1 code implementation • 25 May 2023 • Vy Vo, Trung Le, Long-Tung Vuong, He Zhao, Edwin Bonilla, Dinh Phung
Estimating the parameters of a probabilistic directed graphical model from incomplete data remains a long-standing challenge.
1 code implementation • 20 Feb 2023 • He Zhao, Ke Sun, Amir Dezfouli, Edwin Bonilla
The key to missing value imputation is to capture the data distribution with incomplete samples and impute the missing values accordingly.
1 code implementation • 27 Sep 2022 • Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin Bonilla, Gholamreza Haffari, Dinh Phung
Interpretable machine learning seeks to understand the reasoning process of complex black-box systems that are long notorious for lack of explainability.
no code implementations • ICML 2018 • Amir Dezfouli, Edwin Bonilla, Richard Nock
Traditional methods for the discovery of latent network structures are limited in two ways: they either assume that all the signal comes from the network (i. e. there is no source of signal outside the network) or they place constraints on the network parameters to ensure model or algorithmic stability.
1 code implementation • 24 May 2018 • Virginia Aglietti, Theodoros Damoulas, Edwin Bonilla
We generalize the log Gaussian Cox process (LGCP) framework to model multiple correlated point data jointly.