On-line off-line Ranking Support Vector Machine and analysis. - Bin Gu 001; Jiandong Wang; Haiyan Chen 001 (IJCNN, 2008)

Ordinal-Class Core Vector Machine. - Bin Gu 001; Jiandong Wang; Tao Li (J. Comput. Sci. Technol., 2010)

Semi-supervised Distributed Clustering with Mahalanobis Distance Metric Learning. - Yuecheng Yu; Jiandong Wang; Guansheng Zheng; Bin Gu 001 (J. Digit. Content Technol. its Appl., 2010)

Accurate on-line v-support vector learning. - Bin Gu 001; Jiandong Wang; Yuecheng Yu; Guansheng Zheng; Yufan Huang; Tao Xu (Neural Networks, 2012)

Regularization Path for ν-Support Vector Classification. - Bin Gu 001; Jiandong Wang; Guansheng Zheng; Yuecheng Yu (IEEE Trans. Neural Networks Learn. Syst., 2012)

Feasibility and Finite Convergence Analysis for Accurate On-Line $ nu$-Support Vector Machine. - Bin Gu 001; Victor S. Sheng (IEEE Trans. Neural Networks Learn. Syst., 2013)

Cost-sensitive learning for defect escalation. - Victor S. Sheng; Bin Gu 001; Wei Fang 007; Jian Wu 002 (Knowl. Based Syst., 2014)

Direct Estimation of Cardiac Biventricular Volumes With an Adapted Bayesian Formulation. - Zhijie Wang 003; Mohamed Ben Salah; Bin Gu 001; Ali Islam; Aashish Goela; Shuo Li 001 (IEEE Trans. Biomed. Eng., 2014)

A New Generalized Error Path Algorithm for Model Selection. - Bin Gu 001; Charles X. Ling (ICML, 2015)

Bi-Parameter Space Partition for Cost-Sensitive SVM. - Bin Gu 001; Victor S. Sheng; Shuo Li 001 (IJCAI, 2015)

Data Sparseness in Linear SVM. - Xiang Li 012; Huaimin Wang; Bin Gu 001; Charles X. Ling (IJCAI, 2015)

Incremental learning for ν-Support Vector Regression. - Bin Gu 001; Victor S. Sheng; Zhijie Wang 003; Derek Ho; Said Osman; Shuo Li 001 (Neural Networks, 2015)

Incremental Support Vector Learning for Ordinal Regression. - Bin Gu 001; Victor S. Sheng; KengYeow Tay; Walter Romano; Shuo Li 001 (IEEE Trans. Neural Networks Learn. Syst., 2015)

Asynchronous Doubly Stochastic Proximal Optimization with Variance Reduction. - Bin Gu 001; Zhouyuan Huo; Heng Huang (CoRR, 2016)

Zeroth-order Asynchronous Doubly Stochastic Algorithm with Variance Reduction. - Bin Gu 001; Zhouyuan Huo; Heng Huang (CoRR, 2016)

Inexact Proximal Gradient Methods for Non-convex and Non-smooth Optimization. - Bin Gu 001; Zhouyuan Huo; Heng Huang (CoRR, 2016)

Decoupled Asynchronous Proximal Stochastic Gradient Descent with Variance Reduction. - Zhouyuan Huo; Bin Gu 001; Heng Huang (CoRR, 2016)

Groups-Keeping Solution Path Algorithm for Sparse Regression with Automatic Feature Grouping. - Bin Gu 001; Guodong Liu; Heng Huang (KDD, 2017)

Triply Stochastic Gradients on Multiple Kernel Learning. - Xiang Li 012; Bin Gu 001; Shuang Ao; Huaimin Wang; Charles X. Ling (UAI, 2017)

Accelerated Method for Stochastic Composition Optimization with Nonsmooth Regularization. - Zhouyuan Huo; Bin Gu 001; Heng Huang (CoRR, 2017)

Cross Validation Through Two-Dimensional Solution Surface for Cost-Sensitive SVM. - Bin Gu 001; Victor S. Sheng; KengYeow Tay; Walter Romano; Shuo Li 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

A Robust Regularization Path Algorithm for ν-Support Vector Classification. - Bin Gu 001; Victor S. Sheng (IEEE Trans. Neural Networks Learn. Syst., 2017)

Structural Minimax Probability Machine. - Bin Gu 001; Xingming Sun; Victor S. Sheng (IEEE Trans. Neural Networks Learn. Syst., 2017)

Inexact Proximal Gradient Methods for Non-Convex and Non-Smooth Optimization. - Bin Gu 001; De Wang; Zhouyuan Huo; Heng Huang (AAAI, 2018)

Asynchronous Doubly Stochastic Sparse Kernel Learning. - Bin Gu 001; Miao Xin; Zhouyuan Huo; Heng Huang (AAAI, 2018)

Accelerated Method for Stochastic Composition Optimization With Nonsmooth Regularization. - Zhouyuan Huo; Bin Gu 001; Ji Liu; Heng Huang (AAAI, 2018)

Asynchronous Doubly Stochastic Group Regularized Learning. - Bin Gu 001; Zhouyuan Huo; Heng Huang (AISTATS, 2018)

Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines. - Bin Gu 001; Zhouyuan Huo; Cheng Deng; Heng Huang (ICML, 2018)

Decoupled Parallel Backpropagation with Convergence Guarantee. - Zhouyuan Huo; Bin Gu 001; Qian Yang 003; Heng Huang (ICML, 2018)

Faster Training Algorithms for Structured Sparsity-Inducing Norm. - Bin Gu 001; Xingwang Ju; Xiang Li 012; Guansheng Zheng (IJCAI, 2018)

Accelerated Asynchronous Greedy Coordinate Descent Algorithm for SVMs. - Bin Gu 001; Yingying Shan; Xiang Geng; Guansheng Zheng (IJCAI, 2018)

New Incremental Learning Algorithm for Semi-Supervised Support Vector Machine. - Bin Gu 001; Xiao-Tong Yuan; Songcan Chen; Heng Huang (KDD, 2018)

Training Neural Networks Using Features Replay. - Zhouyuan Huo; Bin Gu 001; Heng Huang (NeurIPS, 2018)

The convergence of linear classifiers on large sparse data. - Xiang Li 012; Huaimin Wang; Bin Gu 001; Charles X. Ling (Neurocomputing, 2018)

Sparse regression with output correlation for cardiac ejection fraction estimation. - Bin Gu 001; Yingying Shan; Victor S. Sheng; Yuhui Zheng; Shuo Li 001 (Inf. Sci., 2018)

A regularization path algorithm for support vector ordinal regression. - Bin Gu 001 (Neural Networks, 2018)

Chunk incremental learning for cost-sensitive hinge loss support vector machine. - Bin Gu 001; Xin Quan; Yunhua Gu; Victor S. Sheng; Guansheng Zheng (Pattern Recognit., 2018)

A Solution Path Algorithm for General Parametric Quadratic Programming Problem. - Bin Gu 001; Victor S. Sheng (IEEE Trans. Neural Networks Learn. Syst., 2018)

Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy. - Bin Gu 001; Zhouyuan Huo; Heng Huang (AAAI, 2019)

Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization. - Feihu Huang 001; Bin Gu 001; Zhouyuan Huo; Songcan Chen; Heng Huang (AAAI, 2019)

Efficient Approximate Solution Path Algorithm for Order Weight L_1-Norm with Accuracy Guarantee. - Runxue Bao; Bin Gu 001; Heng Huang (ICDM, 2019)

Scalable Semi-Supervised SVM via Triply Stochastic Gradients. - Xiang Geng; Bin Gu 001; Xiang Li 012; Wanli Shi; Guansheng Zheng; Heng Huang (IJCAI, 2019)

Asynchronous Stochastic Frank-Wolfe Algorithms for Non-Convex Optimization. - Bin Gu 001; Wenhan Xian; Heng Huang (IJCAI, 2019)

Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization. - Wanli Shi; Bin Gu 001; Xiang Li 012; Xiang Geng; Heng Huang (IJCAI, 2019)

Tackle Balancing Constraint for Incremental Semi-Supervised Support Vector Learning. - Shuyang Yu; Bin Gu 001; Kunpeng Ning; Haiyan Chen 001; Jian Pei 001; Heng Huang (KDD, 2019)

Efficient inexact proximal gradient algorithms for structured sparsity-inducing norm. - Bin Gu 001; Xiang Geng; Xiang Li 012; Guansheng Zheng (Neural Networks, 2019)

Majority Voting and Pairing with Multiple Noisy Labeling. - Victor S. Sheng; Jing Zhang 015; Bin Gu 001; Xindong Wu 001 (IEEE Trans. Knowl. Data Eng., 2019)

Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization. - Wanli Shi; Bin Gu 001; Xiang Li 012; Heng Huang (AAAI, 2020)

Safe Sample Screening for Robust Support Vector Machine. - Zhou Zhai; Bin Gu 001; Xiang Li 012; Heng Huang (AAAI, 2020)

Fast OSCAR and OWL Regression via Safe Screening Rules. - Runxue Bao; Bin Gu 001; Heng Huang (ICML, 2020)

Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data. - Bin Gu 001; Zhiyuan Dang; Xiang Li 012; Heng Huang (KDD, 2020)

Semi-Supervised Multi-Label Learning from Crowds via Deep Sequential Generative Model. - Wanli Shi; Victor S. Sheng; Xiang Li 012; Bin Gu 001 (KDD, 2020)

Large Batch Training Does Not Need Warmup. - Zhouyuan Huo; Bin Gu 001; Heng Huang (CoRR, 2020)

Faster On-Device Training Using New Federated Momentum Algorithm. - Zhouyuan Huo; Qian Yang 003; Bin Gu 001; Lawrence Carin; Heng Huang (CoRR, 2020)

Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning. - Bin Gu 001; An Xu; Zhouyuan Huo; Cheng Deng; Heng Huang (CoRR, 2020)

Improved Bilevel Model: Fast and Optimal Algorithm with Theoretical Guarantee. - Junyi Li; Bin Gu 001; Heng Huang (CoRR, 2020)

A Unified q-Memorization Framework for Asynchronous Stochastic Optimization. - Bin Gu 001; Wenhan Xian; Zhouyuan Huo; Cheng Deng; Heng Huang (J. Mach. Learn. Res., 2020)

Large-Scale Kernel Method for Vertical Federated Learning. - Zhiyuan Dang; Bin Gu 001; Heng Huang (Federated Learning, 2020)

Large Batch Optimization for Deep Learning Using New Complete Layer-Wise Adaptive Rate Scaling. - Zhouyuan Huo; Bin Gu 001; Heng Huang (AAAI, 2021)

Improved Penalty Method via Doubly Stochastic Gradients for Bilevel Hyperparameter Optimization. - Wanli Shi; Bin Gu 001 (AAAI, 2021)

Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients. - Huimin Wu; Zhengmian Hu; Bin Gu 001 (AAAI, 2021)

Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating. - Qingsong Zhang; Bin Gu 001; Cheng Deng; Heng Huang (AAAI, 2021)

Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm. - Qingsong Zhang; Bin Gu 001; Zhiyuan Dang; Cheng Deng; Heng Huang (CIKM, 2021)

Finding Age Path of Self-Paced Learning. - Bin Gu 001; Zhou Zhai; Xiang Li 012; Heng Huang (ICDM, 2021)

AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization. - Qingsong Zhang; Bin Gu 001; Cheng Deng; Songxiang Gu; Liefeng Bo; Jian Pei 001; Heng Huang (KDD, 2021)

Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm. - Bin Gu 001; Guodong Liu; Yanfu Zhang; Xiang Geng; Heng Huang (CoRR, 2021)

Learning Sampling Policy for Faster Derivative Free Optimization. - Zhou Zhai; Bin Gu 001; Heng Huang (CoRR, 2021)

An Accelerated Variance-Reduced Conditional Gradient Sliding Algorithm for First-order and Zeroth-order Optimization. - Xiyuan Wei; Bin Gu 001; Heng Huang (CoRR, 2021)

Black-Box Reductions for Zeroth-Order Gradient Algorithms to Achieve Lower Query Complexity. - Bin Gu 001; Xiyuan Wei; Shangqian Gao; Ziran Xiong; Cheng Deng; Heng Huang (J. Mach. Learn. Res., 2021)

Triply stochastic gradient method for large-scale nonlinear similar unlabeled classification. - Wanli Shi; Bin Gu 001; Xiang Li 012; Cheng Deng; Heng Huang (Mach. Learn., 2021)

Solving large-scale support vector ordinal regression with asynchronous parallel coordinate descent algorithms. - Bin Gu 001; Xiang Geng; Wanli Shi; Yingying Shan; Yufang Huang; Zhijie Wang 003; Guansheng Zheng (Pattern Recognit., 2021)

Generalized error path algorithm. - Bin Gu 001; Charles X. Ling (Pattern Recognit., 2021)

A kernel path algorithm for general parametric quadratic programming problem. - Bin Gu 001; Ziran Xiong; Shuyang Yu; Guansheng Zheng (Pattern Recognit., 2021)

Accelerating Sequential Minimal Optimization via Stochastic Subgradient Descent. - Bin Gu 001; Yingying Shan; Xin Quan; Guansheng Zheng (IEEE Trans. Cybern., 2021)

Scalable Kernel Ordinal Regression via Doubly Stochastic Gradients. - Bin Gu 001; Xiang Geng; Xiang Li 012; Wanli Shi; Guansheng Zheng; Cheng Deng; Heng Huang (IEEE Trans. Neural Networks Learn. Syst., 2021)

Efficient Active Learning by Querying Discriminative and Representative Samples and Fully Exploiting Unlabeled Data. - Bin Gu 001; Zhou Zhai; Cheng Deng; Heng Huang (IEEE Trans. Neural Networks Learn. Syst., 2021)

Balanced Self-Paced Learning for AUC Maximization. - Bin Gu 001; Chenkang Zhang; Huan Xiong; Heng Huang (AAAI, 2022)

A Fully Single Loop Algorithm for Bilevel Optimization without Hessian Inverse. - Junyi Li; Bin Gu 001; Heng Huang (AAAI, 2022)

Chunk Dynamic Updating for Group Lasso with ODEs. - Diyang Li; Bin Gu 001 (AAAI, 2022)

Towards Fairer Classifier via True Fairness Score Path. - Bin Gu 001; Zhou Zhai; Xiang Li 012; Heng Huang (CIKM, 2022)

An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification. - Runxue Bao; Bin Gu 001; Heng Huang (CIKM, 2022)

Towards Practical Large Scale Non-Linear Semi-Supervised Learning with Balancing Constraints. - Zhengqing Gao; Huimin Wu; Martin Takác 001; Bin Gu 001 (CIKM, 2022)

Efficient Semi-Supervised Adversarial Training without Guessing Labels. - Huimin Wu; William de Vazelhes; Bin Gu 001 (ICDM, 2022)

The power of first-order smooth optimization for black-box non-smooth problems. - Alexander V. Gasnikov; Anton Novitskii; Vasilii Novitskii; Farshed Abdukhakimov; Dmitry Kamzolov; Aleksandr Beznosikov; Martin Takác 001; Pavel E. Dvurechensky; Bin Gu 001 (ICML, 2022)

Gradient-Free Method for Heavily Constrained Nonconvex Optimization. - Wanli Shi; Hongchang Gao; Bin Gu 001 (ICML, 2022)

End-to-End Semi-Supervised Ordinal Regression AUC Maximization with Convolutional Kernel Networks. - Ziran Xiong; Wanli Shi; Bin Gu 001 (KDD, 2022)

GAGA: Deciphering Age-path of Generalized Self-paced Regularizer. - Xingyu Qu; Diyang Li; Xiaohan Zhao; Bin Gu 001 (NeurIPS, 2022)

Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity. - William de Vazelhes; Hualin Zhang; Huimin Wu; Xiaotong Yuan; Bin Gu 001 (NeurIPS, 2022)

Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients. - Hualin Zhang; Huan Xiong; Bin Gu 001 (NeurIPS, 2022)

Learning to Control under Time-Varying Environment. - Yuzhen Han; Ruben Solozabal; Jing Dong; Xingyu Zhou; Martin Takác 001; Bin Gu 001 (CoRR, 2022)

Stochastic Bilevel Distributed Optimization over a Network. - Hongchang Gao; Bin Gu 001; My T. Thai (CoRR, 2022)

Incremental learning algorithm for large-scale semi-supervised ordinal regression. - Haiyan Chen 001; Yizhen Jia; Jiaming Ge; Bin Gu 001 (Neural Networks, 2022)

Large-Scale Nonlinear AUC Maximization via Triply Stochastic Gradients. - Zhiyuan Dang; Xiang Li 012; Bin Gu 001; Cheng Deng; Heng Huang (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Scaling Up Generalized Kernel Methods. - Bin Gu 001; Zhiyuan Dang; Zhouyuan Huo; Cheng Deng; Heng Huang (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Privacy-Preserving Asynchronous Vertical Federated Learning Algorithms for Multiparty Collaborative Learning. - Bin Gu 001; An Xu; Zhouyuan Huo; Cheng Deng; Heng Huang (IEEE Trans. Neural Networks Learn. Syst., 2022)

On the Stability and Generalization of Triplet Learning. - Jun Chen; Hong Chen 004; Xue Jiang; Bin Gu 001; Weifu Li; Tieliang Gong; Feng Zheng (AAAI, 2023)

When Online Learning Meets ODE: Learning without Forgetting on Variable Feature Space. - Diyang Li; Bin Gu 001 (AAAI, 2023)

Stability-Based Generalization Analysis for Mixtures of Pointwise and Pairwise Learning. - Jiahuan Wang; Jun Chen; Hong Chen 004; Bin Gu 001; Weifu Li; Xin Tang (AAAI, 2023)

Faster Fair Machine via Transferring Fairness Constraints to Virtual Samples. - Zhou Zhai; Lei Luo 001; Heng Huang; Bin Gu 001 (AAAI, 2023)

Denoising Multi-Similarity Formulation: A Self-Paced Curriculum-Driven Approach for Robust Metric Learning. - Chenkang Zhang; Lei Luo 001; Bin Gu 001 (AAAI, 2023)

Variance Reduced Online Gradient Descent for Kernelized Pairwise Learning with Limited Memory. - Hilal AlQuabeh; Bhaskar Mukhoty; Bin Gu 001 (ACML, 2023)

On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network. - Hongchang Gao; Bin Gu 001; My T. Thai (AISTATS, 2023)

Program Translation via Code Distillation. - Yufan Huang; Mengnan Qi; Yongqiang Yao; Maoquan Wang; Bin Gu 001; Colin B. Clement; Neel Sundaresan (EMNLP, 2023)

SUT: Active Defects Probing for Transcompiler Models. - Mengnan Qi; Yufan Huang; Maoquan Wang; Yongqiang Yao; Zihan Liu; Bin Gu 001; Colin B. Clement; Neel Sundaresan (EMNLP, 2023)

Faster Gradient-Free Methods for Escaping Saddle Points. - Hualin Zhang; Bin Gu 001 (ICLR, 2023)

A Unified Optimization Framework of ANN-SNN Conversion: Towards Optimal Mapping from Activation Values to Firing Rates. - Haiyan Jiang; Srinivas Anumasa; Giulia De Masi; Huan Xiong; Bin Gu 001 (ICML, 2023)

Self-Adaptive Perturbation Radii for Adversarial Training. - Huimin Wu; Wanli Shi; Chenkang Zhang; Bin Gu 001 (KDD, 2023)

Doubly Robust AUC Optimization against Noisy and Adversarial Samples. - Chenkang Zhang; Wanli Shi; Lei Luo 001; Bin Gu 001 (KDD, 2023)

Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization. - Jun Chen; Hong Chen 004; Bin Gu 001; Hao Deng (NeurIPS, 2023)

Direct Training of SNN using Local Zeroth Order Method. - Bhaskar Mukhoty; Velibor Bojkovic; William de Vazelhes; Xiaohan Zhao; Giulia De Masi; Huan Xiong; Bin Gu 001 (NeurIPS, 2023)

A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning. - Ganyu Wang; Bin Gu 001; Qingsong Zhang; Xiang Li 012; Boyu Wang 004; Charles X. Ling (NeurIPS, 2023)

Accelerated On-Device Forward Neural Network Training with Module-Wise Descending Asynchronism. - Xiaohan Zhao; Hualin Zhang; Zhouyuan Huo; Bin Gu 001 (NeurIPS, 2023)

Energy Efficient Training of SNN using Local Zeroth Order Method. - Bhaskar Mukhoty; Velibor Bojkovic; William de Vazelhes; Giulia De Masi; Huan Xiong; Bin Gu 001 (CoRR, 2023)

Advancing Counterfactual Inference through Quantile Regression. - Shaoan Xie; Biwei Huang; Bin Gu 001; Tongliang Liu; Kun Zhang 001 (CoRR, 2023)

Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimization. - Ganyu Wang; Qingsong Zhang; Li Xiang; Boyu Wang 004; Bin Gu 001; Charles Ling 001 (CoRR, 2023)

Rethinking the Instruction Quality: LIFT is What You Need. - Yang Xu; Yongqiang Yao; Yufan Huang; Mengnan Qi; Maoquan Wang; Bin Gu 001; Neel Sundaresan (CoRR, 2023)

Incremental learning for transductive support vector machine. - Haiyan Chen 001; Ying Yu; Yizhen Jia; Bin Gu 001 (Pattern Recognit., 2023)

Kernel Path for ν-Support Vector Classification. - Bin Gu 001; Ziran Xiong; Xiang Li 012; Zhou Zhai; Guansheng Zheng (IEEE Trans. Neural Networks Learn. Syst., 2023)

A new large-scale learning algorithm for generalized additive models. - Bin Gu 001; Chenkang Zhang; Zhouyuan Huo; Heng Huang (Mach. Learn., 2023)

Kernel Error Path Algorithm. - Ziran Xiong; Charles X. Ling; Bin Gu 001 (IEEE Trans. Neural Networks Learn. Syst., 2023)

Limited Memory Online Gradient Descent for Kernelized Pairwise Learning with Dynamic Averaging. - Hilal AlQuabeh; William de Vazelhes; Bin Gu 001 (AAAI, 2024)

Enhancing Training of Spiking Neural Network with Stochastic Latency. - Srinivas Anumasa; Bhaskar Mukhoty; Velibor Bojkovic; Giulia De Masi; Huan Xiong; Bin Gu 001 (AAAI, 2024)

Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery. - William de Vazelhes; Bhaskar Mukhoty; Xiao-Tong Yuan; Bin Gu 001 (AAAI, 2024)

Dynamic Spiking Graph Neural Networks. - Nan Yin; Mengzhu Wang; Zhenghan Chen; Giulia De Masi; Huan Xiong; Bin Gu 001 (AAAI, 2024)

Data Driven Threshold and Potential Initialization for Spiking Neural Networks. - Velibor Bojkovic; Srinivas Anumasa; Giulia De Masi; Bin Gu 001; Huan Xiong (AISTATS, 2024)

Fast and Adversarial Robust Kernelized SDU Learning. - Yajing Fan; Wanli Shi; Yi Chang 001; Bin Gu 001 (AISTATS, 2024)

Learning Sampling Policy to Achieve Fewer Queries for Zeroth-Order Optimization. - Zhou Zhai; Wanli Shi; Heng Huang; Yi Chang 001; Bin Gu 001 (AISTATS, 2024)

FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion. - Xiaofeng Wu 002; Velibor Bojkovic; Bin Gu 001; Kun Suo; Kai Zou (ECCV (69), 2024)

Exploring Vulnerabilities in Spiking Neural Networks: Direct Adversarial Attacks on Raw Event Data. - Yanmeng Yao; Xiaohan Zhao; Bin Gu 001 (ECCV (72), 2024)

TAB: Temporal Accumulated Batch Normalization in Spiking Neural Networks. - Haiyan Jiang; Vincent Zoonekynd; Giulia De Masi; Bin Gu 001; Huan Xiong (ICLR, 2024)

Learning No-Regret Sparse Generalized Linear Models with Varying Observation(s). - Diyang Li; Charles Ling 001; Zhiqiang Xu; Huan Xiong; Bin Gu 001 (ICLR, 2024)

Federated Causal Discovery from Heterogeneous Data. - Loka Li; Ignavier Ng; Gongxu Luo; Biwei Huang; Guangyi Chen 002; Tongliang Liu; Bin Gu 001; Kun Zhang 001 (ICLR, 2024)

General Stability Analysis for Zeroth-Order Optimization Algorithms. - Xinyue Liu; Hualin Zhang; Bin Gu 001; Hong Chen 004 (ICLR, 2024)

Certified Adversarial Robustness for Rate Encoded Spiking Neural Networks. - Bhaskar Mukhoty; Hilal AlQuabeh; Giulia De Masi; Huan Xiong; Bin Gu 001 (ICLR, 2024)

DREAM: Dual Structured Exploration with Mixup for Open-set Graph Domain Adaption. - Nan Yin; Mengzhu Wang; Zhenghan Chen; Li Shen 008; Huan Xiong; Bin Gu 001; Xiao Luo 001 (ICLR, 2024)

New Insight of Variance reduce in Zero-Order Hard-Thresholding: Mitigating Gradient Error and Expansivity Contradictions. - Xinzhe Yuan; William de Vazelhes; Bin Gu 001; Huan Xiong (ICLR, 2024)

NDOT: Neuronal Dynamics-based Online Training for Spiking Neural Networks. - Haiyan Jiang; Giulia De Masi; Huan Xiong; Bin Gu 001 (ICML, 2024)

Double Momentum Method for Lower-Level Constrained Bilevel Optimization. - Wanli Shi; Yi Chang 001; Bin Gu 001 (ICML, 2024)

Hard-Thresholding Meets Evolution Strategies in Reinforcement Learning. - Chengqian Gao; William de Vazelhes; Hualin Zhang; Bin Gu 001; Zhiqiang Xu (IJCAI, 2024)

Fine-grained Analysis of Stability and Generalization for Stochastic Bilevel Optimization. - Xuelin Zhang; Hong Chen 004; Bin Gu 001; Tieliang Gong; Feng Zheng (IJCAI, 2024)

Asynchronous Vertical Federated Learning for Kernelized AUC Maximization. - Ke Zhang; Ganyu Wang; Han Li; Yulong Wang 002; Hong Chen 004; Bin Gu 001 (KDD, 2024)

Subspace Selection based Prompt Tuning with Nonconvex Nonsmooth Black-Box Optimization. - Haozhen Zhang; Hualin Zhang; Bin Gu 001; Yi Chang 001 (KDD, 2024)

How Does Black-Box Impact the Learning Guarantee of Stochastic Compositional Optimization? - Jun Chen; Hong Chen 004; Bin Gu 001 (NeurIPS, 2024)

Continuous Spiking Graph Neural Networks. - Nan Yin; Mengzhu Wang; Li Shen 008; Hitesh Laxmichand Patel; Baopu Li; Bin Gu 001; Huan Xiong (CoRR, 2024)

Is Next Token Prediction Sufficient for GPT? Exploration on Code Logic Comprehension. - Mengnan Qi; Yufan Huang; Yongqiang Yao; Maoquan Wang; Bin Gu 001; Neel Sundaresan (CoRR, 2024)

Error Density-dependent Empirical Risk Minimization. - Hong Chen 004; Xuelin Zhang; Tieliang Gong; Bin Gu 001; Feng Zheng (Expert Syst. Appl., 2024)

On the Intrinsic Structures of Spiking Neural Networks. - Shao-Qun Zhang; Jia-Yi Chen; Jin-Hui Wu; Gao Zhang; Huan Xiong; Bin Gu 001; Zhi-Hua Zhou (J. Mach. Learn. Res., 2024)

Perturbation diversity certificates robust generalization. - Zhuang Qian; Shufei Zhang; Kaizhu Huang; Qiufeng Wang 001; Xinping Yi; Bin Gu 001; Huan Xiong (Neural Networks, 2024)

On the Number of Linear Regions of Convolutional Neural Networks With Piecewise Linear Activations. - Huan Xiong; Lei Huang 015; Wenston J. T. Zang; Xiantong Zhen; Guo-Sen Xie; Bin Gu 001; Le Song (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Kernel Path for Semisupervised Support Vector Machine. - Zhou Zhai; Heng Huang; Bin Gu 001 (IEEE Trans. Neural Networks Learn. Syst., 2024)

Messages are Never Propagated Alone: Collaborative Hypergraph Neural Network for Time-Series Forecasting. - Nan Yin; Li Shen 008; Huan Xiong; Bin Gu 001; Chong Chen 002; Xian-Sheng Hua 001; Siwei Liu 001; Xiao Luo 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Tackle balancing constraints in semi-supervised ordinal regression. - Chenkang Zhang; Heng Huang; Bin Gu 001 (Mach. Learn., 2024)

Obtaining Lower Query Complexities Through Lightweight Zeroth-Order Proximal Gradient Algorithms. - Bin Gu 001; Xiyuan Wei; Hualin Zhang; Yi Chang 001; Heng Huang (Neural Comput., 2024)

Asynchronous Parallel Large-Scale Gaussian Process Regression. - Zhiyuan Dang; Bin Gu 001; Cheng Deng; Heng Huang (IEEE Trans. Neural Networks Learn. Syst., 2024)

Secure and fast asynchronous Vertical Federated Learning via cascaded hybrid optimization. - Ganyu Wang; Qingsong Zhang; Xiang Li 012; Boyu Wang 004; Bin Gu 001; Charles X. Ling (Mach. Learn., 2024)

Inter-feature Relationship Certifies Robust Generalization of Adversarial Training. - Shufei Zhang; Zhuang Qian; Kaizhu Huang; Qiu-Feng Wang 001; Bin Gu 001; Huan Xiong; Xinping Yi (Int. J. Comput. Vis., 2024)

New Scalable and Efficient Online Pairwise Learning Algorithm. - Bin Gu 001; Runxue Bao; Chenkang Zhang; Heng Huang (IEEE Trans. Neural Networks Learn. Syst., 2024)

Error Analysis Affected by Heavy-Tailed Gradients for Non-Convex Pairwise Stochastic Gradient Descent. - Jun Chen; Hong Chen 004; Bin Gu 001; Guodong Liu; Yingjie Wang 007; Weifu Li (AAAI, 2025)

Leveraging First and Zeroth-Order Gradient to Address Imbalanced Black-Box Prompt Tuning via Minimax Optimization. - Haozhen Zhang; Zhaogeng Liu; Bin Gu 001; Yi Chang 001 (AAAI, 2025)

Query Efficient Black-Box Visual Prompting with Subspace Learning. - Zhaogeng Liu; Haozhen Zhang; Hualin Zhang; Xingchen Li; Wanli Shi; Bin Gu 001; Yi Chang 001 (CVPR, 2025)

Improving Generalization and Robustness in SNNs Through Signed Rate Encoding and Sparse Encoding Attacks. - Bhaskar Mukhoty; Hilal AlQuabeh; Bin Gu 001 (ICLR, 2025)

Event-Driven Online Vertical Federated Learning. - Ganyu Wang; Boyu Wang 004; Bin Gu 001; Charles Ling 001 (ICLR, 2025)

Collaborative Discrete-Continuous Black-Box Prompt Learning for Language Models. - Hualin Zhang; Haozhen Zhang; Zhekai Liu; Bin Gu 001; Yi Chang 001 (ICLR, 2025)

Enhancing Black-Box Adversarial Attacks on Discrete Sequential Data via Bilevel Bayesian Optimization in Hybrid Spaces. - Tianxing Man; Xingchen Li; Zhaogeng Liu; Haozhen Zhang; Bin Gu 001; Yi Chang 001 (KDD (1), 2025)

Temporal Misalignment in ANN-SNN Conversion and Its Mitigation via Probabilistic Spiking Neurons. - Velibor Bojkovic; Xiaofeng Wu 002; Bin Gu 001 (CoRR, 2025)

Global Model Selection for Semi-Supervised Support Vector Machine via Solution Paths. - Yajing Fan; Shuyang Yu; Bin Gu 001; Ziran Xiong; Zhou Zhai; Heng Huang; Yi Chang 001 (IEEE Trans. Neural Networks Learn. Syst., 2025)

Global Model Selection via Solution Paths for Robust Support Vector Machine. - Zhou Zhai; Bin Gu 001; Cheng Deng; Heng Huang (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

Stagewise Training With Exponentially Growing Training Sets. - Bin Gu 001; Hilal AlQuabeh; William de Vazelhes; Zhouyuan Huo; Heng Huang (IEEE Trans. Neural Networks Learn. Syst., 2025)

Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees. - William de Vazelhes; Xiao-Tong Yuan; Bin Gu 001 (CoRR, 2025)

FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning. - Ganyu Wang; Jinjie Fang; Maxwell J. Yin; Bin Gu 001; Xi Chen; Boyu Wang 004; Charles X. Ling (CoRR, 2025)

Generalization Bounds of Deep Neural Networks With τ-Mixing Samples. - Liyuan Liu; Yaohui Chen 002; Weifu Li; Yingjie Wang 007; Bin Gu 001; Feng Zheng; Hong Chen 004 (IEEE Trans. Neural Networks Learn. Syst., 2025)