Directed Networks
Predictive Approximate Bayesian Computation via Saddle Points
Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He
Neural Information Processing Systems http://nips.cc/
Computationally and statistically efficient learning of causal Bayes nets using path queries
Neural Information Processing Systems http://nips.cc/
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms
Shahana Ibrahim, Xiao Fu, Nikolaos Kargas, Kejun Huang
Neural Information Processing Systems http://nips.cc/
Data-dependent PAC-Bayes priors via differential privacy
Gintare Karolina Dziugaite, Daniel M. Roy
Neural Information Processing Systems http://nips.cc/
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning
Neural Information Processing Systems http://nips.cc/
Constructing Deep Neural Networks by Bayesian Network Structure Learning
Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Guy Koren, Gal Novik
We introduce a principled approach for unsupervised structure learning of deep neural networks.
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs
Marek Petrik, Reazul Hasan Russel
Neural Information Processing Systems http://nips.cc/
Implicit Reparameterization Gradients
Mikhail Figurnov, Shakir Mohamed, Andriy Mnih
Neural Information Processing Systems http://nips.cc/
9023effe3c16b0477df9b93e26d57e2c-Paper.pdf
This simple intuition makes Bayesian methods appealing approaches for transfer in RL, and many previous works have been proposed in this direction.