Author

C. Monteleoni

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Jul 2026

Learning from Local Walks on Dynamic Graphs with Bandit Feedback

This work identifies a process-agnostic structural condition, based on sliding-window mixing, that ensures the graph's intrinsic walk remains stable for both exploration and navigation and establishes sublinear expected regret.

Sourav Chakraborty, Amit Kiran Rege, C. Monteleoni et al. · arXiv.org · 0 citations

#machine learning Preprint May 2026

Emulating the Forced Response of Climate Models with Generative Machine Learning

This research demonstrates that the model, ArchesClimate -- SSP, does not simply imitate scenarios seen during training, but is actually capable of modeling the response of a climate state to diverse forcings, an important step towards reliable and rapid climate model scenario generation.

Graham Clyne, Julia Kaltenborn, Peer Nowack et al. · 0 citations