A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios
arXiv:2512.23748v1 Announce Type: new
Abstract: For complex simulation problems, inferring parameters of scientific interest often precludes the use of classical likelihood-based techniques due to intractable likelihood functions. Simulation-based inference (SBI) methods forego the need for explici...
Large Language Models for EDA Cloud Job Resource and Lifetime Prediction
arXiv:2512.19701v1 Announce Type: new
Abstract: The rapid growth of cloud computing in the Electronic Design Automation (EDA) industry has created a critical need for resource and job lifetime prediction to achieve optimal scheduling. Traditional machine learning methods often struggle with the com...
Convolutional-neural-operator-based transfer learning for solving PDEs
arXiv:2512.17969v1 Announce Type: new
Abstract: Convolutional neural operator is a CNN-based architecture recently proposed to enforce structure-preserving continuous-discrete equivalence and enable the genuine, alias-free learning of solution operators of PDEs. This neural operator was demonstrate...
Latent Action World Models for Control with Unlabeled Trajectories
arXiv:2512.10016v1 Announce Type: new
Abstract: Inspired by how humans combine direct interaction with action-free experience (e.g., videos), we study world models that learn from heterogeneous data. Standard world models typically rely on action-conditioned trajectories, which limits effectiveness...
Cluster-Dags as Powerful Background Knowledge For Causal Discovery
arXiv:2512.10032v1 Announce Type: new
Abstract: Finding cause-effect relationships is of key importance in science. Causal discovery aims to recover a graph from data that succinctly describes these cause-effect relationships. However, current methods face several challenges, especially when dealin...