Weitong Zhang

Weitong Zhang

Ph.D. Candidate in Computer Science, University of California, Los Angeles

Publications

You can also find my recent publications on my Google Scholar profile.

* means Equal Contirbution.

  1. Zhao, L., Deng, Y., Zhang, W., & Gu, Q. (2024). Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance. ArXiv Preprint ArXiv:2402.08680.
  2. Huang, Z., Hwang, J., Zhang, J., Baik, J., Zhang, W., Wodarz, D., Sun, Y., Gu, Q., & Wang, W. (2023). Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems. The Symbiosis of Deep Learning and Differential Equations III.
  3. Zhang, W.*, He, J.*, Zhou, D., Zhang, A., & Gu, Q. (2023). Provably Efficient Representation Learning in Low-rank Markov Decision Processes. UAI.
  4. Deng, Y., Zhang, W., Chen, Z., & Gu, Q. (2023). Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves. ArXiv Preprint ArXiv:2311.04205.
  5. Zhang, W., Wang, X., Nie, W., Eaton, J., Rees, B., & Gu, Q. (2023). MoleculeGPT: Instruction Following Large Language Models for Molecular Property Prediction. NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development.
  6. Sheng, H., Sun, J., Hoar, B., Zhang, W., Xiang, D., Tang, T., Hazra, A., & others. (2023). Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation. ChemRxiv Preprint.
  7. Lopez, V., Cramer, E. Y., Pagano, R., Drake, J. M., O’Dea, E. B., Linas, B. P., Ayer, T., Xiao, J., Adee, M., Chhatwal, J., & others. (2023). Challenges of COVID-19 Case Forecasting in the US, 2020-2021. MedRxiv, 2023–2005.
  8. Lopez, V., Cramer, E. Y., Pagano, R., Drake, J. M., O’Dea, E. B., Linas, B. P., Ayer, T., Xiao, J., Adee, M., Chhatwal, J., & others. (2023). Challenges of COVID-19 Case Forecasting in the US, 2020-2021. MedRxiv, 2023–2005.
  9. Ji, K., Zhao, Q., He, J., Zhang, W., & Gu, Q. (2023). Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs. The Twelfth International Conference on Learning Representations.
  10. Zhang, J., Zhang, W., & Gu, Q. (2023). Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs. International Conference on Machine Learning.
  11. Zhang, W., He, J., Fan, Z., & Gu, Q. (2023). On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits. International Conference on Machine Learning.
  12. Shea, K., Borchering, R. K., Probert, W. J. M., Howerton, E., Bogich, T. L., Li, S.-L., van Panhuis, W. G., Viboud, C., Aguás, R., Belov, A. A., & others. (2023). Multiple models for outbreak decision support in the face of uncertainty. Proceedings of the National Academy of Sciences, 120(18), e2207537120.
  13. Zhang, W.*, Wang, X.*, Smith, J., Eaton, J., Rees, B., & Gu, Q. (2023). DiffMol: 3D Structured Molecule Generation with Discrete Denoising Diffusion Probabilistic Models. ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling.
  14. Cramer, E. Y., Ray, E. L., Lopez, V. K., Bracher, J., Brennen, A., Castro Rivadeneira, A. J., Gerding, A., Gneiting, T., House, K. H., Huang, Y., & others. (2022). Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proceedings of the National Academy of Sciences, 119(15), e2113561119.
  15. Hoar, B. B., Zhang, W., Xu, S., Deeba, R., Costentin, C., Gu, Q., & Liu, C. (2022). Electrochemical mechanistic analysis from cyclic voltammograms based on deep learning. ACS Measurement Science Au.
  16. Jia, Y., Zhang, W., Zhou, D., Gu, Q., & Wang, H. (2021). Learning neural contextual bandits through perturbed rewards. International Conference on Learning Representations.
  17. Zhang, W., Zhou, D., & Gu, Q. (2021). Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. Advances in Neural Information Processing Systems.
  18. Zou, D., Wang, L., Xu, P., Chen, J., Zhang, W., & Gu, Q. (2020). Epidemic model guided machine learning for COVID-19 forecasts in the United States. MedRxiv.
  19. Ray, E. L., Wattanachit, N., Niemi, J., Kanji, A. H., House, K., Cramer, E. Y., Bracher, J., Zheng, A., Yamana, T. K., Xiong, X., & others. (2020). Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the us. MedRXiv.
  20. Zhang, W., Zhou, D., Li, L., & Gu, Q. (2020). Neural Thompson Sampling. International Conference on Learning Representations.
  21. Wu, Y. F., Zhang, W., Xu, P., & Gu, Q. (2020). A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems (Vol. 33, pp. 17617–17628). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2020/file/cc9b3c69b56df284846bf2432f1cba90-Paper.pdf
  22. Liu, S., Zhang, W., Wu, X., Feng, S., Pei, X., & Yao, D. (2018). A simulation system and speed guidance algorithms for intersection traffic control using connected vehicle technology. Tsinghua Science and Technology, 24(2), 160–170.