2024-09-12-lahoud24a.md

title	abstract	openreview	software	section	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
DataSP: A Differential All-to-All Shortest Path Algorithm for Learning Costs and Predicting Paths with Context	Learning latent costs of transitions on graphs from trajectories demonstrations under various contextual features is challenging but useful for path planning. Yet, existing methods either oversimplify cost assumptions or scale poorly with the number of observed trajectories. This paper introduces DataSP, a differentiable all-to-all shortest path algorithm to facilitate learning latent costs from trajectories. It allows to learn from a large number of trajectories in each learning step without additional computation. Complex latent cost functions from contextual features can be represented in the algorithm through a neural network approximation. We further propose a method to sample paths from DataSP in order to reconstruct/mimic observed paths’ distributions. We prove that the inferred distribution follows the maximum entropy principle. We show that DataSP outperforms state-of-the-art differentiable combinatorial solver and classical machine learning approaches in predicting paths on graphs.	nbwSnQBLU3	https://github.com/AlanLahoud/dataSP	Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	lahoud24a	0	DataSP: A Differential All-to-All Shortest Path Algorithm for Learning Costs and Predicting Paths with Context	2094	2112	2094-2112	2094	false	Lahoud, Alan and Schaffernicht, Erik and Stork, Johannes	given family Alan Lahoud given family Erik Schaffernicht given family Johannes Stork	2024-09-12		Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence	244	inproceedings	date-parts 2024 9 12	https://raw.githubusercontent.com/mlresearch/v244/main/assets/lahoud24a/lahoud24a.pdf