Publications

Publications and Preprints

Language Models

• Embodied LLM Agents Learn to Cooperate in Organized Teams

  Xudong Guo, Kaixuan Huang, Jiale Liu, Wenhui Fan, Natalia Vélez, Qingyun Wu, Huazheng Wang, Thomas L. Griffiths, Mengdi Wang
  arXiv preprint [link]

• Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications

  Boyi Wei*, Kaixuan Huang*, Yangsibo Huang*, Tinghao Xie, Xiangyu Qi, Mengzhou Xia, Prateek Mittal, Mengdi Wang, Peter Henderson
  arXiv preprint [link] [Code]

• Visual Adversarial Examples Jailbreak Large Language Models

  Xiangyu Qi*, Kaixuan Huang*, Ashwinee Panda, Peter Henderson, Mengdi Wang, Prateek Mittal
  AAAI 2024 ( Oral ) ICML2023 Adv ML workshop. (Oral) [link] [Code]

• Scaling In-Context Demonstrations with Structured Attention

  Tianle Cai*, Kaixuan Huang*, Jason D. Lee, Mengdi Wang
  ICML 2023 Workshop on Efficient Systems for Foundation Models. [link]

Diffusion Models

• Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement

  Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, Mengdi Wang
  In Advances in Neural Information Processing Systems (NeurIPS), 2023. [link] [Code]

• Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data

  Minshuo Chen*, Kaixuan Huang*, Tuo Zhao, Mengdi Wang
  In International Conference on Machine Learning (ICML), 2023. [link]

AI for Sciences

• A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions

  Yanyi Chu*, Dan Yu*, Yupeng Li, Kaixuan Huang, Yue Shen, Le Cong, Jason Zhang, Mengdi Wang
  Nature Machine Intelligence (2024) [link]

• Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources

  Yikuan Li*, Chengsheng Mao*, Kaixuan Huang*, Hanyin Wang*, Zheng Yu*, Mengdi Wang, Yuan Luo
  arxiv preprint [link]

• Deep Reinforcement Learning for Cost-Effective Medical Diagnosis

  Zheng Yu*, Yikuan Li*, Joseph Kim*, Kaixuan Huang*, Yuan Luo, Mengdi Wang
  In International Conference on Learning Representations (ICLR), 2023. [link]

Theories on Optimization, Deep Learning, and Reinforcement Learning

• Going Beyond Linear RL: Sample Efficient Neural Function Approximation

  Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang (alphabetical)
  In Advances in Neural Information Processing Systems (NeurIPS), 2021. [link]

• Optimal Gradient-based Algorithms for Non-concave Bandit Optimization

  Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang (alphabetical)
  In Advances in Neural Information Processing Systems (NeurIPS), 2021. [link]

• A Short Note on the Relationship of Information Gain and Eluder Dimension

  Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei (alphabetical)
  ICML2021 Workshop on Reinforcement Learning Theory. [link]

• Fast Federated Learning in the Presence of Arbitrary Device Unavailability

  Xinran Gu*, Kaixuan Huang*, Jingzhao Zhang, Longbo Huang
  In Advances in Neural Information Processing Systems (NeurIPS), 2021. [link]

• Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective

  Kaixuan Huang*, Yuqing Wang*, Molei Tao, Tuo Zhao
  In Advances in Neural Information Processing Systems (NeurIPS), 2020. [link]

• On the Convergence of FedAvg on Non-IID Data

  Xiang Li*, Kaixuan Huang*, Wenhao Yang*, Shusen Wang, Zhihua Zhang
  In International Conference on Learning Representations (ICLR), 2020. (Oral Presentation) [link]