The tension between deduction and induction is perhaps the most fundamental issue in
areas such as philosophy, cognition and artificial intelligence (AI). The deduction camp …

Probabilistic programming languages (PPLs) are an expressive means of representing and
reasoning about probabilistic models. The computational challenge of probabilistic …

Bayesian neural networks (BNNs) provide a formalism to quantify and calibrate uncertainty
in deep learning. Current inference approaches for BNNs often resort to few-sample …

$ k $-subset sampling is ubiquitous in machine learning, enabling regularization and
interpretability through sparsity. The challenge lies in rendering $ k $-subset sampling …

High-quality labels are often very scarce, whereas unlabeled data with inferred weak labels
occurs more naturally. In many cases, these weak labels dictate the frequency of each …

The development of efficient exact and approximate algorithms for probabilistic inference is
a long-standing goal of artificial intelligence research. Whereas substantial progress has …

Exact inference in probabilistic graphical models is particularly challenging in the presence
of relational and other deterministic constraints. For discrete domains, weighted model …

Weighted model integration (WMI) is an appealing framework for probabilistic inference: it
allows for expressing the complex dependencies in real-world problems, where variables …

Weighted model integration (WMI) is a framework to perform advanced probabilistic
inference on hybrid domains, ie, on distributions over mixed continuous-discrete random …

Real world decision making problems often involve both discrete and continuous variables
and require a combination of probabilistic and deterministic knowledge. Stimulated by …