18 citations · 18 across the 8 of their papers we have counts for
9 papers
Two-Sample Testing via Path-based Inference
Eshant English, Wei-Cheng Lai, Yanfeng Yang +3
Modern deep generative models are primarily studied for their ability to generate realistic samples, yet the generative dynamics they learn can also serve as objects of statistical…
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…
CORTEX: A Structured Reasoning Benchmark for Trustworthy 3D Chest CT MLLMs
Hashmat Shadab Malik, Anees Ur Rehman Hashmi, Numan Saeed +3
Reasoning in multimodal large language models (MLLMs) has shown strong promise in medical imaging. However, this reasoning is usually free-form text judged only by its final answer…
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…
On the Challenges and Opportunities in Generative AI
Laura Manduchi, Clara Meister, Kushagra Pandey +23
The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervi…
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…