Scaled Relative Graph of Normal Matrices. - Xinmeng Huang; Ernest K. Ryu; Wotao Yin (CoRR, 2020)

DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training. - Kun Yuan 001; Yiming Chen 003; Xinmeng Huang; Yingya Zhang; Pan Pan; Yinghui Xu; Wotao Yin (ICCV, 2021)

An Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders. - Xinmeng Huang; Kun Yuan 001; Xianghui Mao; Wotao Yin (NeurIPS, 2021)

On the Comparison between Cyclic Sampling and Random Reshuffling. - Xinmeng Huang; Kun Yuan 001; Xianghui Mao; Wotao Yin (CoRR, 2021)

Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression. - Xinmeng Huang; Yiming Chen 003; Wotao Yin; Kun Yuan 001 (NeurIPS, 2022)

Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints. - Xinmeng Huang; Donghwan Lee; Edgar Dobriban; Hamed Hassani (NeurIPS, 2022)

Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization. - Kun Yuan 001; Xinmeng Huang; Yiming Chen 003; Xiaohan Zhang; Yingya Zhang; Pan Pan (NeurIPS, 2022)

Collaborative Learning of Distributions under Heterogeneity and Communication Constraints. - Xinmeng Huang; Donghwan Lee; Edgar Dobriban; Hamed Hassani (CoRR, 2022)

Optimal Complexity in Non-Convex Decentralized Learning over Time-Varying Networks. - Xinmeng Huang; Kun Yuan 001 (CoRR, 2022)

Demystifying Disagreement-on-the-Line in High Dimensions. - Donghwan Lee; Behrad Moniri; Xinmeng Huang; Edgar Dobriban; Hamed Hassani (ICML, 2023)

Unbiased Compression Saves Communication in Distributed Optimization: When and How Much? - Yutong He; Xinmeng Huang; Kun Yuan 001 (NeurIPS, 2023)

Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression. - Yutong He; Xinmeng Huang; Yiming Chen 003; Wotao Yin; Kun Yuan 001 (CoRR, 2023)

Optimal Heterogeneous Collaborative Linear Regression and Contextual Bandits. - Xinmeng Huang; Kan Xu; Donghwan Lee; Hamed Hassani; Hamsa Bastani; Edgar Dobriban (CoRR, 2023)

Momentum Benefits Non-IID Federated Learning Simply and Provably. - Ziheng Cheng; Xinmeng Huang; Kun Yuan 001 (CoRR, 2023)

Stochastic Controlled Averaging for Federated Learning with Communication Compression. - Xinmeng Huang; Ping Li; Xiaoyun Li (CoRR, 2023)

T-Cal: An Optimal Test for the Calibration of Predictive Models. - Donghwan Lee; Xinmeng Huang; Hamed Hassani; Edgar Dobriban (J. Mach. Learn. Res., 2023)

Removing Data Heterogeneity Influence Enhances Network Topology Dependence of Decentralized SGD. - Kun Yuan 001; Sulaiman A. Alghunaim; Xinmeng Huang (J. Mach. Learn. Res., 2023)

Uncertainty in Language Models: Assessment through Rank-Calibration. - Xinmeng Huang; Shuo Li; Mengxin Yu; Matteo Sesia; Hamed Hassani; Insup Lee 001; Osbert Bastani; Edgar Dobriban (EMNLP, 2024)

Momentum Benefits Non-iid Federated Learning Simply and Provably. - Ziheng Cheng; Xinmeng Huang; Pengfei Wu 006; Kun Yuan 001 (ICLR, 2024)

Stochastic Controlled Averaging for Federated Learning with Communication Compression. - Xinmeng Huang; Ping Li 001; Xiaoyun Li (ICLR, 2024)

Distributed Bilevel Optimization with Communication Compression. - Yutong He; Jie Hu 022; Xinmeng Huang; Songtao Lu; Bin Wang; Kun Yuan 001 (ICML, 2024)

One-Shot Safety Alignment for Large Language Models via Optimal Dualization. - Xinmeng Huang; Shuo Li; Edgar Dobriban; Osbert Bastani; Hamed Hassani; Dongsheng Ding (NeurIPS, 2024)

SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization. - Shuchen Zhu; Boao Kong; Songtao Lu; Xinmeng Huang; Kun Yuan 001 (NeurIPS, 2024)

Decentralized Bilevel Optimization over Graphs: Loopless Algorithmic Update and Transient Iteration Complexity. - Boao Kong; Shuchen Zhu; Songtao Lu; Xinmeng Huang; Kun Yuan 001 (CoRR, 2024)

Understanding the Influence of Digraphs on Decentralized Optimization: Effective Metrics, Lower Bound, and Optimal Algorithm. - Liyuan Liang; Xinmeng Huang; Ran Xin; Kun Yuan 001 (SIAM J. Optim., 2025)