9 citations · 22 across the 6 of their papers we have counts for
7 papers
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…
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation
Zhizhou Ren, Anji Liu, Yitao Liang +2
Learning new task-specific skills from a few trials is a fundamental challenge for artificial intelligence. Meta reinforcement learning (meta-RL) tackles this problem by learning t…
Tractable Regularization of Probabilistic Circuits
Anji Liu, Guy Van den Broeck
Probabilistic Circuits (PCs) are a promising avenue for probabilistic modeling. They combine advantages of probabilistic graphical models (PGMs) with those of neural networks (NNs)…
A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries
Antonio Vergari, YooJung Choi, Anji Liu +2
Circuit representations are becoming the lingua franca to express and reason about tractable generative and discriminative models. In this paper, we show how complex inference scen…
On Effective Parallelization of Monte Carlo Tree Search
Anji Liu, Yitao Liang, Ji Liu +2
Despite its groundbreaking success in Go and computer games, Monte Carlo Tree Search (MCTS) is computationally expensive as it requires a substantial number of rollouts to construc…
Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration
Anji Liu, Yitao Liang, Guy Van den Broeck
Off-policy reinforcement learning (RL) is concerned with learning a rewarding policy by executing another policy that gathers samples of experience. While the former policy (i.e. t…