Bayesian Inference
Bayesian Learning of Sum-Product Networks
Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani
Neural Information Processing Systems http://nips.cc/
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions Rui Y ang
These Q values are often objectives that offline algorithms aim to approximate.
Distributed Weight Consolidation: A Brain Segmentation Case Study
Patrick McClure, Charles Y. Zheng, Jakub Kaczmarzyk, John Rogers-Lee, Satra Ghosh, Dylan Nielson, Peter A. Bandettini, Francisco Pereira
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/
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net
The lasso and elastic net linear regression models impose a double-exponential prior distribution on the model parameters to achieve regression shrinkage and variable selection, allowing the inference of robust models from large data sets.
fe04e05fbe48920b8ba90bea2ddfe60b-Supplemental.pdf
We study whether and how can we model a joint distributionp(x,z)using two conditional modelsp(x|z) and q(z|x) that form a cycle.
On the Generative Utility of Cyclic Conditionals Chang Liu 1, Haoyue T ang 2, Tao Qin
Work done during an internship at Microsoft Research Asia.