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Found 15 papers, 10 papers with code
Split Conformal Prediction under Data Contamination
Conformal prediction is a non-parametric technique for constructing prediction intervals or sets from arbitrary predictive models under the assumption that the data is exchangeable.
SteinGen: Generating Fidelitous and Diverse Graph Samples
Generating graphs that preserve characteristic structures while promoting sample diversity can be challenging, especially when the number of graph observations is small.
Nonlinear Causal Discovery via Kernel Anchor Regression
Anchor regression has been developed to address this problem for a large class of causal graphical models, though the relationships between the variables are assumed to be linear.
On RKHS Choices for Assessing Graph Generators via Kernel Stein Statistics
Score-based kernelised Stein discrepancy (KSD) tests have emerged as a powerful tool for the goodness of fit tests, especially in high dimensions; however, the test performance may depend on the choice of kernels in an underlying reproducing kernel Hilbert space (RKHS).
A Kernelised Stein Statistic for Assessing Implicit Generative Models
Assessing the quality of such synthetic data generators hence has to be addressed.
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators
We propose and analyse a novel statistical procedure, coined AgraSSt, to assess the quality of graph generators that may not be available in explicit form.
Standardisation-function Kernel Stein Discrepancy: A Unifying View on Kernel Stein Discrepancy Tests for Goodness-of-fit
Non-parametric goodness-of-fit testing procedures based on kernel Stein discrepancies (KSD) are promising approaches to validate general unnormalised distributions in various scenarios.
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data
In realistic scenarios with very limited numbers of data samples, however, it can be challenging to identify a kernel powerful enough to distinguish complex distributions.
A Stein Goodness of fit Test for Exponential Random Graph Models
We propose and analyse a novel nonparametric goodness of fit testing procedure for exchangeable exponential random graph models (ERGMs) when a single network realisation is observed.
A kernel test for quasi-independence
We consider settings in which the data of interest correspond to pairs of ordered times, e. g, the birth times of the first and second child, the times at which a new user creates an account and makes the first purchase on a website, and the entry and survival times of patients in a clinical trial.
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
Survival Analysis and Reliability Theory are concerned with the analysis of time-to-event data, in which observations correspond to waiting times until an event of interest such as death from a particular disease or failure of a component in a mechanical system.
Learning Deep Kernels for Non-Parametric Two-Sample Tests
We propose a class of kernel-based two-sample tests, which aim to determine whether two sets of samples are drawn from the same distribution.
Ranked #1 on
Two-sample testing
on HIGGS Data Set
Model Reuse with Reduced Kernel Mean Embedding Specification
Given a publicly available pool of machine learning models constructed for various tasks, when a user plans to build a model for her own machine learning application, is it possible to build upon models in the pool such that the previous efforts on these existing models can be reused rather than starting from scratch?
Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs
On the other hand, compressing the vertices while preserving the directed edge information provides a way to learn the small-scale representation of a directed graph.
A Linear-Time Kernel Goodness-of-Fit Test
We propose a novel adaptive test of goodness-of-fit, with computational cost linear in the number of samples.
