6 papers · 1 filter
Embedded Conditional Independence Tests for Large Language Model Generated Text with an Application to German Parliament Speeches
Marco Simnacher, Georg Keilbar, Benjamin König +2
Conditional independence tests (CITs) test for conditional dependence between two random objects and given a third random object . Existing CITs have limited applicabili…
Counterfactual Explanations for Deep Two-Sample Testing
Wei-Cheng Lai, Marco Simnacher, Christoph Lippert
Two-sample testing is a fundamental tool for detecting distributional differences across scientific domains, but classical tests (including kernel-based tests) can be ineffective o…
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…
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…
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…
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…