sufficient statistics
Learning Additive Exponential Family Graphical Models via $\ell_{2,1}$-norm Regularized M-Estimation
Xiaotong Yuan, Ping Li, Tong Zhang, Qingshan Liu, Guangcan Liu
We investigate a subclass of exponential family graphical models of which the sufficient statistics are defined by arbitrary additive forms. We propose two ℓ2,1norm regularized maximum likelihood estimators to learn the model parameters from i.i.d.
Differentially Private Bayesian Linear Regression
Garrett Bernstein, Daniel R. Sheldon
Neural Information Processing Systems http://nips.cc/
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Dominik Linzner, Michael Schmidt, Heinz Koeppl
Instead ofsampling andscoring allpossible structures individually,we assume the generator of the CTBN to be composed as a mixture of generators stemming from different structures.
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods
Belhal Karimi, Hoi-To Wai, Eric Moulines, Marc Lavielle
Neural Information Processing Systems http://nips.cc/
Differentially Private Bayesian Inference for Exponential Families
Garrett Bernstein, Daniel R. Sheldon
Neural Information Processing Systems http://nips.cc/
ContinualLearning
However,therobustnessand better performance of the proposed approaches result in a method, applicable to all the settings without worrying about the embedding size.