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Found 7 papers, 2 papers with code
Collaborative non-parametric two-sample testing
This paper addresses the multiple two-sample test problem in a graph-structured setting, which is a common scenario in fields such as Spatial Statistics and Neuroscience.
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization
Quantifying the difference between two probability density functions, $p$ and $q$, using available data, is a fundamental problem in Statistics and Machine Learning.
Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation
Consider each node of a graph to be generating a data stream that is synchronized and observed at near real-time.
Collaborative likelihood-ratio estimation over graphs
In this paper, we introduce the first -to the best of our knowledge-graph-based extension of this problem, which reads as follows: Suppose each node $v$ of a fixed graph has access to observations coming from two unknown node-specific pdfs, $p_v$ and $q_v$, and the goal is to estimate for each node the likelihood-ratio between both pdfs by also taking into account the information provided by the graph structure.
Online non-parametric change-point detection for heterogeneous data streams observed over graph nodes
Consider a heterogeneous data stream being generated by the nodes of a graph.
ADAPT : Awesome Domain Adaptation Python Toolbox
In this paper, we introduce the ADAPT library, an open source Python API providing the implementation of the main transfer learning and domain adaptation methods.
Offline detection of change-points in the mean for stationary graph signals
This paper addresses the problem of segmenting a stream of graph signals: we aim to detect changes in the mean of a multivariate signal defined over the nodes of a known graph.
