From the 1 of 10 linked papers with an AI index.
10 papers
Advancing Optimal Subset Oracle via Learning Relaxation of Neural Set Functions
Yongquan Shi, Zijing Ou, Shiping Wang +1
The paper proposes a continuous relaxation of neural set functions that replaces Monte‑Carlo gradient estimation in optimal subset oracle training with a learned surrogate objectiv…
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…
Diffusion Alignment Beyond KL: Variance Minimisation as Effective Policy Optimiser
Zijing Ou, Jacob Si, Junyi Zhu +4
Diffusion alignment adapts pretrained diffusion models to sample from reward-tilted distributions along the denoising trajectory. This process naturally admits a Sequential Monte C…
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 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…
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…