Detection of differentially abundant cell subpopulations in scRNA-seq data
Comprehensive and accurate comparisons of transcriptomic distributions of cells from
samples taken from two different biological states, such as healthy versus diseased …
samples taken from two different biological states, such as healthy versus diseased …
Machine and Deep Learning applied to galaxy morphology-A comparative study
Morphological classification is a key piece of information to define samples of galaxies
aiming to study the large-scale structure of the universe. In essence, the challenge is to build …
aiming to study the large-scale structure of the universe. In essence, the challenge is to build …
Minimax optimality of permutation tests
Minimax optimality of permutation tests Page 1 The Annals of Statistics 2022, Vol. 50, No. 1,
225–251 https://doi.org/10.1214/21-AOS2103 © Institute of Mathematical Statistics, 2022 …
225–251 https://doi.org/10.1214/21-AOS2103 © Institute of Mathematical Statistics, 2022 …
Model-independent detection of new physics signals using interpretable SemiSupervised classifier tests
The supplementary material contains the proof of Theorem 4.1, some of the proposed
algorithms from Section 3.2, and details about the exploratory data analysis of the Higgs …
algorithms from Section 3.2, and details about the exploratory data analysis of the Higgs …
Few-sample feature selection via feature manifold learning
D Cohen, T Shnitzer, Y Kluger… - … on Machine Learning, 2023 - proceedings.mlr.press
In this paper, we present a new method for few-sample supervised feature selection (FS).
Our method first learns the manifold of the feature space of each class using kernels …
Our method first learns the manifold of the feature space of each class using kernels …
[HTML][HTML] On the use of random forest for two-sample testing
Following the line of classification-based two-sample testing, tests based on the Random
Forest classifier are proposed. The developed tests are easy to use, require almost no …
Forest classifier are proposed. The developed tests are easy to use, require almost no …
Sequential predictive two-sample and independence testing
A Podkopaev, A Ramdas - Advances in neural information …, 2024 - proceedings.neurips.cc
We study the problems of sequential nonparametric two-sample and independence testing.
Sequential tests process data online and allow using observed data to decide whether to …
Sequential tests process data online and allow using observed data to decide whether to …
Diagnostics for conditional density models and Bayesian inference algorithms
There has been growing interest in the AI community for precise uncertainty quantification.
Conditional density models f (y| x), where x represents potentially high-dimensional features …
Conditional density models f (y| x), where x represents potentially high-dimensional features …
A practical guide to statistical distances for evaluating generative models in science
Generative models are invaluable in many fields of science because of their ability to
capture high-dimensional and complicated distributions, such as photo-realistic images …
capture high-dimensional and complicated distributions, such as photo-realistic images …
A Review and Taxonomy of Methods for Quantifying Dataset Similarity
M Stolte, A Bommert, J Rahnenführer - arXiv preprint arXiv:2312.04078, 2023 - arxiv.org
In statistics and machine learning, measuring the similarity between two or more datasets is
important for several purposes. The performance of a predictive model on novel datasets …
important for several purposes. The performance of a predictive model on novel datasets …