Integrative conformal p-values for out-of-distribution testing with labelled outliers
This paper presents a conformal inference method for out-of-distribution testing that
leverages side information from labelled outliers, which are commonly underutilized or even …
leverages side information from labelled outliers, which are commonly underutilized or even …
Integrative conformal p-values for powerful out-of-distribution testing with labeled outliers
This paper develops novel conformal methods to test whether a new observation was
sampled from the same distribution as a reference set. Blending inductive and transductive …
sampled from the same distribution as a reference set. Blending inductive and transductive …
[HTML][HTML] Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis
We present a gene-level regulatory model, single-cell ATAC+ RNA linking (SCARlink),
which predicts single-cell gene expression and links enhancers to target genes using multi …
which predicts single-cell gene expression and links enhancers to target genes using multi …
Statistical inference and large-scale multiple testing for high-dimensional regression models
This paper presents a selective survey of recent developments in statistical inference and
multiple testing for high-dimensional regression models, including linear and logistic …
multiple testing for high-dimensional regression models, including linear and logistic …
Dynamic defect detection in fast, robust nde methods by transfer learning based optimally binned hypothesis tests
The structural integrity of safety-critical infrastructures diminishes over time, necessitating
periodic inspections. The gathering of low-noise, precise nondestructive evaluation (NDE) …
periodic inspections. The gathering of low-noise, precise nondestructive evaluation (NDE) …
[HTML][HTML] DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data
Voxel-based multiple testing is widely used in neuroimaging data analysis. Traditional false
discovery rate (FDR) control methods often ignore the spatial dependence among the voxel …
discovery rate (FDR) control methods often ignore the spatial dependence among the voxel …
Locally adaptive transfer learning algorithms for large-scale multiple testing
Transfer learning has enjoyed increasing popularity in a range of big data applications. In
the context of large-scale multiple testing, the goal is to extract and transfer knowledge …
the context of large-scale multiple testing, the goal is to extract and transfer knowledge …
Structure–adaptive sequential testing for online false discovery rate control
Consider the online testing of a stream of hypotheses where a real-time decision must be
made before the next data point arrives. The error rate is required to be controlled at all …
made before the next data point arrives. The error rate is required to be controlled at all …
Alteration Detection of Tensor Dependence Structure via Sparsity-Exploited Reranking Algorithm
L Ma, S Qin, Y Xia - arXiv preprint arXiv:2310.08798, 2023 - arxiv.org
Tensor-valued data arise frequently from a wide variety of scientific applications, and many
among them can be translated into an alteration detection problem of tensor dependence …
among them can be translated into an alteration detection problem of tensor dependence …
DART: Distance assisted recursive testing
Multiple testing is a commonly used tool in modern data science. Sometimes, the
hypotheses are embedded in a space; the distances between the hypotheses reflect their co …
hypotheses are embedded in a space; the distances between the hypotheses reflect their co …