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Found 10 papers, 6 papers with code
Certifying Fairness of Probabilistic Circuits
In this paper, we propose an algorithm to search for discrimination patterns in a general class of probabilistic models, namely probabilistic circuits.
Solving Marginal MAP Exactly by Probabilistic Circuit Transformations
Probabilistic circuits (PCs) are a class of tractable probabilistic models that allow efficient, often linear-time, inference of queries such as marginals and most probable explanations (MPE).
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference
Circuit representations are becoming the lingua franca to express and reason about tractable generative and discriminative models.
A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries
Circuit representations are becoming the lingua franca to express and reason about tractable generative and discriminative models.
Group Fairness by Probabilistic Modeling with Latent Fair Decisions
This is often challenging as the labels in the data are biased.
Handling Missing Data in Decision Trees: A Probabilistic Approach
As such, handling missing data in decision trees is a well studied problem.
On Tractable Computation of Expected Predictions
In this paper, we identify a pair of generative and discriminative models that enables tractable computation of expectations, as well as moments of any order, of the latter with respect to the former in case of regression.
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
As machine learning is increasingly used to make real-world decisions, recent research efforts aim to define and ensure fairness in algorithmic decision making.
What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features
While discriminative classifiers often yield strong predictive performance, missing feature values at prediction time can still be a challenge.
On Robust Trimming of Bayesian Network Classifiers
To this end, we propose a closeness metric between Bayesian classifiers, called the expected classification agreement (ECA).
