ASVRG: Accelerated Proximal SVRG. - Fanhua Shang; Licheng Jiao; Kaiwen Zhou 001; James Cheng; Yan Ren 002; Yufei Jin (ACML, 2018)

Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization. - Fanhua Shang; Yuanyuan Liu 001; Kaiwen Zhou 001; James Cheng; Kelvin Kai Wing Ng; Yuichi Yoshida (AISTATS, 2018)

A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates. - Kaiwen Zhou 001; Fanhua Shang; James Cheng (ICML, 2018)

VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning. - Fanhua Shang; Kaiwen Zhou 001; James Cheng; Ivor W. Tsang; Lijun Zhang 005; Dacheng Tao (CoRR, 2018)

Norm-Range Partition: A Univiseral Catalyst for LSH based Maximum Inner Product Search (MIPS). - Xiao Yan 002; Xinyan Dai; Jie Liu 048; Kaiwen Zhou 001; James Cheng (CoRR, 2018)

Direct Acceleration of SAGA using Sampled Negative Momentum. - Kaiwen Zhou 001; Qinghua Ding; Fanhua Shang; James Cheng; Danli Li; Zhi-Quan Luo (AISTATS, 2019)

Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning. - Xinyan Dai; Xiao Yan 002; Kaiwen Zhou 001; Han Yang 002; Kelvin Kai Wing Ng; James Cheng; Yu Fan (CoRR, 2019)

Tight Convergence Rate of Gradient Descent for Eigenvalue Computation. - Qinghua Ding; Kaiwen Zhou 001; James Cheng (IJCAI, 2020)

Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates. - Kaiwen Zhou 001; Anthony Man-Cho So; James Cheng (NeurIPS, 2020)

Convolutional Embedding for Edit Distance. - Xinyan Dai; Xiao Yan 002; Kaiwen Zhou 001; Yuxuan Wang; Han Yang 002; James Cheng (SIGIR, 2020)

Amortized Nesterov's Momentum: A Robust Momentum and Its Application to Deep Learning. - Kaiwen Zhou 001; Yanghua Jin; Qinghua Ding; James Cheng (UAI, 2020)

Edit Distance Embedding using Convolutional Neural Networks. - Xinyan Dai; Xiao Yan 002; Kaiwen Zhou 001; Yuxuan Wang; Han Yang 002; James Cheng (CoRR, 2020)

VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning. - Fanhua Shang; Kaiwen Zhou 001; Hongying Liu 001; James Cheng; Ivor W. Tsang; Lijun Zhang 005; Dacheng Tao; Licheng Jiao (IEEE Trans. Knowl. Data Eng., 2020)

Local Reweighting for Adversarial Training. - Ruize Gao; Feng Liu 003; Kaiwen Zhou 001; Gang Niu 001; Bo Han 003; James Cheng (CoRR, 2021)

Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization. - Kaiwen Zhou 001; Anthony Man-Cho So; James Cheng (CoRR, 2021)

Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums. - Kaiwen Zhou 001; Lai Tian; Anthony Man-Cho So; James Cheng (AISTATS, 2022)

Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. - Ruize Gao; Jiongxiao Wang; Kaiwen Zhou 001; Feng Liu 003; Binghui Xie; Gang Niu 001; Bo Han 003; James Cheng (ICML, 2022)

On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions. - Lai Tian; Kaiwen Zhou 001; Anthony Man-Cho So (ICML, 2022)

An Adaptive Incremental Gradient Method With Support for Non-Euclidean Norms. - Binghui Xie; Chenhan Jin; Kaiwen Zhou 001; James Cheng; Wei Meng 001 (CoRR, 2022)

Pareto Invariant Risk Minimization. - Yongqiang Chen 002; Kaiwen Zhou 001; Yatao Bian; Binghui Xie; Kaili Ma 001; Yonggang Zhang 003; Han Yang 002; Bo Han 003; James Cheng (CoRR, 2022)

Efficient Private SCO for Heavy-Tailed Data via Clipping. - Chenhan Jin; Kaiwen Zhou 001; Bo Han 003; James Cheng; Ming-Chang Yang (CoRR, 2022)

A Novel Extrapolation Technique to Accelerate WMMSE. - Kaiwen Zhou 001; Zhilin Chen; Guochen Liu; Zhitang Chen (ICASSP, 2023)

Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization. - Yongqiang Chen 002; Kaiwen Zhou 001; Yatao Bian; Binghui Xie; Bingzhe Wu; Yonggang Zhang 003; Kaili Ma 001; Han Yang 002; Peilin Zhao; Bo Han 003; James Cheng (ICLR, 2023)

Does Invariant Graph Learning via Environment Augmentation Learn Invariance? - Yongqiang Chen 002; Yatao Bian; Kaiwen Zhou 001; Binghui Xie; Bo Han 003; James Cheng (NeurIPS, 2023)

Understanding and Improving Feature Learning for Out-of-Distribution Generalization. - Yongqiang Chen 002; Wei Huang; Kaiwen Zhou 001; Yatao Bian; Bo Han 003; James Cheng (NeurIPS, 2023)

Towards Understanding Feature Learning in Out-of-Distribution Generalization. - Yongqiang Chen 002; Wei Huang; Kaiwen Zhou 001; Yatao Bian; Bo Han 003; James Cheng (CoRR, 2023)

Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes. - Yongqiang Chen 002; Binghui Xie; Kaiwen Zhou 001; Bo Han 003; Yatao Bian; James Cheng (CoRR, 2023)

Enhancing Evolving Domain Generalization through Dynamic Latent Representations. - Binghui Xie; Yongqiang Chen 002; Jiaqi Wang 012; Kaiwen Zhou 001; Bo Han 003; Wei Meng 001; James Cheng (AAAI, 2024)

Enhancing Neural Subset Selection: Integrating Background Information into Set Representations. - Binghui Xie; Yatao Bian; Kaiwen Zhou 001; Yongqiang Chen 002; Peilin Zhao; Bo Han 003; Wei Meng 001; James Cheng (ICLR, 2024)

RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models. - Haoyu Chen 003; Wenbo Li 002; Jinjin Gu; Jingjing Ren; Sixiang Chen; Tian Ye 001; Renjing Pei; Kaiwen Zhou 001; Fenglong Song; Lei Zhu 003 (NeurIPS, 2024)

HORSE: Hierarchical Representation for Large-Scale Neural Subset Selection. - Binghui Xie; Yixuan Wang; Yongqiang Chen 002; Kaiwen Zhou 001; Yu Li; Wei Meng 001; James Cheng (NeurIPS, 2024)

Beyond Pixels: Text Enhances Generalization in Real-World Image Restoration. - Haoze Sun; Wenbo Li 002; Jiayue Liu; Kaiwen Zhou 001; Yongqiang Chen 002; Yong Guo; Yanwei Li; Renjing Pei; Long Peng 003; Yujiu Yang (CoRR, 2024)

Efficient private SCO for heavy-tailed data via averaged clipping. - Chenhan Jin; Kaiwen Zhou 001; Bo Han 003; James Cheng; Tieyong Zeng (Mach. Learn., 2024)

MESH - Understanding Videos Like Human: Measuring Hallucinations in Large Video Models. - Garry Yang; Zizhe Chen; Man Hon Wong 001; Haoyu Lei; Yongqiang Chen 002; Zhenguo Li; Kaiwen Zhou 001; James Cheng (CoRR, 2025)