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ORCIDBin Gu 0001
Person information
- affiliation: Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE
- affiliation (former): Nanjing University of Information Science and Technology, Jiangsu Engineering Center of Network Monitoring, China
- affiliation (PhD 2011): Nanjing University of Aeronautics and Astronautics, China
Other persons with the same name
- Bin Gu — disambiguation page
- Bin Gu 0002
![[0000-0002-5578-7360]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-5578-7360")
— Tianjin University, Tianjin, China - Bin Gu 0003 — Boston University, Questrom School of Business, Department of Information Systems, Boston, MA, USA (and 2 more)
- Bin Gu 0004 — University of Science and Technology of China, Hefei, China
- Bin Gu 0005 — Southeast University, National Mobile Communications Research Laboratory, Nanjing, China (and 1 more)
- Bin Gu 0006 — Beijing Institute of Control Engineering, China
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Journal Articles
- 2024
[j46]Hong Chen, Xuelin Zhang, Tieliang Gong, Bin Gu, Feng Zheng:
Error Density-dependent Empirical Risk Minimization. Expert Syst. Appl. 254: 124332 (2024)- [j45]Shao-Qun Zhang, Jia-Yi Chen, Jin-Hui Wu, Gao Zhang, Huan Xiong, Bin Gu, Zhi-Hua Zhou:
On the Intrinsic Structures of Spiking Neural Networks. J. Mach. Learn. Res. 25: 194:1-194:74 (2024) - [j44]Chenkang Zhang, Heng Huang, Bin Gu:
Tackle balancing constraints in semi-supervised ordinal regression. Mach. Learn. 113(5): 2575-2595 (2024) - [j43]Ganyu Wang, Qingsong Zhang, Xiang Li, Boyu Wang, Bin Gu, Charles X. Ling:
Secure and fast asynchronous Vertical Federated Learning via cascaded hybrid optimization. Mach. Learn. 113(9): 6413-6451 (2024) - [j42]Bin Gu, Xiyuan Wei, Hualin Zhang, Yi Chang, Heng Huang:
Obtaining Lower Query Complexities Through Lightweight Zeroth-Order Proximal Gradient Algorithms. Neural Comput. 36(5): 897-935 (2024) - [j41]Zhuang Qian, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, Xinping Yi, Bin Gu, Huan Xiong:
Perturbation diversity certificates robust generalization. Neural Networks 172: 106117 (2024) - [j40]Nan Yin, Li Shen, Huan Xiong, Bin Gu, Chong Chen, Xian-Sheng Hua, Siwei Liu, Xiao Luo:
Messages are Never Propagated Alone: Collaborative Hypergraph Neural Network for Time-Series Forecasting. IEEE Trans. Pattern Anal. Mach. Intell. 46(4): 2333-2347 (2024) - [j39]Huan Xiong, Lei Huang, Wenston J. T. Zang, Xiantong Zhen, Guo-Sen Xie, Bin Gu, Le Song:
On the Number of Linear Regions of Convolutional Neural Networks With Piecewise Linear Activations. IEEE Trans. Pattern Anal. Mach. Intell. 46(7): 5131-5148 (2024) - [j38]Zhou Zhai, Heng Huang, Bin Gu:
Kernel Path for Semisupervised Support Vector Machine. IEEE Trans. Neural Networks Learn. Syst. 35(2): 1512-1522 (2024) - [j37]Zhiyuan Dang, Bin Gu, Cheng Deng, Heng Huang:
Asynchronous Parallel Large-Scale Gaussian Process Regression. IEEE Trans. Neural Networks Learn. Syst. 35(6): 8683-8694 (2024) - 2023
- [j36]Bin Gu, Chenkang Zhang, Zhouyuan Huo, Heng Huang:
A new large-scale learning algorithm for generalized additive models. Mach. Learn. 112(9): 3077-3104 (2023) - [j35]Haiyan Chen, Ying Yu, Yizhen Jia, Bin Gu:
Incremental learning for transductive support vector machine. Pattern Recognit. 133: 108982 (2023) - [j34]Bin Gu, Ziran Xiong, Xiang Li, Zhou Zhai, Guansheng Zheng:
Kernel Path for ν-Support Vector Classification. IEEE Trans. Neural Networks Learn. Syst. 34(1): 490-501 (2023) - [j33]Ziran Xiong, Charles X. Ling, Bin Gu:
Kernel Error Path Algorithm. IEEE Trans. Neural Networks Learn. Syst. 34(11): 8866-8878 (2023) - 2022
- [j32]Haiyan Chen, Yizhen Jia, Jiaming Ge, Bin Gu:
Incremental learning algorithm for large-scale semi-supervised ordinal regression. Neural Networks 149: 124-136 (2022) - [j31]Zhiyuan Dang, Xiang Li, Bin Gu, Cheng Deng, Heng Huang:
Large-Scale Nonlinear AUC Maximization via Triply Stochastic Gradients. IEEE Trans. Pattern Anal. Mach. Intell. 44(3): 1385-1398 (2022) - [j30]Bin Gu, Zhiyuan Dang, Zhouyuan Huo, Cheng Deng, Heng Huang:
Scaling Up Generalized Kernel Methods. IEEE Trans. Pattern Anal. Mach. Intell. 44(7): 3767-3778 (2022) - [j29]Bin Gu, An Xu, Zhouyuan Huo, Cheng Deng, Heng Huang:
Privacy-Preserving Asynchronous Vertical Federated Learning Algorithms for Multiparty Collaborative Learning. IEEE Trans. Neural Networks Learn. Syst. 33(11): 6103-6115 (2022) - 2021
- [j28]Bin Gu, Xiyuan Wei, Shangqian Gao, Ziran Xiong, Cheng Deng, Heng Huang:
Black-Box Reductions for Zeroth-Order Gradient Algorithms to Achieve Lower Query Complexity. J. Mach. Learn. Res. 22: 170:1-170:47 (2021) - [j27]Wanli Shi, Bin Gu, Xiang Li, Cheng Deng, Heng Huang:
Triply stochastic gradient method for large-scale nonlinear similar unlabeled classification. Mach. Learn. 110(8): 2005-2033 (2021) - [j26]Bin Gu, Xiang Geng, Wanli Shi, Yingying Shan, Yufang Huang, Zhijie Wang, Guansheng Zheng:
Solving large-scale support vector ordinal regression with asynchronous parallel coordinate descent algorithms. Pattern Recognit. 109: 107592 (2021) - [j25]Bin Gu, Ziran Xiong, Shuyang Yu, Guansheng Zheng:
A kernel path algorithm for general parametric quadratic programming problem. Pattern Recognit. 116: 107941 (2021) - [j24]Bin Gu, Charles X. Ling:
Generalized error path algorithm. Pattern Recognit. 120: 108112 (2021) - [j23]Bin Gu, Yingying Shan, Xin Quan, Guansheng Zheng:
Accelerating Sequential Minimal Optimization via Stochastic Subgradient Descent. IEEE Trans. Cybern. 51(4): 2215-2223 (2021) - [j22]Bin Gu, Xiang Geng, Xiang Li, Wanli Shi, Guansheng Zheng, Cheng Deng, Heng Huang:
Scalable Kernel Ordinal Regression via Doubly Stochastic Gradients. IEEE Trans. Neural Networks Learn. Syst. 32(8): 3677-3689 (2021) - [j21]Bin Gu, Zhou Zhai, Cheng Deng, Heng Huang:
Efficient Active Learning by Querying Discriminative and Representative Samples and Fully Exploiting Unlabeled Data. IEEE Trans. Neural Networks Learn. Syst. 32(9): 4111-4122 (2021) - 2020
- [j20]Bin Gu, Wenhan Xian, Zhouyuan Huo, Cheng Deng, Heng Huang:
A Unified q-Memorization Framework for Asynchronous Stochastic Optimization. J. Mach. Learn. Res. 21: 190:1-190:53 (2020) - 2019
- [j19]Bin Gu, Xiang Geng, Xiang Li, Guansheng Zheng:
Efficient inexact proximal gradient algorithms for structured sparsity-inducing norm. Neural Networks 118: 352-362 (2019) - [j18]Victor S. Sheng, Jing Zhang, Bin Gu, Xindong Wu:
Majority Voting and Pairing with Multiple Noisy Labeling. IEEE Trans. Knowl. Data Eng. 31(7): 1355-1368 (2019) - 2018
- [j17]Xiang Li, Huaimin Wang, Bin Gu, Charles X. Ling:
The convergence of linear classifiers on large sparse data. Neurocomputing 273: 622-633 (2018) - [j16]Bin Gu, Yingying Shan, Victor S. Sheng, Yuhui Zheng, Shuo Li:
Sparse regression with output correlation for cardiac ejection fraction estimation. Inf. Sci. 423: 303-312 (2018) - [j15]Bin Gu:
A regularization path algorithm for support vector ordinal regression. Neural Networks 98: 114-121 (2018) - [j14]Bin Gu, Xin Quan, Yunhua Gu, Victor S. Sheng, Guansheng Zheng:
Chunk incremental learning for cost-sensitive hinge loss support vector machine. Pattern Recognit. 83: 196-208 (2018) - [j13]Bin Gu, Victor S. Sheng:
A Solution Path Algorithm for General Parametric Quadratic Programming Problem. IEEE Trans. Neural Networks Learn. Syst. 29(9): 4462-4472 (2018) - 2017
- [j12]Bin Gu, Victor S. Sheng, KengYeow Tay, Walter Romano, Shuo Li:
Cross Validation Through Two-Dimensional Solution Surface for Cost-Sensitive SVM. IEEE Trans. Pattern Anal. Mach. Intell. 39(6): 1103-1121 (2017) - [j11]Bin Gu, Victor S. Sheng:
A Robust Regularization Path Algorithm for ν-Support Vector Classification. IEEE Trans. Neural Networks Learn. Syst. 28(5): 1241-1248 (2017) - [j10]Bin Gu, Xingming Sun, Victor S. Sheng:
Structural Minimax Probability Machine. IEEE Trans. Neural Networks Learn. Syst. 28(7): 1646-1656 (2017) - 2015
- [j9]Bin Gu, Victor S. Sheng, Zhijie Wang, Derek Ho, Said Osman, Shuo Li:
Incremental learning for ν-Support Vector Regression. Neural Networks 67: 140-150 (2015) - [j8]Bin Gu, Victor S. Sheng, KengYeow Tay, Walter Romano, Shuo Li:
Incremental Support Vector Learning for Ordinal Regression. IEEE Trans. Neural Networks Learn. Syst. 26(7): 1403-1416 (2015) - 2014
- [j7]Victor S. Sheng, Bin Gu, Wei Fang, Jian Wu:
Cost-sensitive learning for defect escalation. Knowl. Based Syst. 66: 146-155 (2014) - [j6]Zhijie Wang, Mohamed Ben Salah, Bin Gu, Ali Islam, Aashish Goela, Shuo Li:
Direct Estimation of Cardiac Biventricular Volumes With an Adapted Bayesian Formulation. IEEE Trans. Biomed. Eng. 61(4): 1251-1260 (2014) - 2013
- [j5]Bin Gu, Victor S. Sheng:
Feasibility and Finite Convergence Analysis for Accurate On-Line $\nu$-Support Vector Machine. IEEE Trans. Neural Networks Learn. Syst. 24(8): 1304-1315 (2013) - 2012
- [j4]Bin Gu, Jiandong Wang, Yuecheng Yu, Guansheng Zheng, Yufan Huang, Tao Xu:
Accurate on-line v-support vector learning. Neural Networks 27: 51-59 (2012) - [j3]Bin Gu, Jiandong Wang, Guansheng Zheng, Yuecheng Yu:
Regularization Path for ν-Support Vector Classification. IEEE Trans. Neural Networks Learn. Syst. 23(5): 800-811 (2012) - 2010
- [j2]Bin Gu, Jiandong Wang, Tao Li:
Ordinal-Class Core Vector Machine. J. Comput. Sci. Technol. 25(4): 699-708 (2010) - [j1]Yuecheng Yu, Jiandong Wang, Guansheng Zheng, Bin Gu:
Semi-supervised Distributed Clustering with Mahalanobis Distance Metric Learning. J. Digit. Content Technol. its Appl. 4(9): 132-140 (2010)
Conference and Workshop Papers
- 2024
- [c85]Hilal AlQuabeh, William de Vazelhes, Bin Gu:
Limited Memory Online Gradient Descent for Kernelized Pairwise Learning with Dynamic Averaging. AAAI 2024: 10821-10828 - [c84]Srinivas Anumasa, Bhaskar Mukhoty, Velibor Bojkovic, Giulia De Masi, Huan Xiong, Bin Gu:
Enhancing Training of Spiking Neural Network with Stochastic Latency. AAAI 2024: 10900-10908 - [c83]William de Vazelhes, Bhaskar Mukhoty, Xiao-Tong Yuan, Bin Gu:
Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery. AAAI 2024: 11731-11739 - [c82]Nan Yin, Mengzhu Wang, Zhenghan Chen, Giulia De Masi, Huan Xiong, Bin Gu:
Dynamic Spiking Graph Neural Networks. AAAI 2024: 16495-16503 - [c81]Yajing Fan, Wanli Shi, Yi Chang, Bin Gu:
Fast and Adversarial Robust Kernelized SDU Learning. AISTATS 2024: 1153-1161 - [c80]Zhou Zhai, Wanli Shi, Heng Huang, Yi Chang, Bin Gu:
Learning Sampling Policy to Achieve Fewer Queries for Zeroth-Order Optimization. AISTATS 2024: 1162-1170 - [c79]Velibor Bojkovic, Srinivas Anumasa, Giulia De Masi, Bin Gu, Huan Xiong:
Data Driven Threshold and Potential Initialization for Spiking Neural Networks. AISTATS 2024: 4771-4779 - [c78]Haiyan Jiang, Vincent Zoonekynd, Giulia De Masi, Bin Gu, Huan Xiong:
TAB: Temporal Accumulated Batch Normalization in Spiking Neural Networks. ICLR 2024 - [c77]Diyang Li, Charles Ling, Zhiqiang Xu, Huan Xiong, Bin Gu:
Learning No-Regret Sparse Generalized Linear Models with Varying Observation(s). ICLR 2024 - [c76]Longkang Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang:
Federated Causal Discovery from Heterogeneous Data. ICLR 2024 - [c75]Xinyue Liu, Hualin Zhang, Bin Gu, Hong Chen:
General Stability Analysis for Zeroth-Order Optimization Algorithms. ICLR 2024 - [c74]Bhaskar Mukhoty, Hilal AlQuabeh, Giulia De Masi, Huan Xiong, Bin Gu:
Certified Adversarial Robustness for Rate Encoded Spiking Neural Networks. ICLR 2024 - [c73]Nan Yin, Mengzhu Wang, Zhenghan Chen, Li Shen, Huan Xiong, Bin Gu, Xiao Luo:
DREAM: Dual Structured Exploration with Mixup for Open-set Graph Domain Adaption. ICLR 2024 - [c72]Xinzhe Yuan, William de Vazelhes, Bin Gu, Huan Xiong:
New Insight of Variance reduce in Zero-Order Hard-Thresholding: Mitigating Gradient Error and Expansivity Contradictions. ICLR 2024 - [c71]Haiyan Jiang, Giulia De Masi, Huan Xiong, Bin Gu:
NDOT: Neuronal Dynamics-based Online Training for Spiking Neural Networks. ICML 2024 - [c70]Wanli Shi, Yi Chang, Bin Gu:
Double Momentum Method for Lower-Level Constrained Bilevel Optimization. ICML 2024 - [c69]Chengqian Gao, William de Vazelhes, Hualin Zhang, Bin Gu, Zhiqiang Xu:
Hard-Thresholding Meets Evolution Strategies in Reinforcement Learning. IJCAI 2024: 3989-3997 - [c68]Xuelin Zhang, Hong Chen, Bin Gu, Tieliang Gong, Feng Zheng:
Fine-grained Analysis of Stability and Generalization for Stochastic Bilevel Optimization. IJCAI 2024: 5508-5516 - [c67]Haozhen Zhang, Hualin Zhang, Bin Gu, Yi Chang:
Subspace Selection based Prompt Tuning with Nonconvex Nonsmooth Black-Box Optimization. KDD 2024: 4179-4190 - [c66]Ke Zhang, Ganyu Wang, Han Li, Yulong Wang, Hong Chen, Bin Gu:
Asynchronous Vertical Federated Learning for Kernelized AUC Maximization. KDD 2024: 4244-4255 - 2023
- [c65]Jun Chen, Hong Chen, Xue Jiang, Bin Gu, Weifu Li, Tieliang Gong, Feng Zheng:
On the Stability and Generalization of Triplet Learning. AAAI 2023: 7033-7041 - [c64]Diyang Li, Bin Gu:
When Online Learning Meets ODE: Learning without Forgetting on Variable Feature Space. AAAI 2023: 8545-8553 - [c63]Jiahuan Wang, Jun Chen, Hong Chen, Bin Gu, Weifu Li, Xin Tang:
Stability-Based Generalization Analysis for Mixtures of Pointwise and Pairwise Learning. AAAI 2023: 10113-10121 - [c62]Chenkang Zhang, Lei Luo, Bin Gu:
Denoising Multi-Similarity Formulation: A Self-Paced Curriculum-Driven Approach for Robust Metric Learning. AAAI 2023: 11183-11191 - [c61]Zhou Zhai, Lei Luo, Heng Huang, Bin Gu:
Faster Fair Machine via Transferring Fairness Constraints to Virtual Samples. AAAI 2023: 11918-11925 - [c60]Hilal AlQuabeh, Bhaskar Mukhoty, Bin Gu:
Variance Reduced Online Gradient Descent for Kernelized Pairwise Learning with Limited Memory. ACML 2023: 28-43 - [c59]Hongchang Gao, Bin Gu, My T. Thai:
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network. AISTATS 2023: 9238-9281 - [c58]Yufan Huang, Mengnan Qi, Yongqiang Yao, Maoquan Wang, Bin Gu, Colin B. Clement, Neel Sundaresan:
Program Translation via Code Distillation. EMNLP 2023: 10903-10914 - [c57]Mengnan Qi, Yufan Huang, Maoquan Wang, Yongqiang Yao, Zihan Liu, Bin Gu, Colin B. Clement, Neel Sundaresan:
SUT: Active Defects Probing for Transcompiler Models. EMNLP 2023: 14024-14034 - [c56]Hualin Zhang, Bin Gu:
Faster Gradient-Free Methods for Escaping Saddle Points. ICLR 2023 - [c55]Haiyan Jiang, Srinivas Anumasa, Giulia De Masi, Huan Xiong, Bin Gu:
A Unified Optimization Framework of ANN-SNN Conversion: Towards Optimal Mapping from Activation Values to Firing Rates. ICML 2023: 14945-14974 - [c54]Huimin Wu, Wanli Shi, Chenkang Zhang, Bin Gu:
Self-Adaptive Perturbation Radii for Adversarial Training. KDD 2023: 2570-2581 - [c53]Chenkang Zhang, Wanli Shi, Lei Luo, Bin Gu:
Doubly Robust AUC Optimization against Noisy and Adversarial Samples. KDD 2023: 3195-3205 - [c52]Jun Chen, Hong Chen, Bin Gu, Hao Deng:
Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization. NeurIPS 2023 - [c51]Bhaskar Mukhoty, Velibor Bojkovic, William de Vazelhes, Xiaohan Zhao, Giulia De Masi, Huan Xiong, Bin Gu:
Direct Training of SNN using Local Zeroth Order Method. NeurIPS 2023 - [c50]Ganyu Wang, Bin Gu, Qingsong Zhang, Xiang Li, Boyu Wang, Charles X. Ling:
A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning. NeurIPS 2023 - [c49]Xiaohan Zhao, Hualin Zhang, Zhouyuan Huo, Bin Gu:
Accelerated On-Device Forward Neural Network Training with Module-Wise Descending Asynchronism. NeurIPS 2023 - 2022
- [c48]Bin Gu, Chenkang Zhang, Huan Xiong, Heng Huang:
Balanced Self-Paced Learning for AUC Maximization. AAAI 2022: 6765-6773 - [c47]Diyang Li, Bin Gu:
Chunk Dynamic Updating for Group Lasso with ODEs. AAAI 2022: 7408-7416 - [c46]Junyi Li, Bin Gu, Heng Huang:
A Fully Single Loop Algorithm for Bilevel Optimization without Hessian Inverse. AAAI 2022: 7426-7434 - [c45]Runxue Bao, Bin Gu, Heng Huang:
An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification. CIKM 2022: 57-66 - [c44]Zhengqing Gao, Huimin Wu, Martin Takác, Bin Gu:
Towards Practical Large Scale Non-Linear Semi-Supervised Learning with Balancing Constraints. CIKM 2022: 3072-3081 - [c43]Bin Gu, Zhou Zhai, Xiang Li, Heng Huang:
Towards Fairer Classifier via True Fairness Score Path. CIKM 2022: 3113-3121 - [c42]Huimin Wu, William de Vazelhes, Bin Gu:
Efficient Semi-Supervised Adversarial Training without Guessing Labels. ICDM 2022: 538-547 - [c41]Alexander V. Gasnikov, Anton Novitskii, Vasilii Novitskii, Farshed Abdukhakimov, Dmitry Kamzolov, Aleksandr Beznosikov, Martin Takác, Pavel E. Dvurechensky, Bin Gu:
The power of first-order smooth optimization for black-box non-smooth problems. ICML 2022: 7241-7265 - [c40]Wanli Shi, Hongchang Gao, Bin Gu:
Gradient-Free Method for Heavily Constrained Nonconvex Optimization. ICML 2022: 19935-19955 - [c39]Ziran Xiong, Wanli Shi, Bin Gu:
End-to-End Semi-Supervised Ordinal Regression AUC Maximization with Convolutional Kernel Networks. KDD 2022: 2140-2150 - [c38]Xingyu Qu, Diyang Li, Xiaohan Zhao, Bin Gu:
GAGA: Deciphering Age-path of Generalized Self-paced Regularizer. NeurIPS 2022 - [c37]William de Vazelhes, Hualin Zhang, Huimin Wu, Xiaotong Yuan, Bin Gu:
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity. NeurIPS 2022 - [c36]Hualin Zhang, Huan Xiong, Bin Gu:
Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients. NeurIPS 2022 - 2021
- [c35]Zhouyuan Huo, Bin Gu, Heng Huang:
Large Batch Optimization for Deep Learning Using New Complete Layer-Wise Adaptive Rate Scaling. AAAI 2021: 7883-7890 - [c34]Wanli Shi, Bin Gu:
Improved Penalty Method via Doubly Stochastic Gradients for Bilevel Hyperparameter Optimization. AAAI 2021: 9621-9629 - [c33]Huimin Wu, Zhengmian Hu, Bin Gu:
Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients. AAAI 2021: 10329-10337 - [c32]Qingsong Zhang, Bin Gu, Cheng Deng, Heng Huang:
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating. AAAI 2021: 10896-10904 - [c31]Qingsong Zhang, Bin Gu, Zhiyuan Dang, Cheng Deng, Heng Huang:
Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm. CIKM 2021: 2598-2607 - [c30]Bin Gu, Zhou Zhai, Xiang Li, Heng Huang:
Finding Age Path of Self-Paced Learning. ICDM 2021: 151-160 - [c29]Qingsong Zhang, Bin Gu, Cheng Deng, Songxiang Gu, Liefeng Bo, Jian Pei, Heng Huang:
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization. KDD 2021: 3917-3927 - 2020
- [c28]Wanli Shi, Bin Gu, Xiang Li, Heng Huang:
Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization. AAAI 2020: 5734-5741 - [c27]Zhou Zhai, Bin Gu, Xiang Li, Heng Huang:
Safe Sample Screening for Robust Support Vector Machine. AAAI 2020: 6981-6988 - [c26]Runxue Bao, Bin Gu, Heng Huang:
Fast OSCAR and OWL Regression via Safe Screening Rules. ICML 2020: 653-663 - [c25]Wanli Shi, Victor S. Sheng, Xiang Li, Bin Gu:
Semi-Supervised Multi-Label Learning from Crowds via Deep Sequential Generative Model. KDD 2020: 1141-1149 - [c24]Bin Gu, Zhiyuan Dang, Xiang Li, Heng Huang:
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data. KDD 2020: 2483-2493 - 2019
- [c23]Feihu Huang, Bin Gu, Zhouyuan Huo, Songcan Chen, Heng Huang:
Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization. AAAI 2019: 1503-1510 - [c22]Bin Gu, Zhouyuan Huo, Heng Huang:
Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy. AAAI 2019: 3697-3704 - [c21]Runxue Bao, Bin Gu, Heng Huang:
Efficient Approximate Solution Path Algorithm for Order Weight L_1-Norm with Accuracy Guarantee. ICDM 2019: 958-963 - [c20]Bin Gu, Wenhan Xian, Heng Huang:
Asynchronous Stochastic Frank-Wolfe Algorithms for Non-Convex Optimization. IJCAI 2019: 737-743 - [c19]Xiang Geng, Bin Gu, Xiang Li, Wanli Shi, Guansheng Zheng, Heng Huang:
Scalable Semi-Supervised SVM via Triply Stochastic Gradients. IJCAI 2019: 2364-2370 - [c18]Wanli Shi, Bin Gu, Xiang Li, Xiang Geng, Heng Huang:
Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization. IJCAI 2019: 3418-3424 - [c17]Shuyang Yu, Bin Gu, Kunpeng Ning, Haiyan Chen, Jian Pei, Heng Huang:
Tackle Balancing Constraint for Incremental Semi-Supervised Support Vector Learning. KDD 2019: 1587-1595 - 2018
- [c16]Bin Gu, Miao Xin, Zhouyuan Huo, Heng Huang:
Asynchronous Doubly Stochastic Sparse Kernel Learning. AAAI 2018: 3085-3092 - [c15]Bin Gu, De Wang, Zhouyuan Huo, Heng Huang:
Inexact Proximal Gradient Methods for Non-Convex and Non-Smooth Optimization. AAAI 2018: 3093-3100 - [c14]Zhouyuan Huo, Bin Gu, Ji Liu, Heng Huang:
Accelerated Method for Stochastic Composition Optimization With Nonsmooth Regularization. AAAI 2018: 3287-3294 - [c13]Bin Gu, Zhouyuan Huo, Heng Huang:
Asynchronous Doubly Stochastic Group Regularized Learning. AISTATS 2018: 1791-1800 - [c12]Bin Gu, Zhouyuan Huo, Cheng Deng, Heng Huang:
Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines. ICML 2018: 1807-1816 - [c11]Zhouyuan Huo, Bin Gu, Qian Yang, Heng Huang:
Decoupled Parallel Backpropagation with Convergence Guarantee. ICML 2018: 2103-2111 - [c10]Bin Gu, Xingwang Ju, Xiang Li, Guansheng Zheng:
Faster Training Algorithms for Structured Sparsity-Inducing Norm. IJCAI 2018: 2163-2169 - [c9]Bin Gu, Yingying Shan, Xiang Geng, Guansheng Zheng:
Accelerated Asynchronous Greedy Coordinate Descent Algorithm for SVMs. IJCAI 2018: 2170-2176 - [c8]Bin Gu, Xiao-Tong Yuan, Songcan Chen, Heng Huang:
New Incremental Learning Algorithm for Semi-Supervised Support Vector Machine. KDD 2018: 1475-1484 - [c7]Zhouyuan Huo, Bin Gu, Heng Huang:
Training Neural Networks Using Features Replay. NeurIPS 2018: 6660-6669 - 2017
- [c6]Bin Gu, Guodong Liu, Heng Huang:
Groups-Keeping Solution Path Algorithm for Sparse Regression with Automatic Feature Grouping. KDD 2017: 185-193 - [c5]Xiang Li, Bin Gu, Shuang Ao, Huaimin Wang, Charles X. Ling:
Triply Stochastic Gradients on Multiple Kernel Learning. UAI 2017 - 2015
- [c4]Bin Gu, Charles X. Ling:
A New Generalized Error Path Algorithm for Model Selection. ICML 2015: 2549-2558 - [c3]Bin Gu, Victor S. Sheng, Shuo Li:
Bi-Parameter Space Partition for Cost-Sensitive SVM. IJCAI 2015: 3532-3539 - [c2]Xiang Li, Huaimin Wang, Bin Gu, Charles X. Ling:
Data Sparseness in Linear SVM. IJCAI 2015: 3628-3634 - 2008
- [c1]Bin Gu, Jiandong Wang, Haiyan Chen:
On-line off-line Ranking Support Vector Machine and analysis. IJCNN 2008: 1364-1369
Parts in Books or Collections
- 2020
- [p1]Zhiyuan Dang, Bin Gu, Heng Huang:
Large-Scale Kernel Method for Vertical Federated Learning. Federated Learning 2020: 66-80
Informal and Other Publications
- 2024
- [i53]Nan Yin, Mengzhu Wang, Zhenghan Chen, Giulia De Masi, Bin Gu, Huan Xiong:
Dynamic Spiking Graph Neural Networks. CoRR abs/2401.05373 (2024) - [i52]William de Vazelhes, Bhaskar Mukhoty, Xiao-Tong Yuan, Bin Gu:
Iterative Regularization with k-Support Norm: An Important Complement to Sparse Recovery. CoRR abs/2401.05394 (2024) - [i51]Hilal AlQuabeh, William de Vazelhes, Bin Gu:
Limited Memory Online Gradient Descent for Kernelized Pairwise Learning with Dynamic Averaging. CoRR abs/2402.01146 (2024) - [i50]Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang:
Federated Causal Discovery from Heterogeneous Data. CoRR abs/2402.13241 (2024) - [i49]Xiaofeng Wu, Velibor Bojkovic, Bin Gu, Kun Suo, Kai Zou:
FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion. CoRR abs/2403.18388 (2024) - [i48]Nan Yin, Mengzhu Wang, Li Shen, Hitesh Laxmichand Patel, Baopu Li, Bin Gu, Huan Xiong:
Continuous Spiking Graph Neural Networks. CoRR abs/2404.01897 (2024) - [i47]Mengnan Qi, Yufan Huang, Yongqiang Yao, Maoquan Wang, Bin Gu, Neel Sundaresan:
Is Next Token Prediction Sufficient for GPT? Exploration on Code Logic Comprehension. CoRR abs/2404.08885 (2024) - [i46]Chengqian Gao, William de Vazelhes, Hualin Zhang, Bin Gu, Zhiqiang Xu:
Hard-Thresholding Meets Evolution Strategies in Reinforcement Learning. CoRR abs/2405.01615 (2024) - [i45]Wanli Shi, Yi Chang, Bin Gu:
Double Momentum Method for Lower-Level Constrained Bilevel Optimization. CoRR abs/2406.17386 (2024) - [i44]Wanli Shi, Hongchang Gao, Bin Gu:
Gradient-Free Method for Heavily Constrained Nonconvex Optimization. CoRR abs/2409.00459 (2024) - 2023
- [i43]Bhaskar Mukhoty, Velibor Bojkovic, William de Vazelhes, Giulia De Masi, Huan Xiong, Bin Gu:
Energy Efficient Training of SNN using Local Zeroth Order Method. CoRR abs/2302.00910 (2023) - [i42]Jun Chen, Hong Chen, Xue Jiang, Bin Gu, Weifu Li, Tieliang Gong, Feng Zheng:
On the Stability and Generalization of Triplet Learning. CoRR abs/2302.09815 (2023) - [i41]Jiahuan Wang, Jun Chen, Hong Chen, Bin Gu, Weifu Li, Xin Tang:
Stability-based Generalization Analysis for Mixtures of Pointwise and Pairwise Learning. CoRR abs/2302.09967 (2023) - [i40]Shaoan Xie, Biwei Huang, Bin Gu, Tongliang Liu, Kun Zhang:
Advancing Counterfactual Inference through Quantile Regression. CoRR abs/2306.05751 (2023) - [i39]Ganyu Wang, Qingsong Zhang, Li Xiang, Boyu Wang, Bin Gu, Charles Ling:
Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimization. CoRR abs/2306.16077 (2023) - [i38]Hilal AlQuabeh, Bhaskar Mukhoty, Bin Gu:
Variance Reduced Online Gradient Descent for Kernelized Pairwise Learning with Limited Memory. CoRR abs/2310.06483 (2023) - [i37]Yufan Huang, Mengnan Qi, Yongqiang Yao, Maoquan Wang, Bin Gu, Colin B. Clement, Neel Sundaresan:
Program Translation via Code Distillation. CoRR abs/2310.11476 (2023) - [i36]Mengnan Qi, Yufan Huang, Maoquan Wang, Yongqiang Yao, Zihan Liu, Bin Gu, Colin B. Clement, Neel Sundaresan:
SUT: Active Defects Probing for Transcompiler Models. CoRR abs/2310.14209 (2023) - [i35]Yang Xu, Yongqiang Yao, Yufan Huang, Mengnan Qi, Maoquan Wang, Bin Gu, Neel Sundaresan:
Rethinking the Instruction Quality: LIFT is What You Need. CoRR abs/2312.11508 (2023) - 2022
- [i34]Qingsong Zhang, Bin Gu, Zhiyuan Dang, Cheng Deng, Heng Huang:
Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm. CoRR abs/2203.10329 (2022) - [i33]Yuzhen Han, Ruben Solozabal, Jing Dong, Xingyu Zhou, Martin Takác, Bin Gu:
Learning to Control under Time-Varying Environment. CoRR abs/2206.02507 (2022) - [i32]Hongchang Gao, Bin Gu, My T. Thai:
Stochastic Bilevel Distributed Optimization over a Network. CoRR abs/2206.15025 (2022) - [i31]Bin Gu, Chenkang Zhang, Huan Xiong, Heng Huang:
Balanced Self-Paced Learning for AUC Maximization. CoRR abs/2207.03650 (2022) - [i30]Runxue Bao, Bin Gu, Heng Huang:
An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification. CoRR abs/2208.06058 (2022) - [i29]Xingyu Qu, Diyang Li, Xiaohan Zhao, Bin Gu:
GAGA: Deciphering Age-path of Generalized Self-paced Regularizer. CoRR abs/2209.07063 (2022) - [i28]Hualin Zhang, Huan Xiong, Bin Gu:
Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients. CoRR abs/2210.01496 (2022) - [i27]William de Vazelhes, Hualin Zhang, Huimin Wu, Xiao-Tong Yuan, Bin Gu:
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity. CoRR abs/2210.05279 (2022) - [i26]Chenkang Zhang, Lei Luo, Bin Gu:
Denoising Multi-Similarity Formulation: A Self-paced Curriculum-Driven Approach for Robust Metric Learning. CoRR abs/2211.11751 (2022) - 2021
- [i25]Bin Gu, Guodong Liu, Yanfu Zhang, Xiang Geng, Heng Huang:
Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm. CoRR abs/2102.09026 (2021) - [i24]Qingsong Zhang, Bin Gu, Cheng Deng, Heng Huang:
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating. CoRR abs/2103.00958 (2021) - [i23]Zhou Zhai, Bin Gu, Heng Huang:
Learning Sampling Policy for Faster Derivative Free Optimization. CoRR abs/2104.04405 (2021) - [i22]Huimin Wu, Zhengmian Hu, Bin Gu:
Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients. CoRR abs/2107.09937 (2021) - [i21]Xiyuan Wei, Bin Gu, Heng Huang:
An Accelerated Variance-Reduced Conditional Gradient Sliding Algorithm for First-order and Zeroth-order Optimization. CoRR abs/2109.08858 (2021) - [i20]Qingsong Zhang, Bin Gu, Cheng Deng, Songxiang Gu, Liefeng Bo, Jian Pei, Heng Huang:
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization. CoRR abs/2109.12519 (2021) - [i19]Junyi Li, Bin Gu, Heng Huang:
A Fully Single Loop Algorithm for Bilevel Optimization without Hessian Inverse. CoRR abs/2112.04660 (2021) - 2020
- [i18]Zhouyuan Huo, Bin Gu, Heng Huang:
Large Batch Training Does Not Need Warmup. CoRR abs/2002.01576 (2020) - [i17]Zhouyuan Huo, Qian Yang, Bin Gu, Lawrence Carin, Heng Huang:
Faster On-Device Training Using New Federated Momentum Algorithm. CoRR abs/2002.02090 (2020) - [i16]Runxue Bao, Bin Gu, Heng Huang:
Fast OSCAR and OWL Regression via Safe Screening Rules. CoRR abs/2006.16433 (2020) - [i15]Bin Gu, Zhiyuan Dang, Xiang Li, Heng Huang:
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data. CoRR abs/2008.06197 (2020) - [i14]Bin Gu, An Xu, Zhouyuan Huo, Cheng Deng, Heng Huang:
Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning. CoRR abs/2008.06233 (2020) - [i13]Junyi Li, Bin Gu, Heng Huang:
Improved Bilevel Model: Fast and Optimal Algorithm with Theoretical Guarantee. CoRR abs/2009.00690 (2020) - 2019
- [i12]Feihu Huang, Bin Gu, Zhouyuan Huo, Songcan Chen, Heng Huang:
Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization. CoRR abs/1902.06158 (2019) - [i11]Xiang Geng, Bin Gu, Xiang Li, Wanli Shi, Guansheng Zheng, Heng Huang:
Scalable Semi-Supervised SVM via Triply Stochastic Gradients. CoRR abs/1907.11584 (2019) - [i10]Wanli Shi, Bin Gu, Xiang Li, Xiang Geng, Heng Huang:
Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization. CoRR abs/1907.12416 (2019) - [i9]Wanli Shi, Bin Gu, Xiang Li, Heng Huang:
Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization. CoRR abs/1912.11193 (2019) - [i8]Zhou Zhai, Bin Gu, Xiang Li, Heng Huang:
Safe Sample Screening for Robust Support Vector Machine. CoRR abs/1912.11217 (2019) - 2018
- [i7]Zhouyuan Huo, Bin Gu, Qian Yang, Heng Huang:
Decoupled Parallel Backpropagation with Convergence Guarantee. CoRR abs/1804.10574 (2018) - [i6]Zhouyuan Huo, Bin Gu, Heng Huang:
Training Neural Networks Using Features Replay. CoRR abs/1807.04511 (2018) - 2017
- [i5]Zhouyuan Huo, Bin Gu, Heng Huang:
Accelerated Method for Stochastic Composition Optimization with Nonsmooth Regularization. CoRR abs/1711.03937 (2017) - 2016
- [i4]Zhouyuan Huo, Bin Gu, Heng Huang:
Decoupled Asynchronous Proximal Stochastic Gradient Descent with Variance Reduction. CoRR abs/1609.06804 (2016) - [i3]Bin Gu, Zhouyuan Huo, Heng Huang:
Asynchronous Doubly Stochastic Proximal Optimization with Variance Reduction. CoRR abs/1610.09447 (2016) - [i2]Bin Gu, Zhouyuan Huo, Heng Huang:
Zeroth-order Asynchronous Doubly Stochastic Algorithm with Variance Reduction. CoRR abs/1612.01425 (2016) - [i1]Bin Gu, Zhouyuan Huo, Heng Huang:
Inexact Proximal Gradient Methods for Non-convex and Non-smooth Optimization. CoRR abs/1612.06003 (2016)
Coauthor Index
[j44] [j42] [j38] [j37] [j36] [j31] [j30] [j29] [j28] [j27] [j22] [j21] [j20] [c80] [c61] [c48] [c46] [c45] [c43] [c35] [c32] [c31] [c30] [c29] [c28] [c27] [c26] [c24] [c23] [c22] [c21] [c20] [c19] [c18] [c17] [c16] [c15] [c14] [c13] [c12] [c11] [c8] [c7] [c6] [p1] [i34] [i31] [i30] [i25] [i24] [i23] [i21] [i20] [i19] [i18] [i17] [i16] [i15] [i14] [i13] [i12] [i11] [i10] [i9] [i8] [i7] [i6] [i5] [i4] [i3] [i2] [i1]
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