@inproceedings{grosse2021probabilistic,
author = {Grosse, Julia and Zhang, Cheng and Hennig, Philipp},
title = {Probabilistic DAG Search},
booktitle = {Conference on Uncertainty in Artificial Intelligence (UAI) 2021},
year = {2021},
month = {July},
abstract = {Exciting contemporary machine learning problems have recently been phrased in the classic formalism of tree search — most famously, the game of Go. Interestingly, the state-space underlying these sequential decision-making problems often posses a more general latent structure than can be captured by a tree. In this work, we develop a probabilistic framework to exploit a search space’s latent structure and thereby share information across the search tree. The method is based on a combination of approximate inference in jointly Gaussian models for the explored part of the problem, and an abstraction for the unexplored part that imposes a reduction of complexity ad hoc. We empirically find our algorithm to compare favorably to existing non-probabilistic alternatives in Tic-Tac-Toe and a feature selection application.},
url = {http://approjects.co.za/?big=en-us/research/publication/probabilistic-dag-search-2/},
}