Bayesian Learning
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning
Bregman divergence for greater adaptability in personalized scenarios.
Mutually Regressive Point Processes
Ifigeneia Apostolopoulou, Scott Linderman, Kyle Miller, Artur Dubrawski
Neural Information Processing Systems http://nips.cc/
Streaming Bayesian Inference for Crowdsourced Classification
Edoardo Manino, Long Tran-Thanh, Nicholas Jennings
Neural Information Processing Systems http://nips.cc/
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/
Leveraging the Exact Likelihood of Deep Latent Variable Models
Pierre-Alexandre Mattei, Jes Frellsen
Neural Information Processing Systems http://nips.cc/
Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai, Srinivas Aluru
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.