Learning Path Tracking for Real Car-like Mobile Robots From Simulation. - Danial Kamran; Junyi Zhu 002; Martin Lauer (ECMR, 2019)

Localization in Aerial Imagery with Grid Maps using LocGAN. - Haohao Hu; Junyi Zhu 002; Sascha Wirges; Martin Lauer (ITSC, 2019)

R-GAP: Recursive Gradient Attack on Privacy. - Junyi Zhu 002; Matthew B. Blaschko (ICLR, 2021)

Differentially Private SGD with Sparse Gradients. - Junyi Zhu 002; Matthew B. Blaschko (CoRR, 2021)

Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization. - Junyi Zhu 002; Xingchen Ma; Matthew B. Blaschko (CVPR, 2023)

Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning. - Junyi Zhu 002; Ruicong Yao; Matthew B. Blaschko (ICML, 2023)

Improving Differentially Private SGD via Randomly Sparsified Gradients. - Junyi Zhu 002; Matthew B. Blaschko (Trans. Mach. Learn. Res., 2023)

FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language Models. - Junyi Zhu 002; Shuochen Liu; Yu Yu; Bo Tang; Yibo Yan; Zhiyu Li; Feiyu Xiong; Tong Xu; Matthew B. Blaschko (EMNLP (Findings), 2024)

Rescaling Intermediate Features Makes Trained Consistency Models Perform Better. - Junyi Zhu 002; Zinan Lin 001; Enshu Liu; Xuefei Ning; Matthew B. Blaschko (Tiny Papers @ ICLR, 2024)

Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better. - Enshu Liu; Junyi Zhu 002; Zinan Lin 001; Xuefei Ning; Matthew B. Blaschko; Sergey Yekhanin; Shengen Yan; Guohao Dai; Huazhong Yang; Yu Wang 002 (CoRR, 2024)

Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs. - Enshu Liu; Junyi Zhu 002; Zinan Lin 001; Xuefei Ning; Matthew B. Blaschko; Shengen Yan; Guohao Dai; Huazhong Yang; Yu Wang 002 (CoRR, 2024)

Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction. - Jiayang Shi; Junyi Zhu 002; Daniël Maria Pelt; Kees Joost Batenburg; Matthew B. Blaschko (Trans. Mach. Learn. Res., 2024)

Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better. - Enshu Liu; Junyi Zhu 002; Zinan Lin 001; Xuefei Ning; Shuaiqi Wang; Matthew B. Blaschko; Sergey Yekhanin; Shengen Yan; Guohao Dai; Huazhong Yang; Yu Wang 002 (ICLR, 2025)

Unlocking the Value of Decentralized Data: A Federated Dual Learning Approach for Model Aggregation. - Junyi Zhu 002; Ruicong Yao; Taha Ceritli; Savas Özkan; Matthew B. Blaschko; Eunchung Noh; Jeongwon Min; Cho Jung Min; Mete Ozay (CoRR, 2025)

Xinyu AI Search: Enhanced Relevance and Comprehensive Results with Rich Answer Presentations. - Bo Tang; Junyi Zhu 002; Chenyang Xi; Yunhang Ge; Jiahao Wu; Yuchen Feng; Yijun Niu; Wenqiang Wei; Yu Yu; Chunyu Li; Zehao Lin; Hao Wu; et al. (CoRR, 2025)

Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with Jigsaw Puzzles. - Zifu Wang; Junyi Zhu 002; Bo Tang; Zhiyu Li; Feiyu Xiong; Jiaqian Yu; Matthew B. Blaschko (CoRR, 2025)