Bayesian Learning
Generative Neural Machine Translation
Neural Information Processing Systems http://nips.cc/
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Xun Zheng, Bryon Aragam, Pradeep K. Ravikumar, Eric P. Xing
Neural Information Processing Systems http://nips.cc/
Direct Estimation of Differences in Causal Graphs
Yuhao Wang, Chandler Squires, Anastasiya Belyaeva, Caroline Uhler
However, the undirected setting is often insufficient: only a causal (i.e., directed) network provides
Bayesian Model-Agnostic Meta-Learning
Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn
A robust meta-learning algorithm therefore mustbe able to systematically deal with such uncertainty in order to be applicable to critical problemssuch as healthcare and self-driving cars.
Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues
Soumendu Sundar Mukherjee, Purnamrita Sarkar, Y. X. Rachel Wang, Bowei Yan
Neural Information Processing Systems http://nips.cc/
Towards Hardware-AwareTractable LearningofProbabilisticModels
Smart portable applications increasingly rely on edge computing due to privacy andlatencyconcerns.
Constraint-based Causal Structure Learning with Consistent Separating Sets
This paper concerns, more specifically, the inconsistency of separating sets used to remove dispensable edges, iteratively, based on conditional independence tests.
