Zhang, Xinwei

20 publications

ICLR 2025 Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models Zeman Li, Xinwei Zhang, Peilin Zhong, Yuan Deng, Meisam Razaviyayn, Vahab Mirrokni

ICLR 2025 DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn, Vahab Mirrokni

NeurIPS 2025 Toward Efficient Inference Attacks: Shadow Model Sharing via Mixture-of-Experts Li Bai, Qingqing Ye, Xinwei Zhang, Sen Zhang, Zi Liang, Jianliang Xu, Haibo Hu

ICLRW 2024 Addax: Memory-Efficient Fine-Tuning of Language Models with a Combination of Forward-Backward and Forward-Only Passes Zeman Li, Xinwei Zhang, Meisam Razaviyayn

NeurIPSW 2024 Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models Zeman Li, Xinwei Zhang, Peilin Zhong, Yuan Deng, Meisam Razaviyayn, Vahab Mirrokni

NeurIPSW 2024 Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models Zeman Li, Xinwei Zhang, Peilin Zhong, Yuan Deng, Meisam Razaviyayn, Vahab Mirrokni

ICML 2024 Boundary Exploration for Bayesian Optimization with Unknown Physical Constraints Yunsheng Tian, Ane Zuniga, Xinwei Zhang, Johannes P. Dürholt, Payel Das, Jie Chen, Wojciech Matusik, Mina Konakovic Lukovic

NeurIPS 2024 DOPPLER: Differentially Private Optimizers with Low-Pass Filter for Privacy Noise Reduction Xinwei Zhang, Zhiqi Bu, Mingyi Hong, Meisam Razaviyayn

NeurIPSW 2024 DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn, Vahab Mirrokni

ICLR 2024 Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach Xinwei Zhang, Zhiqi Bu, Steven Wu, Mingyi Hong

CVPRW 2024 Enhancing the Transferability of Adversarial Attacks with Stealth Preservation Xinwei Zhang, Tianyuan Zhang, Yitong Zhang, Shuangcheng Liu

TMLR 2024 GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data Xinwei Zhang, Mingyi Hong, Jie Chen

TMLR 2024 Hybrid Federated Learning for Feature & Sample Heterogeneity: Algorithms and Implementation Xinwei Zhang, Wotao Yin, Mingyi Hong, Tianyi Chen

NeurIPS 2024 Pre-Training Differentially Private Models with Limited Public Data Zhiqi Bu, Xinwei Zhang, Sheng Zha, Mingyi Hong, George Karypis

ICML 2023 FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks Bingqing Song, Prashant Khanduri, Xinwei Zhang, Jinfeng Yi, Mingyi Hong

ICML 2022 A Stochastic Multi-Rate Control Framework for Modeling Distributed Optimization Algorithms Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Nicola Elia

NeurIPSW 2022 A Unified Framework to Understand Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective Xinwei Zhang, Nicola Elia, Mingyi Hong

NeurIPSW 2022 Building Large Machine Learning Models from Small Distributed Models: A Layer Matching Approach Xinwei Zhang, Bingqing Song, Mehrdad Honarkhah, Jie Ding, Mingyi Hong

NeurIPS 2022 Grow and Merge: A Unified Framework for Continuous Categories Discovery Xinwei Zhang, Jianwen Jiang, Yutong Feng, Zhi-Fan Wu, Xibin Zhao, Hai Wan, Mingqian Tang, Rong Jin, Yue Gao

ICML 2022 Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Steven Wu, Jinfeng Yi