measures the divergence of data points using convex neural
networks, which is beyond Euclidean distance and capable of capturing divergence over …
networks, which is beyond Euclidean distance and capable of capturing divergence over …
Federated learning under covariate shifts with generalization guarantees
This paper addresses intra-client and inter-client covariate shifts in federated learning (FL)
with a focus on the overall generalization performance. To handle covariate shifts, we …
with a focus on the overall generalization performance. To handle covariate shifts, we …