Fanghui Liu

96 papers A* 21A 2B 2Misc 1Journal 63Unranked 7
YearRankTypeTitle / Venue / Authors
2026 J jnl
Statistical Learning Theory in Lean 4: Empirical Processes from Scratch.
CoRR
Yuanhe Zhang, Jason D. Lee, Fanghui Liu
2025 J jnl
DAG-Math: Graph-Guided Mathematical Reasoning in LLMs.
CoRR
Yuanhe Zhang, Ilja Kuzborskij, Jason D. Lee, Chenlei Leng, Fanghui Liu
2025 A* conf
How Gradient descent balances features: A dynamical analysis for two-layer neural networks.
ICLR
Zhenyu Zhu, Fanghui Liu, Volkan Cevher
2025 A* conf
LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently.
ICML
Yuanhe Zhang, Fanghui Liu, Yudong Chen
2025 J jnl
One-step full gradient suffices for low-rank fine-tuning, provably and efficiently.
CoRR
Yuanhe Zhang, Fanghui Liu, Yudong Chen
2025 J jnl
Re-examining Double Descent and Scaling Laws under Norm-based Capacity via Deterministic Equivalence.
CoRR
Yichen Wang, Yudong Chen, Lorenzo Rosasco, Fanghui Liu
2024 J jnl
Benign overfitting in Fixed Dimension via Physics-Informed Learning with Smooth Inductive Bias.
CoRR
Honam Wong, Wendao Wu, Fanghui Liu, Yiping Lu
2024 J jnl
Can overfitted deep neural networks in adversarial training generalize? - An approximation viewpoint.
CoRR
Zhongjie Shi, Fanghui Liu, Yuan Cao, Johan A. K. Suykens
2024 A* conf
Efficient local linearity regularization to overcome catastrophic overfitting.
ICLR
Elías Abad-Rocamora, Fanghui Liu, Grigorios Chrysos, Pablo M. Olmos, Volkan Cevher
2024 J jnl
Efficient local linearity regularization to overcome catastrophic overfitting.
CoRR
Elías Abad-Rocamora, Fanghui Liu, Grigorios G. Chrysos, Pablo M. Olmos, Volkan Cevher
2024 A* conf
Generalization of Scaled Deep ResNets in the Mean-Field Regime.
ICLR
Yihang Chen, Fanghui Liu, Yiping Lu, Grigorios Chrysos, Volkan Cevher
2024 J jnl
Generalization of Scaled Deep ResNets in the Mean-Field Regime.
CoRR
Yihang Chen, Fanghui Liu, Yiping Lu, Grigorios G. Chrysos, Volkan Cevher
2024 A* conf
High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization.
ICML
Yihang Chen, Fanghui Liu, Taiji Suzuki, Volkan Cevher
2024 J jnl
High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization.
CoRR
Yihang Chen, Fanghui Liu, Taiji Suzuki, Volkan Cevher
2024 — conf
Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy.
ECCV (65)
Tao Li, Weisen Jiang, Fanghui Liu, Xiaolin Huang, James T. Kwok
2024 J jnl
Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy.
CoRR
Tao Li, Weisen Jiang, Fanghui Liu, Xiaolin Huang, James T. Kwok
2024 J jnl
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks.
CoRR
Fanghui Liu, Leello Tadesse Dadi, Volkan Cevher
2024 J jnl
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks.
J. Mach. Learn. Res.
Fanghui Liu, Leello Tadesse Dadi, Volkan Cevher
2024 J jnl
Random fourier features for asymmetric kernels.
Mach. Learn.
Mingzhen He, Fan He, Fanghui Liu, Xiaolin Huang
2024 A* conf
Revisiting Character-level Adversarial Attacks for Language Models.
ICML
Elías Abad-Rocamora, Yongtao Wu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher
2024 J jnl
Revisiting character-level adversarial attacks.
CoRR
Elías Abad-Rocamora, Yongtao Wu, Fanghui Liu, Grigorios G. Chrysos, Volkan Cevher
2024 A* conf
Robust NAS under adversarial training: benchmark, theory, and beyond.
ICLR
Yongtao Wu, Fanghui Liu, Carl-Johann Simon-Gabriel, Grigorios Chrysos, Volkan Cevher
2024 J jnl
Robust NAS under adversarial training: benchmark, theory, and beyond.
CoRR
Yongtao Wu, Fanghui Liu, Carl-Johann Simon-Gabriel, Grigorios G. Chrysos, Volkan Cevher
2024 A* conf
The Role of Over-Parameterization in Machine Learning - the Good, the Bad, the Ugly.
AAAI
Fanghui Liu
2023 A* conf
Benign Overfitting in Deep Neural Networks under Lazy Training.
ICML
Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Francesco Locatello, Volkan Cevher
2023 J jnl
Benign Overfitting in Deep Neural Networks under Lazy Training.
CoRR
Zhenyu Zhu, Fanghui Liu, Grigorios G. Chrysos, Francesco Locatello, Volkan Cevher
2023 J jnl
End-to-end kernel learning via generative random Fourier features.
Pattern Recognit.
Kun Fang, Fanghui Liu, Xiaolin Huang, Jie Yang
2023 J jnl
Federated Learning under Covariate Shifts with Generalization Guarantees.
CoRR
Ali Ramezani-Kebrya, Fanghui Liu, Thomas Pethick, Grigorios Chrysos, Volkan Cevher
2023 J jnl
Federated Learning under Covariate Shifts with Generalization Guarantees.
Trans. Mach. Learn. Res.
Ali Ramezani-Kebrya, Fanghui Liu, Thomas Pethick, Grigorios Chrysos, Volkan Cevher
2023 A* conf
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks.
NeurIPS
Jiayuan Ye, Zhenyu Zhu, Fanghui Liu, Reza Shokri, Volkan Cevher
2023 J jnl
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks.
CoRR
Jiayuan Ye, Zhenyu Zhu, Fanghui Liu, Reza Shokri, Volkan Cevher
2023 A* conf
On the Convergence of Encoder-only Shallow Transformers.
NeurIPS
Yongtao Wu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher
2023 J jnl
On the Convergence of Encoder-only Shallow Transformers.
CoRR
Yongtao Wu, Fanghui Liu, Grigorios G. Chrysos, Volkan Cevher
2023 J jnl
Provable benefits of general coverage conditions in efficient online RL with function approximation.
CoRR
Fanghui Liu, Luca Viano, Volkan Cevher
2023 A* conf
What can online reinforcement learning with function approximation benefit from general coverage conditions?
ICML
Fanghui Liu, Luca Viano, Volkan Cevher
2022 A* conf
Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study.
NeurIPS
Yongtao Wu, Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher
2022 J jnl
Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study.
CoRR
Yongtao Wu, Zhenyu Zhu, Fanghui Liu, Grigorios G. Chrysos, Volkan Cevher
2022 A* conf
Generalization Properties of NAS under Activation and Skip Connection Search.
NeurIPS
Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher
2022 J jnl
Generalization Properties of NAS under Activation and Skip Connection Search.
CoRR
Zhenyu Zhu, Fanghui Liu, Grigorios G. Chrysos, Volkan Cevher
2022 A* conf
On the Double Descent of Random Features Models Trained with SGD.
NeurIPS
Fanghui Liu, Johan A. K. Suykens, Volkan Cevher
2022 J jnl
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond.
IEEE Trans. Pattern Anal. Mach. Intell.
Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens
2022 J jnl
Random Fourier Features for Asymmetric Kernels.
CoRR
Mingzhen He, Fan He, Fanghui Liu, Xiaolin Huang
2022 A* conf
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization).
NeurIPS
Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher
2022 J jnl
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization).
CoRR
Zhenyu Zhu, Fanghui Liu, Grigorios G. Chrysos, Volkan Cevher
2022 A* conf
Sound and Complete Verification of Polynomial Networks.
NeurIPS
Elías Abad-Rocamora, Mehmet Fatih Sahin, Fanghui Liu, Grigorios Chrysos, Volkan Cevher
2022 J jnl
Sound and Complete Verification of Polynomial Networks.
CoRR
Elías Abad-Rocamora, Mehmet Fatih Sahin, Fanghui Liu, Grigorios G. Chrysos, Volkan Cevher
2022 J jnl
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines.
IEEE Trans. Pattern Anal. Mach. Intell.
Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens
2022 A* conf
Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration.
NeurIPS
Fanghui Liu, Luca Viano, Volkan Cevher
2022 J jnl
Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration.
CoRR
Fanghui Liu, Luca Viano, Volkan Cevher
2021 J jnl
Analysis of regularized least-squares in reproducing kernel Kreĭn spaces.
Mach. Learn.
Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens
2021 A conf
Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures.
AISTATS
Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens
2021 J jnl
Generalization Properties of hyper-RKHS and its Applications.
J. Mach. Learn. Res.
Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens
2021 A conf
Kernel regression in high dimensions: Refined analysis beyond double descent.
AISTATS
Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens
2021 J jnl
On the Double Descent of Random Features Models Trained with SGD.
CoRR
Fanghui Liu, Johan A. K. Suykens, Volkan Cevher
2020 J jnl
A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels.
IEEE Trans. Neural Networks Learn. Syst.
Fanghui Liu, Xiaolin Huang, Lei Shi, Jie Yang, Johan A. K. Suykens
2020 J jnl
Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces.
CoRR
Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens
2020 J jnl
End-to-end Kernel Learning via Generative Random Fourier Features.
CoRR
Kun Fang, Xiaolin Huang, Fanghui Liu, Jie Yang
2020 J jnl
Generalizing Random Fourier Features via Generalized Measures.
CoRR
Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens
2020 J jnl
Kernel regression in high dimension: Refined analysis beyond double descent.
CoRR
Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens
2020 J jnl
Learning Data-adaptive Non-parametric Kernels.
J. Mach. Learn. Res.
Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li
2020 J jnl
Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond.
CoRR
Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens
2020 A* conf
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling.
AAAI
Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan A. K. Suykens
2020 J jnl
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines.
CoRR
Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens
2019 J jnl
Deep Kernel Learning via Random Fourier Features.
CoRR
Jiaxuan Xie, Fanghui Liu, Kaijie Wang, Xiaolin Huang
2019 — conf
Dense Multi-focus Fusion Net: A Deep Unsupervised Convolutional Network for Multi-focus Image Fusion.
ICAISC (1)
Hafiz Tayyab Mustafa, Fanghui Liu, Jie Yang, Zubair Khan, Qiao Huang
2019 J jnl
Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure.
IEEE Trans. Neural Networks Learn. Syst.
Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Johan A. K. Suykens
2019 J jnl
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling.
CoRR
Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan A. K. Suykens
2019 J jnl
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching.
CoRR
Fanghui Liu, Chen Gong, Xiaolin Huang, Tao Zhou, Jie Yang, Dacheng Tao
2019 J jnl
Sparse Indefinite Kernel Learning on Sphere.
Aust. J. Intell. Inf. Process. Syst.
Jiaxuan Xie, Fanghui Liu, Xiaolin Huang, Jie Yang
2018 — conf
Deep-PUMR: Deep Positive and Unlabeled Learning with Manifold Regularization.
ICONIP (1)
Xingyu Chen, Fanghui Liu, Enmei Tu, Longbing Cao, Jie Yang
2018 J jnl
Densely Connected Discriminative Correlation Filters for Visual Tracking.
IEEE Signal Process. Lett.
Cheng Peng, Fanghui Liu, Jie Yang, Nikola K. Kasabov
2018 J jnl
Generalization Properties of hyper-RKHS and its Application to Out-of-Sample Extensions.
CoRR
Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens
2018 J jnl
Inverse Nonnegative Local Coordinate Factorization for Visual Tracking.
IEEE Trans. Circuits Syst. Video Technol.
Fanghui Liu, Tao Zhou, Chen Gong, Keren Fu, Li Bai, Jie Yang
2018 J jnl
Learning Data-adaptive Nonparametric Kernels.
CoRR
Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li
2018 A* conf
Nonlinear Pairwise Layer and Its Training for Kernel Learning.
AAAI
Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li
2018 J jnl
Online discriminative dictionary learning for robust object tracking.
Neurocomputing
Tao Zhou, Fanghui Liu, Harish Bhaskar, Jie Yang, Huanlong Zhang, Ping Cai
2018 J jnl
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching.
IEEE Trans. Image Process.
Fanghui Liu, Chen Gong, Xiaolin Huang, Tao Zhou, Jie Yang, Dacheng Tao
2018 J jnl
Robust Visual Tracking via Dirac-Weighted Cascading Correlation Filters.
IEEE Signal Process. Lett.
Cheng Peng, Fanghui Liu, Jie Yang, Nikola K. Kasabov
2018 J jnl
Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning.
IEEE Trans. Cybern.
Tao Zhou, Fanghui Liu, Harish Bhaskar, Jie Yang
2017 — conf
Correlation Filters with Adaptive Memories and Fusion for Visual Tracking.
ICONIP (3)
Cheng Peng, Fanghui Liu, Haiyan Yang, Jie Yang, Nikola K. Kasabov
2017 J jnl
Graph Regularized and Locality-Constrained Coding for Robust Visual Tracking.
IEEE Trans. Circuits Syst. Video Technol.
Tao Zhou, Harish Bhaskar, Fanghui Liu, Jie Yang
2017 A* conf
Indefinite Kernel Logistic Regression.
ACM Multimedia
Fanghui Liu, Xiaolin Huang, Jie Yang
2017 J jnl
Indefinite Kernel Logistic Regression.
CoRR
Fanghui Liu, Xiaolin Huang, Jie Yang
2017 J jnl
Kernelized temporal locality learning for real-time visual tracking.
Pattern Recognit. Lett.
Fanghui Liu, Tao Zhou, Keren Fu, Jie Yang
2017 J jnl
Online learning and joint optimization of combined spatial-temporal models for robust visual tracking.
Neurocomputing
Tao Zhou, Harish Bhaskar, Fanghui Liu, Jie Yang, Ping Cai
2017 — conf
Robust Kernel Approximation for Classification.
ICONIP (1)
Fanghui Liu, Xiaolin Huang, Cheng Peng, Jie Yang, Nikola K. Kasabov
2017 J jnl
Visual Tracking via Nonnegative Multiple Coding.
IEEE Trans. Multim.
Fanghui Liu, Chen Gong, Tao Zhou, Keren Fu, Xiangjian He, Jie Yang
2017 B conf
Visual tracking via structural patch-based dictionary pair learning.
ICIP
Tao Zhou, Fanghui Liu, Harish Bhaskar, Jie Yang, Lei Chen, Ping Cai
2016 J jnl
Co-saliency detection via inter and intra saliency propagation.
Signal Process. Image Commun.
Chenjie Ge, Keren Fu, Fanghui Liu, Li Bai, Jie Yang
2016 B conf
Correlation filter tracking via bootstrap learning.
ICIP
Kunqi Gu, Tao Zhou, Fanghui Liu, Jie Yang, Yu Qiao
2016 J jnl
Geometric affine transformation estimation via correlation filter for visual tracking.
Neurocomputing
Fanghui Liu, Tao Zhou, Jie Yang
2016 — conf
Incremental Robust Nonnegative Matrix Factorization for Object Tracking.
ICONIP (2)
Fanghui Liu, Mingna Liu, Tao Zhou, Yu Qiao, Jie Yang
2016 J jnl
Robust visual tracking via constrained correlation filter coding.
Pattern Recognit. Lett.
Fanghui Liu, Tao Zhou, Keren Fu, Jie Yang
2016 Misc conf
Robust visual tracking via inverse nonnegative matrix factorization.
ICASSP
Fanghui Liu, Tao Zhou, Keren Fu, Irene Y. H. Gu, Jie Yang
2015 — conf
Visual Tracking via Nonnegative Regularization Multiple Locality Coding.
ICCV Workshops
Fanghui Liu, Tao Zhou, Jie Yang, Irene Y. H. Gu
2015 J jnl
Visual Tracking via Nonnegative Regularization Multiple Locality Coding.
CoRR
Fanghui Liu, Tao Zhou, Irene Y. H. Gu, Jie Yang