6 citations · 13 across the 6 of their papers we have counts for
5 papers · 1 filter
Scaling Probabilistic Circuits via Monarch Matrices
Honghua Zhang, Meihua Dang, Benjie Wang +3
Probabilistic Circuits (PCs) are tractable representations of probability distributions allowing for exact and efficient computation of likelihoods and marginals. Recent advancemen…
Scaling Pareto-Efficient Decision Making Via Offline Multi-Objective RL
Baiting Zhu, Meihua Dang, Aditya Grover
The goal of multi-objective reinforcement learning (MORL) is to learn policies that simultaneously optimize multiple competing objectives. In practice, an agent's preferences over…
Sparse Probabilistic Circuits via Pruning and Growing
Meihua Dang, Anji Liu, Guy Van den Broeck
Probabilistic circuits (PCs) are a tractable representation of probability distributions allowing for exact and efficient computation of likelihoods and marginals. There has been s…
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…
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…