3 papers
stat.ML2026
The MAPS Algorithm: Fast model-agnostic and distribution-free prediction intervals for supervised learning
Daniel Salnikov, Dan Leonte, Kevin Michalewicz
A fundamental problem in modern supervised learning is computing reliable conditional prediction intervals in high-dimensional settings: existing methods often rely on restrictive…
stat.ME2025
Generalized network autoregressive modelling of longitudinal networks with application to presidential elections in the USA
Guy Nason, Daniel Salnikov, Mario Cortina-Borja
Longitudinal networks are becoming increasingly relevant in the study of dynamic processes characterised by known or inferred community structure. Generalised Network Autoregressiv…
stat.ME2024
Modelling clusters in network time series with an application to presidential elections in the USA
Guy Nason, Daniel Salnikov, Mario Cortina-Borja
Network time series are becoming increasingly relevant in the study of dynamic processes characterised by a known or inferred underlying network structure. Generalised Network Auto…