Zhendong Wang

Austin, Texas, US

I am a fourth year PhD student in the department of Statistics & Data Science at the University of Texas at Austin. I am supervised by professor Mingyuan Zhou. Before coming to UT, I completed my Master degree at Columbia University in Data Science major. I received my Bachelor degree in Civil Engineering from Tongji University in China, during which I went to the University of California, Berkeley for one year exchange study.

Research Interests: I have broad research interests in statistical machine learning. Specifically, my research interests lie in:

Deep generative models, e.g. diffusion models, GANs, etc.
Reinforcement Learning, e.g. online/offline RL, imitation learning, etc.
Multimodal Large Language Models, e.g., interleaved text-image generation, preference optimization algorithms, etc.
Uncertainty Quantification, e.g. conformal predictions, etc.

I am set to graduate in December 2024. Please don’t hesitate to get in touch if you’re interested in my research or have potential opportunities to discuss.

news

Mar 15, 2024	I will join NVIDIA Deep Imagination Research group led by Ming-Yu Liu for 2024 summer internship.
Jan 1, 2024	Our new paper Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Prompts is now public on ArXiv and the code has been publicly released on Github. Continue the Part-time Internship with Microsoft GenAI team for Fall 2023 and Spring 2024.
Jan 1, 2024	Our paper In-Context Learning Unlocked for Diffusion Models has been accepted by NeurIPS 2023 and the code has been publicly released on Github with diffusers supported. Our paper Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models has been accepted by NeurIPS 2023 and the code has been publicly released on Github.
May 1, 2023	Our new paper In-Context Learning Unlocked for Diffusion Models was public on arXiv and the code was publicly released on Github. Our new paper Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models was public on arXiv and the code will be released soon. I am joining Microsoft Azure AI team for 2023 summer internship.
Jan 30, 2023	Our new paper Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning was accepted by ICLR 2023 and the code was publicly released on Github. Our new paper Diffusion-GAN: Training GANs with Diffusion was accepted by ICLR 2023 and the code was publicly released on Github. Our new paper Probabilistic Conformal Prediction Using Conditional Random Samples was accepted by AISTATS 2023 and the code was publicly released on Github.

selected publications

Preprint

Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Prompts

Yin, Yueqin, Wang, Zhendong, Gu, Yi, Huang, Hai, Chen, Weizhu, and Zhou, Mingyuan

arXiv preprint arXiv:2402.10958 2024

Bib PDF Code

@article{yin2024relative,
  title = {Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Prompts},
  author = {Yin, Yueqin and Wang, Zhendong and Gu, Yi and Huang, Hai and Chen, Weizhu and Zhou, Mingyuan},
  journal = {arXiv preprint arXiv:2402.10958},
  year = {2024},
}

NeurIPS 2023

In-Context Learning Unlocked for Diffusion Models

Wang, Zhendong, Jiang, Yifan, Lu, Yadong, Shen, Yelong, He, Pengcheng, Chen, Weizhu, Wang, Zhangyang, and Zhou, Mingyuan

Advances in Neural Information Processing Systems 2023

Bib PDF Code

@article{wang2023context,
  title = {In-Context Learning Unlocked for Diffusion Models},
  author = {Wang, Zhendong and Jiang, Yifan and Lu, Yadong and Shen, Yelong and He, Pengcheng and Chen, Weizhu and Wang, Zhangyang and Zhou, Mingyuan},
  journal = {Advances in Neural Information Processing Systems},
  year = {2023},
}

NeurIPS 2023

Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models

Wang, Zhendong, Jiang, Yifan, Zheng, Huangjie, Wang, Peihao, He, Pengcheng, Wang, Zhangyang, Chen, Weizhu, and Zhou, Mingyuan

Advances in Neural Information Processing Systems 2023

Bib PDF Code

@article{wang2023patch,
  title = {Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models},
  author = {Wang, Zhendong and Jiang, Yifan and Zheng, Huangjie and Wang, Peihao and He, Pengcheng and Wang, Zhangyang and Chen, Weizhu and Zhou, Mingyuan},
  journal = {Advances in Neural Information Processing Systems},
  year = {2023},
}

ICLR 2023

Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning

Wang, Zhendong, Hunt, J Jonathan, and Zhou, Mingyuan

International Conference on Learning Representations 2023

Bib PDF Code

@article{wang2023diffusioninrl,
  title = {Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning},
  author = {Wang, Zhendong and Hunt, J Jonathan and Zhou, Mingyuan},
  journal = {International Conference on Learning Representations},
  year = {2023},
}

ICLR 2023

Diffusion-GAN: Training GANs with Diffusion

Wang, Zhendong, Zheng, Huangjie, He, Pengcheng, Chen, Weizhu, and Zhou, Mingyuan

International Conference on Learning Representations 2023

Bib PDF Code

@article{wang2023diffusiongan,
  title = {Diffusion-GAN: Training GANs with Diffusion},
  author = {Wang, Zhendong and Zheng, Huangjie and He, Pengcheng and Chen, Weizhu and Zhou, Mingyuan},
  journal = {International Conference on Learning Representations},
  year = {2023},
}

AISTATS 2023

Probabilistic Conformal Prediction Using Conditional Random Samples

Wang, Zhendong*, Gao, Ruijiang*, Yin, Mingzhang*, Zhou, Mingyuan, and Blei, David M

International Conference on Artificial Intelligence and Statistics 2023 2023

Bib PDF Code

@article{wang2023pcp,
  title = {Probabilistic Conformal Prediction Using Conditional Random Samples},
  author = {Wang, Zhendong* and Gao, Ruijiang* and Yin, Mingzhang* and Zhou, Mingyuan and Blei, David M},
  journal = {International Conference on Artificial Intelligence and Statistics 2023},
  year = {2023},
}

ICML 2020

Thompson sampling via local uncertainty

Wang, Zhendong, and Zhou, Mingyuan

In International Conference on Machine Learning 2020

Bib PDF Code

@inproceedings{wang2020thompson,
  title = {Thompson sampling via local uncertainty},
  author = {Wang, Zhendong and Zhou, Mingyuan},
  booktitle = {International Conference on Machine Learning},
  pages = {10115--10125},
  year = {2020},
  organization = {PMLR},
}