5 papers
Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering
Sixian Wang, Zhiwei Tang, Tsung-Hui Chang
Diffusion models often exhibit inconsistent sample quality due to stochastic variations inherent in their sampling trajectories. Although training-based fine-tuning (e.g. DDPO [1])…
Inference-Time Alignment of Diffusion Models with Direct Noise Optimization
Zhiwei Tang, Jiangweizhi Peng, Jiasheng Tang +3
In this work, we focus on the alignment problem of diffusion models with a continuous reward function, which represents specific objectives for downstream tasks, such as increasing…
Accelerating Parallel Sampling of Diffusion Models
Zhiwei Tang, Jiasheng Tang, Hao Luo +2
Diffusion models have emerged as state-of-the-art generative models for image generation. However, sampling from diffusion models is usually time-consuming due to the inherent auto…
Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles
Zhiwei Tang, Dmitry Rybin, Tsung-Hui Chang
In this study, we delve into an emerging optimization challenge involving a black-box objective function that can only be gauged via a ranking oracle-a situation frequently encount…
FedLion: Faster Adaptive Federated Optimization with Fewer Communication
Zhiwei Tang, Tsung-Hui Chang
In Federated Learning (FL), a framework to train machine learning models across distributed data, well-known algorithms like FedAvg tend to have slow convergence rates, resulting i…