Visual Tracking via Nonnegative Regularization Multiple Locality Coding. - Fanghui Liu 001; Tao Zhou 002; Jie Yang 002; Irene Y. H. Gu (ICCV Workshops, 2015)

Robust visual tracking via inverse nonnegative matrix factorization. - Fanghui Liu 001; Tao Zhou 002; Keren Fu; Irene Y. H. Gu; Jie Yang 002 (ICASSP, 2016)

Correlation filter tracking via bootstrap learning. - Kunqi Gu; Tao Zhou 002; Fanghui Liu 001; Jie Yang 002; Yu Qiao 003 (ICIP, 2016)

Incremental Robust Nonnegative Matrix Factorization for Object Tracking. - Fanghui Liu 001; Mingna Liu; Tao Zhou 002; Yu Qiao 003; Jie Yang 002 (ICONIP (2), 2016)

Geometric affine transformation estimation via correlation filter for visual tracking. - Fanghui Liu 001; Tao Zhou 002; Jie Yang 002 (Neurocomputing, 2016)

Robust visual tracking via constrained correlation filter coding. - Fanghui Liu 001; Tao Zhou 002; Keren Fu; Jie Yang 002 (Pattern Recognit. Lett., 2016)

Co-saliency detection via inter and intra saliency propagation. - Chenjie Ge; Keren Fu; Fanghui Liu 001; Li Bai 001; Jie Yang 002 (Signal Process. Image Commun., 2016)

Visual tracking via structural patch-based dictionary pair learning. - Tao Zhou 002; Fanghui Liu 001; Harish Bhaskar; Jie Yang 002; Lei Chen; Ping Cai (ICIP, 2017)

Robust Kernel Approximation for Classification. - Fanghui Liu 001; Xiaolin Huang; Cheng Peng 004; Jie Yang 002; Nikola K. Kasabov (ICONIP (1), 2017)

Correlation Filters with Adaptive Memories and Fusion for Visual Tracking. - Cheng Peng 004; Fanghui Liu 001; Haiyan Yang; Jie Yang 002; Nikola K. Kasabov (ICONIP (3), 2017)

Indefinite Kernel Logistic Regression. - Fanghui Liu 001; Xiaolin Huang; Jie Yang 002 (ACM Multimedia, 2017)

Online learning and joint optimization of combined spatial-temporal models for robust visual tracking. - Tao Zhou 002; Harish Bhaskar; Fanghui Liu 001; Jie Yang 002; Ping Cai (Neurocomputing, 2017)

Kernelized temporal locality learning for real-time visual tracking. - Fanghui Liu 001; Tao Zhou 002; Keren Fu; Jie Yang 002 (Pattern Recognit. Lett., 2017)

Graph Regularized and Locality-Constrained Coding for Robust Visual Tracking. - Tao Zhou 002; Harish Bhaskar; Fanghui Liu 001; Jie Yang 002 (IEEE Trans. Circuits Syst. Video Technol., 2017)

Visual Tracking via Nonnegative Multiple Coding. - Fanghui Liu 001; Chen Gong 002; Tao Zhou 002; Keren Fu; Xiangjian He; Jie Yang 002 (IEEE Trans. Multim., 2017)

Nonlinear Pairwise Layer and Its Training for Kernel Learning. - Fanghui Liu 001; Xiaolin Huang; Chen Gong 002; Jie Yang 002; Li Li 013 (AAAI, 2018)

Deep-PUMR: Deep Positive and Unlabeled Learning with Manifold Regularization. - Xingyu Chen; Fanghui Liu 001; Enmei Tu; Longbing Cao; Jie Yang 002 (ICONIP (1), 2018)

Learning Data-adaptive Nonparametric Kernels. - Fanghui Liu 001; Xiaolin Huang; Chen Gong 002; Jie Yang 002; Li Li 013 (CoRR, 2018)

Generalization Properties of hyper-RKHS and its Application to Out-of-Sample Extensions. - Fanghui Liu 001; Lei Shi 010; Xiaolin Huang; Jie Yang 002; Johan A. K. Suykens (CoRR, 2018)

Online discriminative dictionary learning for robust object tracking. - Tao Zhou 002; Fanghui Liu 001; Harish Bhaskar; Jie Yang 002; Huanlong Zhang; Ping Cai (Neurocomputing, 2018)

Densely Connected Discriminative Correlation Filters for Visual Tracking. - Cheng Peng 004; Fanghui Liu 001; Jie Yang 002; Nikola K. Kasabov (IEEE Signal Process. Lett., 2018)

Robust Visual Tracking via Dirac-Weighted Cascading Correlation Filters. - Cheng Peng 004; Fanghui Liu 001; Jie Yang 002; Nikola K. Kasabov (IEEE Signal Process. Lett., 2018)

Inverse Nonnegative Local Coordinate Factorization for Visual Tracking. - Fanghui Liu 001; Tao Zhou 002; Chen Gong 002; Keren Fu; Li Bai 001; Jie Yang 002 (IEEE Trans. Circuits Syst. Video Technol., 2018)

Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning. - Tao Zhou 002; Fanghui Liu 001; Harish Bhaskar; Jie Yang 002 (IEEE Trans. Cybern., 2018)

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching. - Fanghui Liu 001; Chen Gong 002; Xiaolin Huang; Tao Zhou 002; Jie Yang 002; Dacheng Tao (IEEE Trans. Image Process., 2018)

Dense Multi-focus Fusion Net: A Deep Unsupervised Convolutional Network for Multi-focus Image Fusion. - Hafiz Tayyab Mustafa; Fanghui Liu 001; Jie Yang 002; Zubair Khan; Qiao Huang (ICAISC (1), 2019)

Sparse Indefinite Kernel Learning on Sphere. - Jiaxuan Xie; Fanghui Liu 001; Xiaolin Huang; Jie Yang 002 (Aust. J. Intell. Inf. Process. Syst., 2019)

Deep Kernel Learning via Random Fourier Features. - Jiaxuan Xie; Fanghui Liu 001; Kaijie Wang; Xiaolin Huang (CoRR, 2019)

Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure. - Fanghui Liu 001; Xiaolin Huang; Chen Gong 002; Jie Yang 002; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2019)

Random Fourier Features via Fast Surrogate Leverage Weighted Sampling. - Fanghui Liu 001; Xiaolin Huang; Yudong Chen 001; Jie Yang 002; Johan A. K. Suykens (AAAI, 2020)

Generalizing Random Fourier Features via Generalized Measures. - Fanghui Liu 001; Xiaolin Huang; Yingyi Chen; Johan A. K. Suykens (CoRR, 2020)

Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces. - Fanghui Liu 001; Lei Shi 010; Xiaolin Huang; Jie Yang 002; Johan A. K. Suykens (CoRR, 2020)

Kernel regression in high dimension: Refined analysis beyond double descent. - Fanghui Liu 001; Zhenyu Liao 001; Johan A. K. Suykens (CoRR, 2020)

Learning Data-adaptive Non-parametric Kernels. - Fanghui Liu 001; Xiaolin Huang; Chen Gong 002; Jie Yang 002; Li Li 013 (J. Mach. Learn. Res., 2020)

A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels. - Fanghui Liu 001; Xiaolin Huang; Lei Shi 010; Jie Yang 002; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2020)

Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures. - Fanghui Liu 001; Xiaolin Huang; Yingyi Chen; Johan A. K. Suykens (AISTATS, 2021)

Kernel regression in high dimensions: Refined analysis beyond double descent. - Fanghui Liu 001; Zhenyu Liao 001; Johan A. K. Suykens (AISTATS, 2021)

Generalization Properties of hyper-RKHS and its Applications. - Fanghui Liu 001; Lei Shi 010; Xiaolin Huang; Jie Yang 002; Johan A. K. Suykens (J. Mach. Learn. Res., 2021)

Analysis of regularized least-squares in reproducing kernel Kreĭn spaces. - Fanghui Liu 001; Lei Shi 010; Xiaolin Huang; Jie Yang 002; Johan A. K. Suykens (Mach. Learn., 2021)

On the Double Descent of Random Features Models Trained with SGD. - Fanghui Liu 001; Johan A. K. Suykens; Volkan Cevher (NeurIPS, 2022)

Understanding Deep Neural Function Approximation in Reinforcement Learning via $ epsilon$-Greedy Exploration. - Fanghui Liu 001; Luca Viano; Volkan Cevher (NeurIPS, 2022)

Sound and Complete Verification of Polynomial Networks. - Elías Abad-Rocamora; Mehmet Fatih Sahin; Fanghui Liu 001; Grigorios Chrysos 002; Volkan Cevher (NeurIPS, 2022)

Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study. - Yongtao Wu; Zhenyu Zhu; Fanghui Liu 001; Grigorios Chrysos 002; Volkan Cevher (NeurIPS, 2022)

Generalization Properties of NAS under Activation and Skip Connection Search. - Zhenyu Zhu; Fanghui Liu 001; Grigorios Chrysos 002; Volkan Cevher (NeurIPS, 2022)

Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization). - Zhenyu Zhu; Fanghui Liu 001; Grigorios Chrysos 002; Volkan Cevher (NeurIPS, 2022)

Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration. - Fanghui Liu 001; Luca Viano; Volkan Cevher (CoRR, 2022)

Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond. - Fanghui Liu 001; Xiaolin Huang; Yudong Chen 001; Johan A. K. Suykens (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Towards a Unified Quadrature Framework for Large-Scale Kernel Machines. - Fanghui Liu 001; Xiaolin Huang; Yudong Chen 001; Johan A. K. Suykens (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

What can online reinforcement learning with function approximation benefit from general coverage conditions? - Fanghui Liu 001; Luca Viano; Volkan Cevher (ICML, 2023)

Benign Overfitting in Deep Neural Networks under Lazy Training. - Zhenyu Zhu; Fanghui Liu 001; Grigorios Chrysos 002; Francesco Locatello; Volkan Cevher (ICML, 2023)

Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks. - Jiayuan Ye 001; Zhenyu Zhu; Fanghui Liu 001; Reza Shokri; Volkan Cevher (NeurIPS, 2023)

On the Convergence of Encoder-only Shallow Transformers. - Yongtao Wu; Fanghui Liu 001; Grigorios Chrysos 002; Volkan Cevher (NeurIPS, 2023)

Provable benefits of general coverage conditions in efficient online RL with function approximation. - Fanghui Liu 001; Luca Viano; Volkan Cevher (CoRR, 2023)

End-to-end kernel learning via generative random Fourier features. - Kun Fang 004; Fanghui Liu 001; Xiaolin Huang; Jie Yang 002 (Pattern Recognit., 2023)

Federated Learning under Covariate Shifts with Generalization Guarantees. - Ali Ramezani-Kebrya; Fanghui Liu 001; Thomas Pethick; Grigorios Chrysos 002; Volkan Cevher (Trans. Mach. Learn. Res., 2023)

The Role of Over-Parameterization in Machine Learning - the Good, the Bad, the Ugly. - Fanghui Liu 001 (AAAI, 2024)

Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy. - Tao Li; Weisen Jiang; Fanghui Liu 001; Xiaolin Huang; James T. Kwok (ECCV (65), 2024)

Efficient local linearity regularization to overcome catastrophic overfitting. - Elías Abad-Rocamora; Fanghui Liu 001; Grigorios Chrysos 002; Pablo M. Olmos; Volkan Cevher (ICLR, 2024)

Generalization of Scaled Deep ResNets in the Mean-Field Regime. - Yihang Chen; Fanghui Liu 001; Yiping Lu; Grigorios Chrysos 002; Volkan Cevher (ICLR, 2024)

Robust NAS under adversarial training: benchmark, theory, and beyond. - Yongtao Wu; Fanghui Liu 001; Carl-Johann Simon-Gabriel; Grigorios Chrysos 002; Volkan Cevher (ICLR, 2024)

Revisiting Character-level Adversarial Attacks for Language Models. - Elías Abad-Rocamora; Yongtao Wu; Fanghui Liu 001; Grigorios Chrysos 002; Volkan Cevher (ICML, 2024)

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization. - Yihang Chen; Fanghui Liu 001; Taiji Suzuki; Volkan Cevher (ICML, 2024)

Can overfitted deep neural networks in adversarial training generalize? - An approximation viewpoint. - Zhongjie Shi; Fanghui Liu 001; Yuan Cao; Johan A. K. Suykens (CoRR, 2024)

Revisiting character-level adversarial attacks. - Elías Abad-Rocamora; Yongtao Wu; Fanghui Liu 001; Grigorios G. Chrysos; Volkan Cevher (CoRR, 2024)

Benign overfitting in Fixed Dimension via Physics-Informed Learning with Smooth Inductive Bias. - Honam Wong; Wendao Wu; Fanghui Liu 001; Yiping Lu (CoRR, 2024)

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks. - Fanghui Liu 001; Leello Tadesse Dadi; Volkan Cevher (J. Mach. Learn. Res., 2024)

Random fourier features for asymmetric kernels. - Mingzhen He; Fan He; Fanghui Liu 001; Xiaolin Huang (Mach. Learn., 2024)

How Gradient descent balances features: A dynamical analysis for two-layer neural networks. - Zhenyu Zhu; Fanghui Liu 001; Volkan Cevher (ICLR, 2025)

One-step full gradient suffices for low-rank fine-tuning, provably and efficiently. - Yuanhe Zhang; Fanghui Liu 001; Yudong Chen 001 (CoRR, 2025)

Re-examining Double Descent and Scaling Laws under Norm-based Capacity via Deterministic Equivalence. - Yichen Wang; Yudong Chen 001; Lorenzo Rosasco; Fanghui Liu 001 (CoRR, 2025)