6 papers · 1 filter
JAPAN: Joint Adaptive Prediction Areas with Normalising-Flows
Eshant English, Christoph Lippert
Conformal prediction provides a model-agnostic framework for uncertainty quantification with finite-sample validity guarantees, making it an attractive tool for constructing reliab…
JANET: Joint Adaptive predictioN-region Estimation for Time-series
Eshant English, Eliot Wong-Toi, Matteo Fontana +3
Conformal prediction provides machine learning models with prediction sets that offer theoretical guarantees, but the underlying assumption of exchangeability limits its applicabil…
Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series
Eshant English, Christoph Lippert
Conformal Prediction offers a powerful framework for quantifying uncertainty in machine learning models, enabling the construction of prediction sets with finite-sample validity gu…
MixerFlow: MLP-Mixer meets Normalising Flows
Eshant English, Matthias Kirchler, Christoph Lippert
Normalising flows are generative models that transform a complex density into a simpler density through the use of bijective transformations enabling both density estimation and da…
Kernelised Normalising Flows
Eshant English, Matthias Kirchler, Christoph Lippert
Normalising Flows are non-parametric statistical models characterised by their dual capabilities of density estimation and generation. This duality requires an inherently invertibl…
Joint Prediction Regions for time-series models
Eshant English
Machine Learning algorithms are notorious for providing point predictions but not prediction intervals. There are many applications where one requires confidence in predictions and…