Bayesian Learning
The Implicit Delta Method
Epistemic uncertainty quantification is a crucial part of drawing credible conclusions from predictive models, whether concerned about the prediction at a given point or any downstream evaluation that uses the model as input.
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning
Based on the form of causal knowledge, we divide the related works into two categories, i.e.,
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging
Examples include M/EEG inverse problems, neural encoding models for task-based fMRI analyses, and climate science.
Sampling with Trustworthy Constraints: A Variational Gradient Framework
Efficient approximation and sampling methods of intractable distributions plays a key role in probabilistic machine learning, especially Bayesian inference.
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay Joao Marques-Silva
In contrast, we show that the computation of one PI-explanation for an NBC can be achieved in log-linear time, and that the same result also applies to the more general class of linear classifiers. Furthermore, we show that the enumeration of PI-explanations can be obtained with polynomial delay.
FasterRisk: Fast and Accurate Interpretable Risk Scores
Recent work used mathematical programming, which is computationally slow.