Generating Realistic Stock Market Order Streams. - Junyi Li 002; Xintong Wang 002; Yaoyang Lin; Arunesh Sinha; Michael P. Wellman (AAAI, 2020)

Faster Secure Data Mining via Distributed Homomorphic Encryption. - Junyi Li 002; Heng Huang (KDD, 2020)

Improved Bilevel Model: Fast and Optimal Algorithm with Theoretical Guarantee. - Junyi Li 002; Bin Gu 001; Heng Huang (CoRR, 2020)

SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients. - Feihu Huang 001; Junyi Li 002; Heng Huang (NeurIPS, 2021)

Compositional Federated Learning: Applications in Distributionally Robust Averaging and Meta Learning. - Feihu Huang 001; Junyi Li 002; Heng Huang (CoRR, 2021)

A Fully Single Loop Algorithm for Bilevel Optimization without Hessian Inverse. - Junyi Li 002; Bin Gu 001; Heng Huang (AAAI, 2022)

On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum. - Hongchang Gao; Junyi Li 002; Heng Huang (ICML, 2022)

Communication-Efficient Robust Federated Learning with Noisy Labels. - Junyi Li 002; Jian Pei 001; Heng Huang (KDD, 2022)

Enhanced Bilevel Optimization via Bregman Distance. - Feihu Huang 001; Junyi Li 002; Shangqian Gao; Heng Huang (NeurIPS, 2022)

Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction. - Junyi Li 002; Feihu Huang 001; Heng Huang (CoRR, 2022)

FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings. - Junyi Li 002; Heng Huang (CoRR, 2022)

Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning. - Junyi Li 002; Heng Huang (NeurIPS, 2023)

Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems. - Junyi Li 002; Feihu Huang 001; Heng Huang (NeurIPS, 2023)

Federated Conditional Stochastic Optimization. - Xidong Wu; Jianhui Sun; Zhengmian Hu; Junyi Li 002; Aidong Zhang; Heng Huang (NeurIPS, 2023)

FedDA: Faster Framework of Local Adaptive Gradient Methods via Restarted Dual Averaging. - Junyi Li 002; Feihu Huang 001; Heng Huang (CoRR, 2023)

Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems. - Junyi Li 002; Feihu Huang 001; Heng Huang (CoRR, 2023)

Adaptive Federated Minimax Optimization with Lower Complexities. - Feihu Huang 001; Xinrui Wang 003; Junyi Li 002; Songcan Chen (AISTATS, 2024)

Device-Wise Federated Network Pruning. - Shangqian Gao; Junyi Li 002; Zeyu Zhang; Yanfu Zhang; Weidong Cai 001; Heng Huang (CVPR, 2024)

FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization. - Junyi Li 002; Feihu Huang 001; Heng Huang (ICLR, 2024)

Dropout Enhanced Bilevel Training. - Peiran Yu; Junyi Li 002; Heng Huang (ICLR, 2024)

The steerability of large language models toward data-driven personas. - Junyi Li 002; Charith Peris; Ninareh Mehrabi; Palash Goyal; Kai-Wei Chang 001; Aram Galstyan; Richard S. Zemel; Rahul Gupta 001 (NAACL-HLT, 2024)

Provably Faster Algorithms for Bilevel Optimization via Without-Replacement Sampling. - Junyi Li 002; Heng Huang (NeurIPS, 2024)

Hessian Free Efficient Single Loop Iterative Differentiation Methods for Bi-Level Optimization Problems. - Peiran Yu; Junyi Li 002; Heng Huang (Trans. Mach. Learn. Res., 2024)

Provably Mitigating Corruption, Overoptimization, and Verbosity Simultaneously in Offline and Online RLHF/DPO Alignment. - Ziyi Chen 002; Junyi Li 002; Peiran Yu; Heng Huang (CoRR, 2025)