2 citations · 3 across the 8 of their papers we have counts for
8 papers
Discrete Diffusion Models: A Unified Framework from Tokenization to Generation
Ye Yuan, Weien Li, Rui Song +20
Discrete denoising diffusion models (DDMs) have recently emerged as a compelling alternative to autoregressive (AR) modeling for discrete data, offering parallel generation and ite…
Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension
George Andriopoulos, Zixuan Dong, Bimarsha Adhikari +1
Neural multivariate regression underpins a wide range of domains, including control, robotics, and finance, yet the geometry of its learned representations remains poorly character…
Should We Ever Prefer Decision Transformer for Offline Reinforcement Learning?
Yumi Omori, Zixuan Dong, Keith Ross
In recent years, extensive work has explored the application of the Transformer architecture to reinforcement learning problems. Among these, Decision Transformer (DT) has gained p…
Minimal Ingredients for Reward Assignment from Expert Demonstrations
Zixuan Dong, Yumi Omori, Keith Ross
Reward assignment from scarce demonstrations is a key challenge in both offline and online imitation learning. A common and intuitive strategy assigns rewards according to how clos…
The Prevalence of Neural Collapse in Neural Multivariate Regression
George Andriopoulos, Zixuan Dong, Li Guo +2
Recently it has been observed that neural networks exhibit Neural Collapse (NC) during the final stage of training for the classification problem. We empirically show that multivar…
Cross Entropy versus Label Smoothing: A Neural Collapse Perspective
Li Guo, George Andriopoulos, Zifan Zhao +3
Label smoothing loss is a widely adopted technique to mitigate overfitting in deep neural networks. This paper studies label smoothing from the perspective of Neural Collapse (NC),…
Pre-training with Synthetic Data Helps Offline Reinforcement Learning
Zecheng Wang, Che Wang, Zixuan Dong +1
Recently, it has been shown that for offline deep reinforcement learning (DRL), pre-training Decision Transformer with a large language corpus can improve downstream performance (R…
On the Convergence of Monte Carlo UCB for Random-Length Episodic MDPs
Zixuan Dong, Che Wang, Keith Ross
In reinforcement learning, Monte Carlo algorithms update the Q function by averaging the episodic returns. In the Monte Carlo UCB (MC-UCB) algorithm, the action taken in each state…