24 citations · 44 across the 8 of their papers we have counts for
6 papers · 1 filter
Group Fairness by Probabilistic Modeling with Latent Fair Decisions
YooJung Choi, Meihua Dang, Guy Van den Broeck
Machine learning systems are increasingly being used to make impactful decisions such as loan applications and criminal justice risk assessments, and as such, ensuring fairness of…
Handling Missing Data in Decision Trees: A Probabilistic Approach
Pasha Khosravi, Antonio Vergari, YooJung Choi +2
Decision trees are a popular family of models due to their attractive properties such as interpretability and ability to handle heterogeneous data. Concurrently, missing data is a…
On Tractable Computation of Expected Predictions
Pasha Khosravi, YooJung Choi, Yitao Liang +2
Computing expected predictions of discriminative models is a fundamental task in machine learning that appears in many interesting applications such as fairness, handling missing v…
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
YooJung Choi, Golnoosh Farnadi, Behrouz Babaki +1
As machine learning is increasingly used to make real-world decisions, recent research efforts aim to define and ensure fairness in algorithmic decision making. Existing methods of…
What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features
Pasha Khosravi, Yitao Liang, YooJung Choi +1
While discriminative classifiers often yield strong predictive performance, missing feature values at prediction time can still be a challenge. Classifiers may not behave as expect…
On Robust Trimming of Bayesian Network Classifiers
YooJung Choi, Guy Van den Broeck
This paper considers the problem of removing costly features from a Bayesian network classifier. We want the classifier to be robust to these changes, and maintain its classificati…