Welcome to Kaiyi Ji's Homepage
About Me
I am an assistant professor at the Department of Computer Science and Engineering of the University at Buffalo, The State University of New York. I received my Ph.D. degree from the Electrical and Computer Engineering Department of The Ohio State University in December, 2021, advised by Prof. Yingbin Liang. I was a postdoctoral research fellow at the Electrical Engineering and Computer Science Department of the University of Michigan, Ann Arbor, in 2022, working with Prof. Lei Ying. I was a visiting student research collaborator at the department of Electrical Engineering, Princeton University.
Previously I obtained my B.S. degree from University of Science and Technology of China in 2016.
Prospective students: I am now looking for PhD and Intern students with rich math and coding experiences in LLMs (optimization, alignment, prompt tuning, etc). Please send me an email with your CV and Transcript.
Research
My current research interests include:
Large-scale optimization: bilevel optimization, multi-objective optimization, and optimization for LLMs.
Machine Learning and deep learning: multi-task learning, model merging, continual learning and AI4Science
Foundation models: progressive prompts, parameter-efficient fine-tuning, multi-objective RLHF
Recent News!
Multi-Objective/Task Learning
(New!) Scalable Bilevel Loss Balancing for Multi-Task Learning <Code> Peiyao Xiao, Chaosheng Dong, Shaofeng Zou, Kaiyi Ji.
(New!) MGDA Converges under Generalized Smoothness, Provably. Qi Zhang, Peiyao Xiao, Shaofeng Zou, Kaiyi Ji. International Conference on Learning Representations (ICLR) 2025.
(New!) Theoretical Study of Conflict-Avoidant
Multi-Objective Reinforcement Learning Yudan Wang, Peiyao Xiao, Hao Ban, Kaiyi Ji, Shaofeng Zou. IEEE Transactions on Information Theory 2025, under revision.
Fair Resource Allocation in Multi-Task Learning <Code> Hao Ban and Kaiyi Ji. International Conference on Machine Learning (ICML) 2024.
Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms <Code> Peiyao Xiao, Hao Ban, Kaiyi Ji. Conference on Neural Information Processing Systems (NeurIPS) 2023.
Bilevel Optimization: Theory and Applications
(New!) Tuning-Free Bilevel Optimization: New Algorithms and Convergence Analysis <Code> Yifan Yang, Hao Ban, Minhui Huang, Shiqian Ma, Kaiyi Ji.
International Conference on Learning Representations (ICLR) 2025
(New!) Imperative Learning: A Self-supervised Neural-Symbolic Learning Framework for Robot Autonomy <Open-Source Library> Chen Wang, Kaiyi Ji, Junyi Geng, Zhongqiang Ren, Taimeng Fu, Fan Yang, Yifan Guo, Haonan He, Xiangyu Chen, Zitong Zhan, Qiwei Du, Shaoshu Su, Bowen Li, Yuheng Qiu, Yi Du, Qihang Li, Yifan Yang, Xiao Lin, Zhipeng Zhao. arXiv:2406.16087, 2024.
Lower Bounds and Accelerated Algorithms for Bilevel Optimization Kaiyi Ji, Yingbin Liang Journal of Machine Learning Research (JMLR) 2022.
Bilevel Optimization: Nonasymptotic Analysis and Faster Algorithms <Code> Kaiyi Ji, Junjie Yang, Yingbin Liang. International Conference on Machine Learning (ICML) 2021.
Distributed Learning over Networks
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