110 citations · 253 across the 12 of their papers we have counts for
12 papers
Flexible Gravitational-Wave Parameter Estimation with Transformers
Annalena Kofler, Maximilian Dax, Stephen R. Green +6
Gravitational-wave data analysis relies on accurate and efficient methods to extract physical information from noisy detector signals, yet the increasing rate and complexity of obs…
Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models
Frederike Lübeck, Jonas Wildberger, Frederik Träuble +4
Cardiovascular disease (CVD) risk prediction models are essential for identifying high-risk individuals and guiding preventive actions. However, existing models struggle with the c…
Flow Matching for Atmospheric Retrieval of Exoplanets: Where Reliability meets Adaptive Noise Levels
Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax +4
Inferring atmospheric properties of exoplanets from observed spectra is key to understanding their formation, evolution, and habitability. Since traditional Bayesian approaches to…
Real-time gravitational-wave inference for binary neutron stars using machine learning
Maximilian Dax, Stephen R. Green, Jonathan Gair +7
Mergers of binary neutron stars (BNSs) emit signals in both the gravitational-wave (GW) and electromagnetic (EM) spectra. Famously, the 2017 multi-messenger observation of GW170817…
Evidence for eccentricity in the population of binary black holes observed by LIGO-Virgo-KAGRA
Nihar Gupte, Antoni Ramos-Buades, Alessandra Buonanno +9
Binary black holes (BBHs) in eccentric orbits produce distinct modulations in gravitational waves (GWs); measuring orbital eccentricity provides evidence for dynamical binary forma…
Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling
Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax +3
Atmospheric retrievals (AR) characterize exoplanets by estimating atmospheric parameters from observed light spectra, typically by framing the task as a Bayesian inference problem.…
Flow Matching for Scalable Simulation-Based Inference
Maximilian Dax, Jonas Wildberger, Simon Buchholz +3
Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional pro…
Out-of-Variable Generalization for Discriminative Models
Siyuan Guo, Jonas Wildberger, Bernhard Schölkopf
The ability of an agent to do well in new environments is a critical aspect of intelligence. In machine learning, this ability is known as or $\textit{out-of-dist…
On the Interventional Kullback-Leibler Divergence
Jonas Wildberger, Siyuan Guo, Arnab Bhattacharyya +1
Modern machine learning approaches excel in static settings where a large amount of i.i.d. training data are available for a given task. In a dynamic environment, though, an intell…
Adapting to noise distribution shifts in flow-based gravitational-wave inference
Jonas Wildberger, Maximilian Dax, Stephen R. Green +5
Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers $\unicode{x2013}$ producing results of comparable accur…
Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
Maximilian Dax, Stephen R. Green, Jonathan Gair +5
We combine amortized neural posterior estimation with importance sampling for fast and accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian po…
Beta-VAE Reproducibility: Challenges and Extensions
Miroslav Fil, Munib Mesinovic, Matthew Morris +1
-VAE is a follow-up technique to variational autoencoders that proposes special weighting of the KL divergence term in the VAE loss to obtain disentangled representations. Unsup…