182 citations · 575 across the 21 of their papers we have counts for
22 papers
(Sparse) Attention to the Details: Preserving Spectral Fidelity in ML-based Weather Forecasting Models
Maksim Zhdanov, Ana Lucic, Max Welling +1
We introduce Mosaic, a probabilistic weather forecasting model that addresses three failure modes of spectral degradation in ML-based weather prediction: spectral damping (statisti…
Inverse Concave-Utility Reinforcement Learning is Inverse Game Theory
Mustafa Mert Çelikok, Frans A. Oliehoek, Jan-Willem van de Meent
We consider inverse reinforcement learning problems with concave utilities. Concave Utility Reinforcement Learning (CURL) is a generalisation of the standard RL objective, which em…
VISA: Variational Inference with Sequential Sample-Average Approximations
Heiko Zimmermann, Christian A. Naesseth, Jan-Willem van de Meent
We present variational inference with sequential sample-average approximation (VISA), a method for approximate inference in computationally intensive models, such as those based on…
Learning Neural Free-Energy Functionals with Pair-Correlation Matching
Jacobus Dijkman, Marjolein Dijkstra, René van Roij +3
The intrinsic Helmholtz free-energy functional, the centerpiece of classical density functional theory, is at best only known approximately for 3D systems. Here we introduce a meth…
String Diagrams with Factorized Densities
Eli Sennesh, Jan-Willem van de Meent
A growing body of research on probabilistic programs and causal models has highlighted the need to reason compositionally about model classes that extend directed graphical models.…
A Variational Perspective on Generative Flow Networks
Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent +1
Generative flow networks (GFNs) are a class of models for sequential sampling of composite objects, which approximate a target distribution that is defined in terms of an energy fu…