Asymptotic Miss Ratio of LRU Caching with Consistent Hashing. - Kaiyi Ji; Guocong Quan; Jian Tan 001 (INFOCOM, 2018)

LRU Caching with Dependent Competing Requests. - Guocong Quan; Kaiyi Ji; Jian Tan 001 (INFOCOM, 2018)

Minimax Estimation of Neural Net Distance. - Kaiyi Ji; Yingbin Liang (NeurIPS, 2018)

Learning Latent Features with Pairwise Penalties in Matrix Completion. - Kaiyi Ji; Jian Tan 001; Yuejie Chi; Jinfeng Xu 001 (CoRR, 2018)

Convergence of SGD in Learning ReLU Models with Separable Data. - Tengyu Xu; Yi Zhou 017; Kaiyi Ji; Yingbin Liang (CoRR, 2018)

SpiderBoost: A Class of Faster Variance-reduced Algorithms for Nonconvex Optimization. - Zhe Wang 021; Kaiyi Ji; Yi Zhou 017; Yingbin Liang; Vahid Tarokh (CoRR, 2018)

On Resource Pooling and Separation for LRU Caching. - Jian Tan 001; Guocong Quan; Kaiyi Ji; Ness B. Shroff (Proc. ACM Meas. Anal. Comput. Syst., 2018)

Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization. - Kaiyi Ji; Zhe Wang 021; Yi Zhou 017; Yingbin Liang (ICML, 2019)

SpiderBoost and Momentum: Faster Variance Reduction Algorithms. - Zhe Wang 021; Kaiyi Ji; Yi Zhou 017; Yingbin Liang; Vahid Tarokh (NeurIPS, 2019)

Faster Stochastic Algorithms via History-Gradient Aided Batch Size Adaptation. - Kaiyi Ji; Zhe Wang 021; Yi Zhou 017; Yingbin Liang (CoRR, 2019)

Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack. - Ziwei Guan; Kaiyi Ji; Donald J. Bucci Jr.; Timothy Y. Hu; Joseph Palombo; Michael Liston; Yingbin Liang (AAAI, 2020)

History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms. - Kaiyi Ji; Zhe Wang 021; Bowen Weng; Yi Zhou 017; Wei Zhang 013; Yingbin Liang (ICML, 2020)

Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization. - Yi Zhou 017; Zhe Wang 021; Kaiyi Ji; Yingbin Liang; Vahid Tarokh (IJCAI, 2020)

Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters. - Kaiyi Ji; Jason D. Lee; Yingbin Liang; H. Vincent Poor (NeurIPS, 2020)

Multi-Step Model-Agnostic Meta-Learning: Convergence and Improved Algorithms. - Kaiyi Ji; Junjie Yang; Yingbin Liang (CoRR, 2020)

Improving the Convergence Rate of One-Point Zeroth-Order Optimization using Residual Feedback. - Yan Zhang 043; Yi Zhou 017; Kaiyi Ji; Michael M. Zavlanos (CoRR, 2020)

Boosting One-Point Derivative-Free Online Optimization via Residual Feedback. - Yan Zhang 043; Yi Zhou 017; Kaiyi Ji; Michael M. Zavlanos (CoRR, 2020)

Provably Faster Algorithms for Bilevel Optimization and Applications to Meta-Learning. - Kaiyi Ji; Junjie Yang; Yingbin Liang (CoRR, 2020)

Learning Latent Features With Pairwise Penalties in Low-Rank Matrix Completion. - Kaiyi Ji; Jian Tan 001; Jinfeng Xu 001; Yuejie Chi (IEEE Trans. Signal Process., 2020)

Bilevel Optimization: Convergence Analysis and Enhanced Design. - Kaiyi Ji; Junjie Yang; Yingbin Liang (ICML, 2021)

Provably Faster Algorithms for Bilevel Optimization. - Junjie Yang; Kaiyi Ji; Yingbin Liang (NeurIPS, 2021)

Bilevel Optimization for Machine Learning: Algorithm Design and Convergence Analysis. - Kaiyi Ji (CoRR, 2021)

ES-Based Jacobian Enables Faster Bilevel Optimization. - Daouda Sow; Kaiyi Ji; Yingbin Liang (CoRR, 2021)

Understanding Estimation and Generalization Error of Generative Adversarial Networks. - Kaiyi Ji; Yi Zhou 017; Yingbin Liang (IEEE Trans. Inf. Theory, 2021)

Will Bilevel Optimizers Benefit from Loops. - Kaiyi Ji; Mingrui Liu; Yingbin Liang; Lei Ying 001 (NeurIPS, 2022)

On the Convergence Theory for Hessian-Free Bilevel Algorithms. - Daouda Sow; Kaiyi Ji; Yingbin Liang (NeurIPS, 2022)

Data sampling affects the complexity of online SGD over dependent data. - Shaocong Ma; Ziyi Chen 002; Yi Zhou 017; Kaiyi Ji; Yingbin Liang (UAI, 2022)

A new one-point residual-feedback oracle for black-box learning and control. - Yan Zhang 043; Yi Zhou 017; Kaiyi Ji; Michael M. Zavlanos (Autom., 2022)

Efficiently Escaping Saddle Points in Bilevel Optimization. - Minhui Huang; Kaiyi Ji; Shiqian Ma; Lifeng Lai (CoRR, 2022)

A Constrained Optimization Approach to Bilevel Optimization with Multiple Inner Minima. - Daouda Sow; Kaiyi Ji; Ziwei Guan; Yingbin Liang (CoRR, 2022)

Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning. - Kaiyi Ji; Junjie Yang; Yingbin Liang (J. Mach. Learn. Res., 2022)

Achieving Linear Speedup in Non-IID Federated Bilevel Learning. - Minhui Huang; Dewei Zhang; Kaiyi Ji (ICML, 2023)

Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation. - Peiyao Xiao; Kaiyi Ji (ICML, 2023)

Network Utility Maximization with Unknown Utility Functions: A Distributed, Data-Driven Bilevel Optimization Approach. - Kaiyi Ji; Lei Ying 001 (MobiHoc, 2023)

Bilevel Coreset Selection in Continual Learning: A New Formulation and Algorithm. - Jie Hao; Kaiyi Ji; Mingrui Liu (NeurIPS, 2023)

Non-Convex Bilevel Optimization with Time-Varying Objective Functions. - Sen Lin 001; Daouda Sow; Kaiyi Ji; Yingbin Liang; Ness B. Shroff (NeurIPS, 2023)

Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms. - Peiyao Xiao; Hao Ban; Kaiyi Ji (NeurIPS, 2023)

SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning. - Yifan Yang; Peiyao Xiao; Kaiyi Ji (NeurIPS, 2023)

Achieving O(ε-1.5) Complexity in Hessian/Jacobian-free Stochastic Bilevel Optimization. - Yifan Yang; Peiyao Xiao; Kaiyi Ji (NeurIPS, 2023)

Lower Bounds and Accelerated Algorithms for Bilevel Optimization. - Kaiyi Ji; Yingbin Liang (J. Mach. Learn. Res., 2023)

AUC-CL: A Batchsize-Robust Framework for Self-Supervised Contrastive Representation Learning. - Rohan Sharma; Kaiyi Ji; Zhiqiang Xu; Changyou Chen (ICLR, 2024)

Fair Resource Allocation in Multi-Task Learning. - Hao Ban; Kaiyi Ji (ICML, 2024)

Understanding Forgetting in Continual Learning with Linear Regression. - Meng Ding; Kaiyi Ji; Di Wang 015; Jinhui Xu 001 (ICML, 2024)

First-Order Minimax Bilevel Optimization. - Yifan Yang; Zhaofeng Si; Siwei Lyu; Kaiyi Ji (NeurIPS, 2024)

Discriminative Adversarial Unlearning. - Rohan Sharma; Shijie Zhou; Kaiyi Ji; Changyou Chen (CoRR, 2024)

Finite-Time Analysis for Conflict-Avoidant Multi-Task Reinforcement Learning. - Yudan Wang; Peiyao Xiao; Hao Ban; Kaiyi Ji; Shaofeng Zou (CoRR, 2024)

On the Convergence of Multi-objective Optimization under Generalized Smoothness. - Qi Zhang 069; Peiyao Xiao; Kaiyi Ji; Shaofeng Zou (CoRR, 2024)

Imperative Learning: A Self-supervised Neural-Symbolic Learning Framework for Robot Autonomy. - Chen Wang 033; Kaiyi Ji; Junyi Geng; Zhongqiang Ren; Taimeng Fu; Fan Yang 092; Yifan Guo; Haonan He; Xiangyu Chen; Zitong Zhan; Qiwei Du; Shaoshu Su; et al. (CoRR, 2024)

Why Fine-Tuning Struggles with Forgetting in Machine Unlearning? Theoretical Insights and a Remedial Approach. - Meng Ding; Jinhui Xu 001; Kaiyi Ji (CoRR, 2024)

Meta-Learning with Heterogeneous Tasks. - Zhaofeng Si; Shu Hu 001; Kaiyi Ji; Siwei Lyu (CoRR, 2024)

Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements. - Shijie Zhou; Huaisheng Zhu; Rohan Sharma; Ruiyi Zhang; Kaiyi Ji; Changyou Chen (CoRR, 2024)

Boosting One-Point Derivative-Free Online Optimization via Residual Feedback. - Yan Zhang 043; Yi Zhou 017; Kaiyi Ji; Yi Shen 011; Michael M. Zavlanos (IEEE Trans. Autom. Control., 2024)

First-Order Federated Bilevel Learning. - Yifan Yang; Peiyao Xiao; Shiqian Ma; Kaiyi Ji (AAAI, 2025)

Tuning-Free Bilevel Optimization: New Algorithms and Convergence Analysis. - Yifan Yang; Hao Ban; Minhui Huang; Shiqian Ma; Kaiyi Ji (ICLR, 2025)

MGDA Converges under Generalized Smoothness, Provably. - Qi Zhang 069; Peiyao Xiao; Shaofeng Zou; Kaiyi Ji (ICLR, 2025)

ReGNet: Reciprocal Space-Aware Long-Range Modeling and Multi-Property Prediction for Crystals. - Jianan Nie; Peiyao Xiao; Kaiyi Ji; Peng Gao (CoRR, 2025)

Scalable Bilevel Loss Balancing for Multi-Task Learning. - Peiyao Xiao; Chaosheng Dong; Shaofeng Zou; Kaiyi Ji (CoRR, 2025)

SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation. - Hao Ban; Gokul Ram Subramani; Kaiyi Ji (CoRR, 2025)

Task-Agnostic Continual Prompt Tuning with Gradient-Based Selection and Decoding. - Anushka Tiwari; Sayantan Pal; Rohini K. Srihari; Kaiyi Ji (CoRR, 2025)

Theoretical Study of Conflict-Avoidant Multi-Objective Reinforcement Learning. - Yudan Wang; Peiyao Xiao; Hao Ban; Kaiyi Ji; Shaofeng Zou (IEEE Trans. Inf. Theory, 2025)