default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
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.org/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
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 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) - [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 - [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
- [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) - [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 - [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
- [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) - [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 - [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
- [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) - [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 - [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
- [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) - [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 - [p1]Zhiyuan Dang, Bin Gu, Heng Huang:
Large-Scale Kernel Method for Vertical Federated Learning. Federated Learning 2020: 66-80 - [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)
2010 – 2019
- 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) - [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 - [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
- [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) - [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 - [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
- [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) - [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 - [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) - 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) - [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 - 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)
2000 – 2009
- 2008
- [c1]Bin Gu, Jiandong Wang, Haiyan Chen:
On-line off-line Ranking Support Vector Machine and analysis. IJCNN 2008: 1364-1369
Coauthor Index
[j44] [j42] [j38] [j37] [c80] [j36] [c61] [j31] [j30] [j29] [c48] [c46] [c45] [c43] [i34] [i31] [i30] [j28] [j27] [j22] [j21] [c35] [c32] [c31] [c30] [c29] [i25] [i24] [i23] [i21] [i20] [i19] [j20] [c28] [c27] [c26] [c24] [p1] [i18] [i17] [i16] [i15] [i14] [i13] [c23] [c22] [c21] [c20] [c19] [c18] [c17] [i12] [i11] [i10] [i9] [i8] [c16] [c15] [c14] [c13] [c12] [c11] [c8] [c7] [i7] [i6] [c6] [i5] [i4] [i3] [i2] [i1]
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-04 20:46 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
