| Author(s): |
Conaty, Diarmaid
;
Maua, Denis D.
;
de Campos, Cassio P.
;
AUAI
Total Authors: 4
|
| Document type: | Journal article |
| Source: | 2022 25TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2022); v. N/A, p. 10-pg., 2017-01-01. |
| Abstract | |
We discuss the computational complexity of approximating maximum a posteriori inference in sum-product networks. We first show NP-hardness in trees of height two by a reduction from maximum independent set; this implies non-approximability within a sublinear factor. We show that this is a tight bound, as we can find an approximation within a linear factor in networks of height two. We then show that, in trees of height three, it is NP-hard to approximate the problem within a factor 2(f(n)) for any sublinear function f of the size of the input n. Again, this bound is tight, as we prove that the usual max-product algorithm finds (in any network) approximations within factor 2(c.n) for some constant c < 1. Last, we present a simple algorithm, and show that it provably produces solutions at least as good as, and potentially much better than, the max-product algorithm. We empirically analyze the proposed algorithm against max-product using synthetic and real-world data. (AU) | |
| FAPESP's process: | 16/01055-1 - Learning of Tractable Probabilistic Models with Application to Multilabel Classification |
| Grantee: | Denis Deratani Mauá |
| Support Opportunities: | Regular Research Grants |