62 citations · 68 across the 16 of their papers we have counts for
18 papers
Quantum Compression for Distributed Entanglement
Jan Østergaard, Shashi Raj Pandey, Christophe Biscio +2
We study compression strategies for multipartite entanglement distribution under uncertainty in the partitioning of the quantum state. When the partition is not known at the time o…
Multi-Agent Conformal Prediction with Personalized Statistical Validity
Martin V. Vejling, Christophe A. N. Biscio, Adrien Mazoyer +2
Uncertainty quantification is essential in high-stakes machine learning tasks. However, one of the principled solutions, conformal prediction, faces challenges under limited local…
Conformal Data Contamination Tests for In-distribution Data Acquisition
Martin V. Vejling, Shashi Raj Pandey, Christophe A. N. Biscio +1
The amount of quality data in many machine learning tasks is limited to what is available locally to data owners. The set of quality data can be expanded through trading or sharing…
Conformal novelty detection for replicate point patterns with FDR or FWER control
Christophe A. N. Biscio, Adrien Mazoyer, Martin V. Vejling
Monte Carlo tests are widely used for computing valid p-values without requiring known distributions of test statistics. When performing multiple Monte Carlo tests, it is essential…
RIS-Assisted High Resolution Radar Sensing
Martin Voigt Vejling, Hyowon Kim, Christophe A. N. Biscio +2
This paper analyzes monostatic sensing by a user equipment (UE) for a setting in which the UE is unable to resolve multiple targets due to their interference within a single resolu…
Prediction of Rare Channel Conditions using Bayesian Statistics and Extreme Value Theory
Tobias Kallehauge, Anders E. Kalør, Pablo Ramírez-Espinosa +2
Estimating the probability of rare channel conditions is a central challenge in ultra-reliable wireless communication, where random events, such as deep fades, can cause sudden var…
Multi-Sensor Multi-Scan Radar Sensing of Multiple Extended Targets
Martin V. Vejling, Christophe A. N. Biscio, Petar Popovski
We propose an efficient solution to the state estimation problem in multi-scan multi-sensor multiple extended target sensing scenarios. We first model the measurement process by a…
A functional central limit theorem for the K-function with an estimated intensity function
Anne Marie Svane, Christophe Biscio, Rasmus Waagepetersen
The -function is arguably the most important functional summary statistic for spatial point processes. It is used extensively for goodness-of-fit testing and in connection with…
Predictive Rate Selection for Ultra-Reliable Communication using Statistical Radio Maps
Tobias Kallehauge, Pablo Ramìrez-Espinosa, Anders E. Kalør +2
This paper proposes exploiting the spatial correlation of wireless channel statistics beyond the conventional received signal strength maps by constructing statistical radio maps t…
A Unified Approach for Multi-Scale Synchronous Correlation Search in Big Time Series -- Full Version
Nguyen Ho, Van Long Ho, Torben Bach Pedersen +2
The wide deployment of IoT sensors has enabled the collection of very big time series across different domains, from which advanced analytics can be performed to find unknown relat…
A functional central limit theorem for the empirical Ripley's K-function
Christophe A. N. Biscio, Anne Marie Svane
We establish a functional central limit theorem for Ripley's K-function for two classes of point processes. One is the class of point processes having exponential decay of correlat…
Statistical learning and cross-validation for point processes
Ottmar Cronie, Mehdi Moradi, Christophe A. N. Biscio
This paper presents the first general (supervised) statistical learning framework for point processes in general spaces. Our approach is based on the combination of two new concept…
Testing goodness of fit for point processes via topological data analysis
Christophe Ange Napoléon Biscio, Nicolas Chenavier, Christian Hirsch +1
We introduce tests for the goodness of fit of point patterns via methods from topological data analysis. More precisely, the persistent Betti numbers give rise to a bivariate funct…
The accumulated persistence function, a new useful functional summary statistic for topological data analysis, with a view to brain artery trees and spatial point process applications
Christophe Biscio, Jesper Møller
We start with a simple introduction to topological data analysis where the most popular tool is called a persistent diagram. Briefly, a persistent diagram is a multiset of points i…
Standard and robust intensity parameter estimation for stationary determinantal point processes
Jean-François Coeurjolly, Christophe Ange Napoléon Biscio
This work is concerned with the estimation of the intensity parameter of a stationary determinantal point process. We consider the standard estimator, corresponding to the number o…
Contrast estimation for parametric stationary determinantal point processes
Christophe Biscio, Frédéric Lavancier
We study minimum contrast estimation for parametric stationary determi-nantal point processes. These processes form a useful class of models for repulsive (or regular, or inhibitiv…
Brillinger mixing of determinantal point processes and statistical applications
Christophe Ange Napoléon Biscio, Frédéric Lavancier
Stationary determinantal point processes are proved to be Brillinger mixing. This property is an important step towards asymptotic statistics for these processes. As an important e…
Quantifying repulsiveness of determinantal point processes
Christophe Ange Napoléon Biscio, Frédéric Lavancier
Determinantal point processes (DPPs) have recently proved to be a useful class of models in several areas of statistics, including spatial statistics, statistical learning and tele…