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Found 9 papers, 5 papers with code
Soft Learning Probabilistic Circuits
This paper focuses on the main algorithm for training PCs, LearnSPN, a gold standard due to its efficiency, performance, and ease of use, in particular for tabular data.
Probabilistic Circuits with Constraints via Convex Optimization
This work addresses integrating probabilistic propositional logic constraints into the distribution encoded by a probabilistic circuit (PC).
Probabilistic Integral Circuits
In contrast, probabilistic circuits (PCs) are hierarchical discrete mixtures represented as computational graphs composed of input, sum and product units.
Probabilistic Multi-Dimensional Classification
We propose a formal framework for probabilistic MDC in which learning an optimal multi-dimensional classifier can be decomposed, without loss of generality, into learning a set of (smaller) single-variable multi-class probabilistic classifiers and a directed acyclic graph.
Continuous Mixtures of Tractable Probabilistic Models
Meanwhile, tractable probabilistic models such as probabilistic circuits (PCs) can be understood as hierarchical discrete mixture models, and thus are capable of performing exact inference efficiently but often show subpar performance in comparison to continuous latent-space models.
Bayesian Kernelised Test of (In)dependence with Mixed-type Variables
A fundamental task in AI is to assess (in)dependence between mixed-type variables (text, image, sound).
Towards Robust Classification with Deep Generative Forests
Decision Trees and Random Forests are among the most widely used machine learning models, and often achieve state-of-the-art performance in tabular, domain-agnostic datasets.
Joints in Random Forests
Decision Trees (DTs) and Random Forests (RFs) are powerful discriminative learners and tools of central importance to the everyday machine learning practitioner and data scientist.
On Pruning for Score-Based Bayesian Network Structure Learning
Many algorithms for score-based Bayesian network structure learning (BNSL), in particular exact ones, take as input a collection of potentially optimal parent sets for each variable in the data.
