Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Feature selection is a crucial preprocessing step in data analytics and machine learning.
Classical feature selection algorithms select features based on the correlations between …

We present a constraint-based algorithm for learning causal structures from observational
time-series data, in the presence of latent confounders. We assume a discrete-time …

We present a sound and complete algorithm, called iterative causal discovery (ICD), for
recovering causal graphs in the presence of latent confounders and selection bias. ICD …

A proof-of-concept framework for identifying molecules of unknown elemental composition
and structure using experimental rotational data and probabilistic deep learning is …

Earlier studies have shown that classification accuracies of Bayesian networks (BNs)
obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the …

Earlier reports have described classification accuracies of exactly learned augmented naive
Bayes (ANB) classifiers. Those results indicate that a class variable with no parent has …

Modeling uncertainty in deep neural networks, despite recent important advances, is still an
open problem. Bayesian neural networks are a powerful solution, where the prior over …

We present CLEAR, a method for learning session-specific causal graphs, in the possible
presence of latent confounders, from attention in pre-trained attention-based recommenders …

Causal discovery from observational data is an important tool in many branches of science.
Under certain assumptions it allows scientists to explain phenomena, predict, and make …