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Found 6 papers, 1 papers with code
Rao-Blackwellising Bayesian Causal Inference
Specifically, we decompose the problem of inferring the causal structure into (i) inferring a topological order over variables and (ii) inferring the parent sets for each variable.
Understanding the Behavior of Belief Propagation
Probabilistic graphical models are a powerful concept for modeling high-dimensional distributions.
Active Bayesian Causal Inference
In this work, we propose Active Bayesian Causal Inference (ABCI), a fully-Bayesian active learning framework for integrated causal discovery and reasoning, which jointly infers a posterior over causal models and queries of interest.
Distribution Mismatch Correction for Improved Robustness in Deep Neural Networks
The most common normalization method, batch normalization, reduces the distribution shift during training but is agnostic to changes in the input distribution during test time.
Self-Guided Belief Propagation -- A Homotopy Continuation Method
Belief propagation (BP) is a popular method for performing probabilistic inference on graphical models.
Fixed Points of Belief Propagation -- An Analysis via Polynomial Homotopy Continuation
Moreover, we show that this fixed point gives a good approximation, and the NPHC method is able to obtain this fixed point.
