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2020 – today
- 2024
[c10]Ziheng Cheng, Xinmeng Huang, Pengfei Wu, Kun Yuan:
Momentum Benefits Non-iid Federated Learning Simply and Provably. ICLR 2024- [c9]Xinmeng Huang, Ping Li, Xiaoyun Li:
Stochastic Controlled Averaging for Federated Learning with Communication Compression. ICLR 2024 - [c8]Yutong He, Jie Hu, Xinmeng Huang, Songtao Lu, Bin Wang, Kun Yuan:
Distributed Bilevel Optimization with Communication Compression. ICML 2024 - [i17]Boao Kong, Shuchen Zhu, Songtao Lu, Xinmeng Huang, Kun Yuan:
Decentralized Bilevel Optimization over Graphs: Loopless Algorithmic Update and Transient Iteration Complexity. CoRR abs/2402.03167 (2024) - [i16]Xinmeng Huang, Shuo Li, Mengxin Yu, Matteo Sesia, Hamed Hassani, Insup Lee, Osbert Bastani, Edgar Dobriban:
Uncertainty in Language Models: Assessment through Rank-Calibration. CoRR abs/2404.03163 (2024) - [i15]Xinmeng Huang, Shuo Li, Edgar Dobriban, Osbert Bastani, Hamed Hassani, Dongsheng Ding:
One-Shot Safety Alignment for Large Language Models via Optimal Dualization. CoRR abs/2405.19544 (2024) - 2023
- [j2]Kun Yuan, Sulaiman A. Alghunaim, Xinmeng Huang:
Removing Data Heterogeneity Influence Enhances Network Topology Dependence of Decentralized SGD. J. Mach. Learn. Res. 24: 280:1-280:53 (2023) - [j1]Donghwan Lee, Xinmeng Huang, Hamed Hassani, Edgar Dobriban:
T-Cal: An Optimal Test for the Calibration of Predictive Models. J. Mach. Learn. Res. 24: 335:1-335:72 (2023) - [c7]Donghwan Lee, Behrad Moniri, Xinmeng Huang, Edgar Dobriban, Hamed Hassani:
Demystifying Disagreement-on-the-Line in High Dimensions. ICML 2023: 19053-19093 - [c6]Yutong He, Xinmeng Huang, Kun Yuan:
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much? NeurIPS 2023 - [i14]Donghwan Lee, Behrad Moniri, Xinmeng Huang, Edgar Dobriban, Hamed Hassani:
Demystifying Disagreement-on-the-Line in High Dimensions. CoRR abs/2301.13371 (2023) - [i13]Yutong He, Xinmeng Huang, Yiming Chen, Wotao Yin, Kun Yuan:
Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression. CoRR abs/2305.07612 (2023) - [i12]Yutong He, Xinmeng Huang, Kun Yuan:
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much? CoRR abs/2305.16297 (2023) - [i11]Xinmeng Huang, Kan Xu, Donghwan Lee, Hamed Hassani, Hamsa Bastani, Edgar Dobriban:
Optimal Heterogeneous Collaborative Linear Regression and Contextual Bandits. CoRR abs/2306.06291 (2023) - [i10]Ziheng Cheng, Xinmeng Huang, Kun Yuan:
Momentum Benefits Non-IID Federated Learning Simply and Provably. CoRR abs/2306.16504 (2023) - [i9]Xinmeng Huang, Ping Li, Xiaoyun Li:
Stochastic Controlled Averaging for Federated Learning with Communication Compression. CoRR abs/2308.08165 (2023) - 2022
- [c5]Xinmeng Huang, Yiming Chen, Wotao Yin, Kun Yuan:
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression. NeurIPS 2022 - [c4]Xinmeng Huang, Donghwan Lee, Edgar Dobriban, Hamed Hassani:
Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints. NeurIPS 2022 - [c3]Kun Yuan, Xinmeng Huang, Yiming Chen, Xiaohan Zhang, Yingya Zhang, Pan Pan:
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization. NeurIPS 2022 - [i8]Donghwan Lee, Xinmeng Huang, Hamed Hassani, Edgar Dobriban:
T-Cal: An optimal test for the calibration of predictive models. CoRR abs/2203.01850 (2022) - [i7]Xinmeng Huang, Donghwan Lee, Edgar Dobriban, Hamed Hassani:
Collaborative Learning of Distributions under Heterogeneity and Communication Constraints. CoRR abs/2206.00707 (2022) - [i6]Xinmeng Huang, Yiming Chen, Wotao Yin, Kun Yuan:
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression. CoRR abs/2206.03665 (2022) - [i5]Kun Yuan, Xinmeng Huang, Yiming Chen, Xiaohan Zhang, Yingya Zhang, Pan Pan:
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization. CoRR abs/2210.07863 (2022) - [i4]Xinmeng Huang, Kun Yuan:
Optimal Complexity in Non-Convex Decentralized Learning over Time-Varying Networks. CoRR abs/2211.00533 (2022) - 2021
- [c2]Kun Yuan, Yiming Chen, Xinmeng Huang, Yingya Zhang, Pan Pan, Yinghui Xu, Wotao Yin:
DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training. ICCV 2021: 3009-3019 - [c1]Xinmeng Huang, Kun Yuan, Xianghui Mao, Wotao Yin:
An Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders. NeurIPS 2021: 3232-3243 - [i3]Kun Yuan, Yiming Chen
, Xinmeng Huang, Yingya Zhang, Pan Pan, Yinghui Xu, Wotao Yin:
DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training. CoRR abs/2104.11981 (2021) - [i2]Xinmeng Huang, Kun Yuan, Xianghui Mao, Wotao Yin:
On the Comparison between Cyclic Sampling and Random Reshuffling. CoRR abs/2104.12112 (2021) - 2020
- [i1]Xinmeng Huang, Ernest K. Ryu, Wotao Yin:
Scaled Relative Graph of Normal Matrices. CoRR abs/2001.02061 (2020)
Coauthor Index
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