Energy
Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model
It is crucial to quantify the uncertainty of these dynamics models when using them for planning.
Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models
Modularity is a critical design principle for both software systems and artificial intelligence (AI).
PowerGraph: A power grid benchmark dataset for graph neural networks
Therefore, developing grid analysis algorithms is important for supporting reliable operations.
Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths Lars Holdijk University of Oxford Y uanqi Du
We show that our method successful generates low energy transitions for Alanine Dipeptide as well as the larger Polyproline and Chignolin proteins.
Bridging Geometric States via Geometric Diffusion Bridge Shengjie Luo
In this work, we introduce the Geometric Diffusion Bridge (GDB), a novel generative modeling framework that accurately bridges initial and target geometric states.
Causal Inference in the Closed-Loop: Marginal Structural Models for Sequential Excursion Effects Alexander W. Levis
To fill this gap, we introduce a nonparametric causal inference framework for analyzing "closed-loop" designs, which use
