7 papers
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…
Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces
Tobias Schröder, Zijing Ou, Yingzhen Li +1
Energy-based models (EBMs) offer a flexible framework for probabilistic modelling across various data domains. However, training EBMs on data in discrete or mixed state spaces pose…