Yongyuan Liang

Yongyuan (Cheryl) Liang's research focuses on developing robust, versatile, and efficient intelligent agents with strong generalization capacity for AI systems.
She actively explores both theoretical frameworks and empirical findings, with specific research interests in:

Reinforcement Learning (robust RL, offline RL, IRL, RLHF)
Foundation models for policy learning (generative models, representation)
Trustworthy LLM agents, for planning and reasoning

She joins UMD CS as a PhD student, advised by Furong Huang and also works closely with Huazhe Xu.

Email / Google Scholar / Github / Twitter

2024	I'm looking for part-time/full-time internship opportunities. Feel free to reach out if you're interested in my research.
Long-term	I'm open to potential collaboration with graduate/undergraduate students. Please drop me an email if you'd like to have a (virtual) coffee chat :)
Mar' 24	Have been selected as a ICLR 2024 Financial Assistance Awardee.
Feb' 24	Have been awarded a Dean’s Fellowship.
Jan' 24	Three papers to appear in ICLR 2024, including two spotlights and one poster.

Selected Publications & Preprints

[* denotes equal contribution]

	ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization Tianying Ji, Yongyuan Liang, Yan Zeng, Yu Luo, Guowei Xu, Jiawei Guo, Ruijie Zheng, Furong Huang, Fuchun Sun, Huazhe Xu arXiv, 2024 Project Page / Paper / Code / Twitter
	PREMIER-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Shuang Ma, Hal Daumé III, Huazhe Xu, John Langford, Praveen Palanisamy, Kalyan Basu, Furong Huang arXiv, 2024 NeurIPS Workshop FMDM, 2023 Project Page / Paper / Code / Twitter
	DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization Guowei Xu, Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Zhecheng Yuan, Tianying Ji, Yu Luo, Xiaoyu Liu, Jiaxin Yuan, Pu Hua, Shuzhen Li, Yanjie Ze, Hal Daumé III, Furong Huang, Huazhe Xu ICLR, 2024 (Spotlight - Top 5%)* CORL Workshop PRL, 2023 Project Page / Paper / Code / Twitter
	Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations Yongyuan Liang, Yanchao Sun, Ruijie Zheng, Xiangyu Liu, Benjamin Eysenbach, Tuomas Sandholm, Furong Huang, Stephen Marcus McAleer ICLR, 2024 ICML Workshop AdvML-Frontiers, 2023 Project Page / Paper / Code / Twitter
	Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning Yongyuan Liang, Yanchao Sun, Ruijie Zheng, Furong Huang NeurIPS, 2022 NeurIPS Workshop SafeRL, 2021 (Spotlight Talks) Paper / Code / Slides
	Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies Xiangyu Liu, Chenghao Deng, Yanchao Sun, Yongyuan Liang, Furong Huang ICLR, 2024 (Spotlight - Top 5%) NeurIPS Workshop MASEC, 2023 Project Page / Paper / Code / Twitter
	Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL Yanchao Sun, Ruijie Zheng, Yongyuan Liang, Furong Huang ICLR, 2022 NeurIPS Workshop SafeRL, 2021 (Best Paper Award) Project Page / Paper / Code
	Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems Yanchao Sun, Ruijie Zheng, Parisa Hassanzadeh, Yongyuan Liang, Soheil Feizi, Sumitra Ganesh, Furong Huang ICLR, 2023 Paper / Code

Professional Service

Conference Reviewer: ICML(2022, 2023, 2024), NeurIPS(2021, 2022, 2023), ICLR(2021, 2022, 2023, 2024), AAAI(2020)

Workshop Program Committee: FMDM 2023 at NeurIPS

Misc

My name is a bit tricky to pronounce for some, so just call me Cheryl [ˈʃerəl].

I've been playing the violin for over 10 years and was a violinist in the university orchestra.