NeurIPS 2016poster26 citations
Solving Marginal MAP Problems with NP Oracles and Parity Constraints
Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman
Abstract
Arising from many applications at the intersection of decision-making and machine learning, Marginal Maximum A Posteriori (Marginal MAP) problems unify the two main classes of inference, namely maximization (optimization) and marginal inference (counting), and are believed to have higher complexity than both of them. We propose XOR
BibTeX
@inproceedings{NIPS2016_a532400e,
author = {Xue, Yexiang and Li, Zhiyuan and Ermon, Stefano and Gomes, Carla P and Selman, Bart},
booktitle = {Advances in Neural Information Processing Systems},
editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Solving Marginal MAP Problems with NP Oracles and Parity Constraints},
url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/a532400ed62e772b9dc0b86f46e583ff-Paper.pdf},
volume = {29},
year = {2016}
}