3 papers
stat.ML2026
Unsupervised Continual Learning for Amortized Bayesian Inference
Aayush Mishra, Šimon Kucharský, Paul-Christian Bürkner
Amortized Bayesian Inference (ABI) enables efficient posterior estimation using generative neural networks trained on simulated data, but often suffers from performance degradation…
stat.ML2026
From Mice to Trains: Amortized Bayesian Inference on Graph Data
Svenja Jedhoff, Elizaveta Semenova, Aura Raulo +2
Graphs arise across diverse domains, from biology and chemistry to social and information networks, as well as in transportation and logistics. Inference on graph-structured data r…
stat.ME2025
Hilbert space methods for approximating multi-output latent variable Gaussian processes
Soham Mukherjee, Manfred Claassen, Paul-Christian Bürkner
Gaussian processes are a powerful class of non-linear models, but have limited applicability for larger datasets due to their high computational complexity. In such cases, approxim…