TY - CHAP

T1 - Credal Sum-Product Networks

AU - Maua, Denis Deratani

AU - Cozman, Fabio Gagli

AU - Conaty, Diarmaid

AU - de Campos, Cassio P.

PY - 2017

Y1 - 2017

N2 - Sum-product networks are a relatively new and increasingly popular class of (precise) probabilistic graphical models that allow for marginal inference with polynomial effort. As with other probabilistic models, sum-product networks are often learned from data and used to perform classification. Hence, their results are prone to be unreliable and overconfident. In this work, we develop credal sum-product networks, an imprecise extension of sum-product networks. We present algorithms and complexity results for common inference tasks. We apply our algorithms on realistic classification task using images of digits and show that credal sum-product networks obtained by a perturbation of the parameters of learned sum-product networks are able to distinguish between reliable and unreliable classifications with high accuracy.

AB - Sum-product networks are a relatively new and increasingly popular class of (precise) probabilistic graphical models that allow for marginal inference with polynomial effort. As with other probabilistic models, sum-product networks are often learned from data and used to perform classification. Hence, their results are prone to be unreliable and overconfident. In this work, we develop credal sum-product networks, an imprecise extension of sum-product networks. We present algorithms and complexity results for common inference tasks. We apply our algorithms on realistic classification task using images of digits and show that credal sum-product networks obtained by a perturbation of the parameters of learned sum-product networks are able to distinguish between reliable and unreliable classifications with high accuracy.

KW - Sum-product networks

KW - tractable probabilistic models

KW - credal classification

M3 - Chapter

T3 - Proceedings of Machine Learning Research

SP - 205

EP - 216

BT - ISIPTA'17: Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications

ER -