activity
20182025
most citedOn Tractable Computation of Expected Predictions

24 citations · 43 across the 6 of their papers we have counts for

collaborators

11 papers

cs.CV2025

Plug-and-Play Context Feature Reuse for Efficient Masked Generation

Xuejie Liu, Anji Liu, Guy Van den Broeck +1

Masked generative models (MGMs) have emerged as a powerful framework for image synthesis, combining parallel decoding with strong bidirectional context modeling. However, generatin…

cs.CL2025

Tractable Transformers for Flexible Conditional Generation

Anji Liu, Xuejie Liu, Dayuan Zhao +3

Non-autoregressive (NAR) generative models are valuable because they can handle diverse conditional generation tasks in a more principled way than their autoregressive (AR) counter…

cs.LG2021

Towards an Interpretable Latent Space in Structured Models for Video Prediction

Rushil Gupta, Vishal Sharma, Yash Jain +3

We focus on the task of future frame prediction in video governed by underlying physical dynamics. We work with models which are object-centric, i.e., explicitly work with object r…

cs.LG202015 cited

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…

cs.LG20202 cited

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…

cs.LG20201 cited

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…