Bayesian Learning
QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
Causal discovery is essential for understanding relationships among variables of interest in many scientific domains. In this paper, we focus on permutation-based methods for learning causal graphs in Linear Gaussian Acyclic Models (LiGAMs), where the permutation encodes a causal ordering of the variables. Existing methods in this setting do not scale due to their high computational complexity.
Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations
Uncertainty estimation aims to evaluate the confidence of a trained deep neural network.
Discrete Object Generation with Reversible Inductive Construction
Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams
Neural Information Processing Systems http://nips.cc/
Random deep neural networks are biased towards simple functions
Giacomo De Palma, Bobak Kiani, Seth Lloyd
Neural Information Processing Systems http://nips.cc/
Idiographic Personality Gaussian Process for Psychological Assessment
However, existing frameworks face several interrelated limitations.
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
Rui Luo, Jianhong Wang, Yaodong Yang, Jun WANG, Zhanxing Zhu
Neural Information Processing Systems http://nips.cc/