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Hi, I'm Yibo (Jacky) Zhang


photo taken in Vancouver, Canada
I am a PhD student in the Department of Computer Science at Stanford University, where I am fortunate to be advised by Sanmi Koyejo. I am broadly interested in
  • Machine Learning: broadly in understanding and designing machine learning algorithms, especially from a fundamental perspective.
  • Optimization: non-convex optimization, combinatorial optimization, stochastic optimization, submodular optimization.
  • Algorithms: approximation algorithms, heuristic algorithms.
Due to uncovered reasons, Yibo Zhang (张益博) used an academic pseudonyms: Jacky Y. Zhang.
Education Stanford University
  • PhD Student - Department of Computer Science (present)
 University of Illinois at Urbana-Champaign
  • M.S. in Computer Science (2022)
 University of Science and Technology of China
  • B.E. in Computer Science (2019)
Publications & Preprints
(*eqaul contribution) Selected Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting [ pdf ]
Enyi Jiang*, Yibo Jacky Zhang*, Oluwasanmi Koyejo.
International Conference on Learning Representations (ICLR), 2024. Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization [ pdf ] [ poster ] [ talk ]
Xiaojun Xu*, Jacky Y. Zhang*, Evelyn Ma, Danny Son, Oluwasanmi Koyejo, Bo Li.
International Conference on Machine Learning (ICML), 2022. Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective
[ pdf ] [ poster ] [ short talk ] [ long talk ]
Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo.
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021. (Oral) Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability [ pdf ] [ poster ] [ talk ]
Kaizhao Liang*, Jacky Y. Zhang*, Boxin Wang, Zhuolin Yang, Oluwasanmi Koyejo, Bo Li
International Conference on Machine Learning (ICML), 2021. List of All Can Public Large Language Models Help Private Cross-device Federated Learning? [ pdf ]
Boxin Wang, Yibo Jacky Zhang, Yuan Cao, Bo Li, H. Brendan McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer.
Preprint. Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting [ pdf ]
Enyi Jiang*, Yibo Jacky Zhang*, Oluwasanmi Koyejo.
International Conference on Learning Representations (ICLR), 2024. Batch Active Learning from the Perspective of Sparse Approximation [ pdf ] [ poster ]
Maohao Shen*, Bowen Jiang*, Jacky Y. Zhang*, Oluwasanmi Koyejo.
NeurIPS 2022 Workshop on Human in the Loop Learning. Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization [ pdf ] [ poster ] [ talk ]
Xiaojun Xu*, Jacky Y. Zhang*, Evelyn Ma, Danny Son, Oluwasanmi Koyejo, Bo Li
International Conference on Machine Learning (ICML), 2022. Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective
[ pdf ] [ poster ] [ short talk ] [ long talk ]
Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo.
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021. (Oral) Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability [ pdf ] [ poster ] [ talk ]
Kaizhao Liang*, Jacky Y. Zhang*, Boxin Wang, Zhuolin Yang, Oluwasanmi Koyejo, Bo Li
International Conference on Machine Learning (ICML), 2021. Labeling Cost-Sensitive Batch Active Learning for Brain Tumor Segmentation
Maohao Shen, Jacky Y. Zhang, Leihao Chen, Weiman Yan, Neel Jani, Brad Sutton, Oluwasanmi Koyejo.
International Symposium on Biomedical Imaging (ISBI), 2021. Robusta: Robust AutoML for Feature Selection via Reinforcement Learning [ pdf ]
Xiaoyang Wang, Bo Li, Yibo Zhang, Bhavya Kailkhura, Klara Nahrstedt.
AAAI 2021 Workshop Towards Robust, Secure and Efficient Machine Learning. Learning Sparse Distributions using Iterative Hard Thresholding [ pdf ] [ poster ]
Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo.
Neural Information Processing Systems (NeurIPS), 2019. Maximizing Monotone DR-submodular Continuous Functions by Derivative-free Optimization [ pdf ]
Yibo Zhang, Chao Qian, Ke Tang.
Preprint: arXiv 1810.06833, 2018. On Multiset Selection with Size Constraints [ pdf ]
Chao Qian, Yibo Zhang, Ke Tang, Xin Yao.
AAAI Conference on Artificial Intelligence (AAAI), 2018.
Selected Honors
  • 2018 Guo Moruo Scholarship (Highest Graduation Distinction)
  • 2017 Outstanding Student Scholarship Golden Award
  • 2016 Member of Talent Program in Computer Science and Technology, USTC
  • 2016 Outstanding Student Scholarship Golden Award
Contact yiboz@stanford.edu
Miscellaneous I love photography, music and outdoor activities in general.
Photographer of Today: Matt Stuart Music of Today: A Love Song - EGO WRAPPIN' I will probably update these tomorrow almost surely with high probability.