7 citations · 19 across the 21 of their papers we have counts for
6 papers · 1 filter
Neural Mutual Information Estimation with Vector Copulas
Yanzhi Chen, Zijing Ou, Adrian Weller +1
Estimating mutual information (MI) is a fundamental task in data science and machine learning. Existing estimators mainly rely on either highly flexible models (e.g., neural networ…
Memory-Driven Self-Improvement for Decision Making with Large Language Models
Xue Yan, Zijing Ou, Mengyue Yang +4
Large language models (LLMs) have emerged as effective action policies for sequential decision-making (SDM) tasks due to their extensive prior knowledge. However, this broad yet ge…
Inference-Time Scaling of Discrete Diffusion Models via Importance Weighting and Optimal Proposal Design
Zijing Ou, Chinmay Pani, Yingzhen Li
Discrete diffusion models have become highly effective across various domains. However, real-world applications often require the generative process to adhere to certain constraint…
Discrete Neural Flow Samplers with Locally Equivariant Transformer
Zijing Ou, Ruixiang Zhang, Yingzhen Li
Sampling from unnormalised discrete distributions is a fundamental problem across various domains. While Markov chain Monte Carlo offers a principled approach, it often suffers fro…
TabRep: Training Tabular Diffusion Models with a Simple and Effective Continuous Representation
Jacob Si, Zijing Ou, Mike Qu +2
Diffusion models have been the predominant generative model for tabular data generation. However, they face the conundrum of modeling under a separate versus a unified data represe…
Neural Flow Samplers with Shortcut Models
Wuhao Chen, Zijing Ou, Yingzhen Li
Sampling from unnormalized densities presents a fundamental challenge with wide-ranging applications, from posterior inference to molecular dynamics simulations. Continuous flow-ba…