Classification with noisy labels by importance reweighting
In this paper, we study a classification problem in which sample labels are randomly
corrupted. In this scenario, there is an unobservable sample with noise-free labels …
corrupted. In this scenario, there is an unobservable sample with noise-free labels …
[KSIĄŻKA][B] Density ratio estimation in machine learning
Machine learning is an interdisciplinary field of science and engineering that studies
mathematical theories and practical applications of systems that learn. This book introduces …
mathematical theories and practical applications of systems that learn. This book introduces …
Change-point detection in time-series data by relative density-ratio estimation
The objective of change-point detection is to discover abrupt property changes lying behind
time-series data. In this paper, we present a novel statistical change-point detection …
time-series data. In this paper, we present a novel statistical change-point detection …
[KSIĄŻKA][B] Introduction to statistical machine learning
M Sugiyama - 2015 - books.google.com
Machine learning allows computers to learn and discern patterns without actually being
programmed. When Statistical techniques and machine learning are combined together they …
programmed. When Statistical techniques and machine learning are combined together they …
Few-shot domain adaptation by causal mechanism transfer
We study few-shot supervised domain adaptation (DA) for regression problems, where only
a few labeled target domain data and many labeled source domain data are available. Many …
a few labeled target domain data and many labeled source domain data are available. Many …
Maximum mean discrepancy test is aware of adversarial attacks
The maximum mean discrepancy (MMD) test could in principle detect any distributional
discrepancy between two datasets. However, it has been shown that the MMD test is …
discrepancy between two datasets. However, it has been shown that the MMD test is …
Alleviating semantics distortion in unsupervised low-level image-to-image translation via structure consistency constraint
Unsupervised image-to-image (I2I) translation aims to learn a domain map** function that
can preserve the semantics of the input images without paired data. However, because the …
can preserve the semantics of the input images without paired data. However, because the …
Estimating and explaining model performance when both covariates and labels shift
Deployed machine learning (ML) models often encounter new user data that differs from
their training data. Therefore, estimating how well a given model might perform on the new …
their training data. Therefore, estimating how well a given model might perform on the new …
Maximum spatial perturbation consistency for unpaired image-to-image translation
Unpaired image-to-image translation (I2I) is an ill-posed problem, as an infinite number of
translation functions can map the source domain distribution to the target distribution …
translation functions can map the source domain distribution to the target distribution …
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 …