Bayesian Inference
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.
Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo
Oren Mangoubi, Nisheeth Vishnoi
Neural Information Processing Systems http://nips.cc/
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/
Copula-like Variational Inference
Marcel Hirt, Petros Dellaportas, Alain Durmus
Neural Information Processing Systems http://nips.cc/
Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
Notably, most real-world homophilic and heterophilic graphs are comprised of a mixture of nodes in both homophilic and heterophilic structural patterns, exhibiting a structural disparity.
The Functional Neural Process
Christos Louizos, Xiahan Shi, Klamer Schutte, Max Welling
Neural Information Processing Systems http://nips.cc/