Search Results for author:
Found 22 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.
Stein Boltzmann Sampling: A Variational Approach for Global Optimization
In this paper, we introduce a new flow-based method for global optimization of Lipschitz functions, called Stein Boltzmann Sampling (SBS).
UniForCE: The Unimodality Forest Method for Clustering and Estimation of the Number of Clusters
In this work, we focus on the concept of unimodality and propose a flexible cluster definition called locally unimodal cluster.
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.
A framework for paired-sample hypothesis testing for high-dimensional data
First, we estimate the bisecting hyperplanes for each pair of instances and an aggregated rule derived through the Hodges-Lehmann estimator.
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.
To tree or not to tree? Assessing the impact of smoothing the decision boundaries
The approach we propose starts with the rigid decision boundaries of a seed Decision Tree (seed DT), which is used to initialize a Neural DT (NDT).
Clustering for directed graphs using parametrized random walk diffusion kernels
In this paper, we introduce a new clustering framework, the Parametrized Random Walk Diffusion Kernel Clustering (P-RWDKC), which is suitable for handling both directed and undirected graphs.
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.
Generalized Spectral Clustering for Directed and Undirected Graphs
In this paper, we present a generalized spectral clustering framework that can address both directed and undirected graphs.
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.
Winning the competition: enhancing counter-contagion in SIS-like epidemic processes
In this paper we consider the epidemic competition between two generic diffusion processes, where each competing side is represented by a different state of a stochastic process.
Model family selection for classification using Neural Decision Trees
In our approach, this is realized by progressively relaxing the decision boundaries of the initial decision trees (the RMs) as long as this is beneficial in terms of performance measured on an analyzed dataset.
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.
Optimal Multiple Stopping Rule for Warm-Starting Sequential Selection
In this paper we present the Warm-starting Dynamic Thresholding algorithm, developed using dynamic programming, for a variant of the standard online selection problem.
Learning the piece-wise constant graph structure of a varying Ising model
This work focuses on the estimation of multiple change-points in a time-varying Ising model that evolves piece-wise constantly.
Sequential Dynamic Resource Allocation for Epidemic Control
Under the Dynamic Resource Allocation (DRA) model, an administrator has the mission to allocate dynamically a limited budget of resources to the nodes of a network in order to reduce a diffusion process (DP) (e. g. an epidemic).
Revealing posturographic features associated with the risk of falling in patients with Parkinsonian syndromes via machine learning
In this work, we present the ts-AUC, a non-parametric multivariate two-sample test, which we employ to analyze statokinesigram differences among PS patients that are fallers (PSf) and non-fallers (PSNF).
A Probabilistic Framework to Node-level Anomaly Detection in Communication Networks
In this paper we consider the task of detecting abnormal communication volume occurring at node-level in communication networks.
A Spectral Method for Activity Shaping in Continuous-Time Information Cascades
Information Cascades Model captures dynamical properties of user activity in a social network.
Multivariate Hawkes Processes for Large-scale Inference
In this paper, we present a framework for fitting multivariate Hawkes processes for large-scale problems both in the number of events in the observed history $n$ and the number of event types $d$ (i. e. dimensions).
Dip-means: an incremental clustering method for estimating the number of clusters
The proposed algorithm considers each cluster member as a ''viewer'' and applies a univariate statistic hypothesis test for unimodality (dip-test) on the distribution of the distances between the viewer and the cluster members.
