Bayesian Learning
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.
Thompson Sampling with Information Relaxation Penalties Seungki Min Columbia Business School Costis Maglaras Columbia Business School Ciamac C. Moallemi Columbia Business School
We consider a finite-horizon multi-armed bandit (MAB) problem in a Bayesian setting, for which we propose an information relaxation sampling framework.
Pseudo-Extended Markov Chain Monte Carlo
In this paper, we introduce the pseudo-extended MCMC method as a simple approach for improving the mixing of the MCMC sampler for multi-modal posterior distributions.
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen
Neural Information Processing Systems http://nips.cc/
Structure Learning with Side Information: Sample Complexity
Neural Information Processing Systems http://nips.cc/
Amortized Bethe Free Energy Minimization for Learning MRFs
We propose to learn deep undirected graphical models (i.e., MRFs) with a non-ELBO objective for which we can calculate exact gradients.
The Functional Neural Process
Christos Louizos, Xiahan Shi, Klamer Schutte, Max Welling
Neural Information Processing Systems http://nips.cc/
Identifying Causal Effects via Context-specific Independence Relations
Santtu Tikka, Antti Hyttinen, Juha Karvanen
Neural Information Processing Systems http://nips.cc/
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Dominik Linzner, Michael Schmidt, Heinz Koeppl
The CIMs are the generators of the dynamics of a CTBN.
Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians
Axel Brando, Jose A. Rodriguez, Jordi Vitria, Alberto Rubio Muñoz
On the other hand, another source of uncertainty is "inevitable variability in the response variable", i.e. when the variable to predict exhibits randomness,