24 citations · 147 across the 75 of their papers we have counts for
11 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…
On the Tractability of SHAP Explanations
Guy Van den Broeck, Anton Lykov, Maximilian Schleich +1
SHAP explanations are a popular feature-attribution mechanism for explainable AI. They use game-theoretic notions to measure the influence of individual features on the prediction…
Strudel: Learning Structured-Decomposable Probabilistic Circuits
Meihua Dang, Antonio Vergari, Guy Van den Broeck
Probabilistic circuits (PCs) represent a probability distribution as a computational graph. Enforcing structural properties on these graphs guarantees that several inference scenar…
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 the Relationship Between Probabilistic Circuits and Determinantal Point Processes
Honghua Zhang, Steven Holtzen, Guy Van den Broeck
Scaling probabilistic models to large realistic problems and datasets is a key challenge in machine learning. Central to this effort is the development of tractable probabilistic m…
Counterexample-Guided Learning of Monotonic Neural Networks
Aishwarya Sivaraman, Golnoosh Farnadi, Todd Millstein +1
The widespread adoption of deep learning is often attributed to its automatic feature construction with minimal inductive bias. However, in many real-world tasks, the learned funct…