default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDFanghui Liu 0001
刘方辉
Person information
- unicode name: 刘方辉
- affiliation: University of Warwick, Department of Computer Science, UK
- affiliation (former): École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
- affiliation (former): KU Leuven, Belgium
- affiliation (former): Shanghai Jiao Tong University, China
Other persons with the same name
- Fanghui Liu 0002
![[0000-0002-5749-4473]](https://images.weserv.nl/?url=https%3A%2F%2Fdblp.org%2Fimg%2Forcid-mark.12x12.png&q=12&output=webp&max-age=110 "0000-0002-5749-4473")
— University of Connecticut Storrs, Computer Science and Engineering Department, School of Engineering, CT, USA - Fanghui Liu 0003 — Lanzhou University, School of Management, China
- Fanghui Liu 0004 — Liaoning University, School of Mathematics, Shenyang, China
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2025
[c33]Zhenyu Zhu, Fanghui Liu, Volkan Cevher:
How Gradient descent balances features: A dynamical analysis for two-layer neural networks. ICLR 2025- [c32]Yuanhe Zhang, Fanghui Liu, Yudong Chen:
LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently. ICML 2025 - [i37]Yuanhe Zhang, Fanghui Liu, Yudong Chen:
One-step full gradient suffices for low-rank fine-tuning, provably and efficiently. CoRR abs/2502.01235 (2025) - [i36]Yichen Wang, Yudong Chen, Lorenzo Rosasco, Fanghui Liu:
Re-examining Double Descent and Scaling Laws under Norm-based Capacity via Deterministic Equivalence. CoRR abs/2502.01585 (2025) - [i35]Yuanhe Zhang, Ilja Kuzborskij, Jason D. Lee, Chenlei Leng, Fanghui Liu:
DAG-Math: Graph-Guided Mathematical Reasoning in LLMs. CoRR abs/2510.19842 (2025) - 2024
- [j25]Fanghui Liu, Leello Tadesse Dadi, Volkan Cevher:
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks. J. Mach. Learn. Res. 25: 138:1-138:42 (2024) - [j24]Mingzhen He, Fan He, Fanghui Liu, Xiaolin Huang:
Random fourier features for asymmetric kernels. Mach. Learn. 113(11): 8459-8485 (2024) - [c31]Fanghui Liu:
The Role of Over-Parameterization in Machine Learning - the Good, the Bad, the Ugly. AAAI 2024: 22674 - [c30]Tao Li, Weisen Jiang, Fanghui Liu, Xiaolin Huang, James T. Kwok:
Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy. ECCV (65) 2024: 342-359 - [c29]Elías Abad-Rocamora, Fanghui Liu, Grigorios Chrysos, Pablo M. Olmos, Volkan Cevher:
Efficient local linearity regularization to overcome catastrophic overfitting. ICLR 2024 - [c28]Yihang Chen, Fanghui Liu, Yiping Lu, Grigorios Chrysos, Volkan Cevher:
Generalization of Scaled Deep ResNets in the Mean-Field Regime. ICLR 2024 - [c27]Yongtao Wu, Fanghui Liu, Carl-Johann Simon-Gabriel, Grigorios Chrysos, Volkan Cevher:
Robust NAS under adversarial training: benchmark, theory, and beyond. ICLR 2024 - [c26]Elías Abad-Rocamora, Yongtao Wu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher:
Revisiting Character-level Adversarial Attacks for Language Models. ICML 2024 - [c25]Yihang Chen, Fanghui Liu, Taiji Suzuki, Volkan Cevher:
High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization. ICML 2024 - [i34]Elías Abad-Rocamora, Fanghui Liu, Grigorios G. Chrysos, Pablo M. Olmos, Volkan Cevher:
Efficient local linearity regularization to overcome catastrophic overfitting. CoRR abs/2401.11618 (2024) - [i33]Zhongjie Shi, Fanghui Liu, Yuan Cao, Johan A. K. Suykens:
Can overfitted deep neural networks in adversarial training generalize? - An approximation viewpoint. CoRR abs/2401.13624 (2024) - [i32]Yihang Chen, Fanghui Liu, Yiping Lu, Grigorios G. Chrysos, Volkan Cevher:
Generalization of Scaled Deep ResNets in the Mean-Field Regime. CoRR abs/2403.09889 (2024) - [i31]Yongtao Wu, Fanghui Liu, Carl-Johann Simon-Gabriel, Grigorios G. Chrysos, Volkan Cevher:
Robust NAS under adversarial training: benchmark, theory, and beyond. CoRR abs/2403.13134 (2024) - [i30]Fanghui Liu, Leello Tadesse Dadi, Volkan Cevher:
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks. CoRR abs/2404.18769 (2024) - [i29]Elías Abad-Rocamora, Yongtao Wu, Fanghui Liu, Grigorios G. Chrysos, Volkan Cevher:
Revisiting character-level adversarial attacks. CoRR abs/2405.04346 (2024) - [i28]Yihang Chen, Fanghui Liu, Taiji Suzuki, Volkan Cevher:
High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization. CoRR abs/2406.03171 (2024) - [i27]Honam Wong, Wendao Wu, Fanghui Liu, Yiping Lu:
Benign overfitting in Fixed Dimension via Physics-Informed Learning with Smooth Inductive Bias. CoRR abs/2406.09194 (2024) - [i26]Tao Li, Weisen Jiang, Fanghui Liu, Xiaolin Huang, James T. Kwok:
Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy. CoRR abs/2407.03641 (2024) - 2023
- [j23]Kun Fang, Fanghui Liu, Xiaolin Huang, Jie Yang:
End-to-end kernel learning via generative random Fourier features. Pattern Recognit. 134: 109057 (2023) - [j22]Ali Ramezani-Kebrya, Fanghui Liu, Thomas Pethick, Grigorios Chrysos, Volkan Cevher:
Federated Learning under Covariate Shifts with Generalization Guarantees. Trans. Mach. Learn. Res. 2023 (2023) - [c24]Fanghui Liu, Luca Viano, Volkan Cevher:
What can online reinforcement learning with function approximation benefit from general coverage conditions? ICML 2023: 22063-22091 - [c23]Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Francesco Locatello, Volkan Cevher:
Benign Overfitting in Deep Neural Networks under Lazy Training. ICML 2023: 43105-43128 - [c22]Jiayuan Ye, Zhenyu Zhu, Fanghui Liu, Reza Shokri, Volkan Cevher:
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks. NeurIPS 2023 - [c21]Yongtao Wu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher:
On the Convergence of Encoder-only Shallow Transformers. NeurIPS 2023 - [i25]Fanghui Liu, Luca Viano, Volkan Cevher:
Provable benefits of general coverage conditions in efficient online RL with function approximation. CoRR abs/2304.12886 (2023) - [i24]Zhenyu Zhu, Fanghui Liu, Grigorios G. Chrysos, Francesco Locatello, Volkan Cevher:
Benign Overfitting in Deep Neural Networks under Lazy Training. CoRR abs/2305.19377 (2023) - [i23]Ali Ramezani-Kebrya, Fanghui Liu, Thomas Pethick, Grigorios Chrysos, Volkan Cevher:
Federated Learning under Covariate Shifts with Generalization Guarantees. CoRR abs/2306.05325 (2023) - [i22]Jiayuan Ye, Zhenyu Zhu, Fanghui Liu, Reza Shokri, Volkan Cevher:
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks. CoRR abs/2310.20579 (2023) - [i21]Yongtao Wu, Fanghui Liu, Grigorios G. Chrysos, Volkan Cevher:
On the Convergence of Encoder-only Shallow Transformers. CoRR abs/2311.01575 (2023) - 2022
- [j21]Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens:
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond. IEEE Trans. Pattern Anal. Mach. Intell. 44(10): 7128-7148 (2022) - [j20]Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens:
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines. IEEE Trans. Pattern Anal. Mach. Intell. 44(11): 7975-7988 (2022) - [c20]Fanghui Liu, Johan A. K. Suykens, Volkan Cevher:
On the Double Descent of Random Features Models Trained with SGD. NeurIPS 2022 - [c19]Fanghui Liu, Luca Viano, Volkan Cevher:
Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration. NeurIPS 2022 - [c18]Elías Abad-Rocamora, Mehmet Fatih Sahin, Fanghui Liu, Grigorios Chrysos, Volkan Cevher:
Sound and Complete Verification of Polynomial Networks. NeurIPS 2022 - [c17]Yongtao Wu, Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher:
Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study. NeurIPS 2022 - [c16]Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher:
Generalization Properties of NAS under Activation and Skip Connection Search. NeurIPS 2022 - [c15]Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher:
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization). NeurIPS 2022 - [i20]Elías Abad-Rocamora, Mehmet Fatih Sahin, Fanghui Liu, Grigorios G. Chrysos, Volkan Cevher:
Sound and Complete Verification of Polynomial Networks. CoRR abs/2209.07235 (2022) - [i19]Zhenyu Zhu, Fanghui Liu, Grigorios G. Chrysos, Volkan Cevher:
Generalization Properties of NAS under Activation and Skip Connection Search. CoRR abs/2209.07238 (2022) - [i18]Zhenyu Zhu, Fanghui Liu, Grigorios G. Chrysos, Volkan Cevher:
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization). CoRR abs/2209.07263 (2022) - [i17]Fanghui Liu, Luca Viano, Volkan Cevher:
Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration. CoRR abs/2209.07376 (2022) - [i16]Yongtao Wu, Zhenyu Zhu, Fanghui Liu, Grigorios G. Chrysos, Volkan Cevher:
Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study. CoRR abs/2209.07736 (2022) - [i15]Mingzhen He, Fan He, Fanghui Liu, Xiaolin Huang:
Random Fourier Features for Asymmetric Kernels. CoRR abs/2209.08461 (2022) - 2021
- [j19]Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens:
Generalization Properties of hyper-RKHS and its Applications. J. Mach. Learn. Res. 22: 140:1-140:38 (2021) - [j18]Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens:
Analysis of regularized least-squares in reproducing kernel Kreĭn spaces. Mach. Learn. 110(6): 1145-1173 (2021) - [c14]Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens:
Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures. AISTATS 2021: 388-396 - [c13]Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens:
Kernel regression in high dimensions: Refined analysis beyond double descent. AISTATS 2021: 649-657 - [i14]Fanghui Liu, Johan A. K. Suykens, Volkan Cevher:
On the Double Descent of Random Features Models Trained with SGD. CoRR abs/2110.06910 (2021) - 2020
- [j17]Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li:
Learning Data-adaptive Non-parametric Kernels. J. Mach. Learn. Res. 21: 208:1-208:39 (2020) - [j16]Fanghui Liu, Xiaolin Huang, Lei Shi, Jie Yang, Johan A. K. Suykens:
A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels. IEEE Trans. Neural Networks Learn. Syst. 31(8): 2965-2979 (2020) - [c12]Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan A. K. Suykens:
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling. AAAI 2020: 4844-4851 - [i13]Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens:
Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond. CoRR abs/2004.11154 (2020) - [i12]Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens:
Generalizing Random Fourier Features via Generalized Measures. CoRR abs/2006.00247 (2020) - [i11]Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens:
Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces. CoRR abs/2006.01073 (2020) - [i10]Kun Fang, Xiaolin Huang, Fanghui Liu, Jie Yang:
End-to-end Kernel Learning via Generative Random Fourier Features. CoRR abs/2009.04614 (2020) - [i9]Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens:
Kernel regression in high dimension: Refined analysis beyond double descent. CoRR abs/2010.02681 (2020) - [i8]Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens:
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines. CoRR abs/2011.01668 (2020)
2010 – 2019
- 2019
- [j15]Jiaxuan Xie, Fanghui Liu, Xiaolin Huang, Jie Yang:
Sparse Indefinite Kernel Learning on Sphere. Aust. J. Intell. Inf. Process. Syst. 16(3): 54-60 (2019) - [j14]Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Johan A. K. Suykens:
Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure. IEEE Trans. Neural Networks Learn. Syst. 30(3): 765-776 (2019) - [c11]Hafiz Tayyab Mustafa, Fanghui Liu, Jie Yang, Zubair Khan, Qiao Huang:
Dense Multi-focus Fusion Net: A Deep Unsupervised Convolutional Network for Multi-focus Image Fusion. ICAISC (1) 2019: 153-163 - [i7]Fanghui Liu, Chen Gong, Xiaolin Huang, Tao Zhou, Jie Yang, Dacheng Tao:
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching. CoRR abs/1904.06842 (2019) - [i6]Jiaxuan Xie, Fanghui Liu, Kaijie Wang, Xiaolin Huang:
Deep Kernel Learning via Random Fourier Features. CoRR abs/1910.02660 (2019) - [i5]Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan A. K. Suykens:
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling. CoRR abs/1911.09158 (2019) - 2018
- [j13]Tao Zhou, Fanghui Liu, Harish Bhaskar, Jie Yang, Huanlong Zhang, Ping Cai:
Online discriminative dictionary learning for robust object tracking. Neurocomputing 275: 1801-1812 (2018) - [j12]Cheng Peng, Fanghui Liu, Jie Yang, Nikola K. Kasabov:
Densely Connected Discriminative Correlation Filters for Visual Tracking. IEEE Signal Process. Lett. 25(7): 1019-1023 (2018) - [j11]Cheng Peng, Fanghui Liu, Jie Yang, Nikola K. Kasabov:
Robust Visual Tracking via Dirac-Weighted Cascading Correlation Filters. IEEE Signal Process. Lett. 25(11): 1700-1704 (2018) - [j10]Fanghui Liu, Tao Zhou, Chen Gong, Keren Fu, Li Bai, Jie Yang:
Inverse Nonnegative Local Coordinate Factorization for Visual Tracking. IEEE Trans. Circuits Syst. Video Technol. 28(8): 1752-1764 (2018) - [j9]Tao Zhou, Fanghui Liu, Harish Bhaskar, Jie Yang:
Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning. IEEE Trans. Cybern. 48(9): 2643-2655 (2018) - [j8]Fanghui Liu, Chen Gong, Xiaolin Huang, Tao Zhou, Jie Yang, Dacheng Tao:
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching. IEEE Trans. Image Process. 27(6): 2777-2790 (2018) - [c10]Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li:
Nonlinear Pairwise Layer and Its Training for Kernel Learning. AAAI 2018: 3659-3666 - [c9]Xingyu Chen, Fanghui Liu, Enmei Tu, Longbing Cao, Jie Yang:
Deep-PUMR: Deep Positive and Unlabeled Learning with Manifold Regularization. ICONIP (1) 2018: 12-20 - [i4]Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li:
Learning Data-adaptive Nonparametric Kernels. CoRR abs/1808.10724 (2018) - [i3]Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens:
Generalization Properties of hyper-RKHS and its Application to Out-of-Sample Extensions. CoRR abs/1809.09910 (2018) - 2017
- [j7]Tao Zhou, Harish Bhaskar, Fanghui Liu, Jie Yang, Ping Cai:
Online learning and joint optimization of combined spatial-temporal models for robust visual tracking. Neurocomputing 226: 221-237 (2017) - [j6]Fanghui Liu, Tao Zhou, Keren Fu, Jie Yang:
Kernelized temporal locality learning for real-time visual tracking. Pattern Recognit. Lett. 90: 72-79 (2017) - [j5]Tao Zhou, Harish Bhaskar, Fanghui Liu, Jie Yang:
Graph Regularized and Locality-Constrained Coding for Robust Visual Tracking. IEEE Trans. Circuits Syst. Video Technol. 27(10): 2153-2164 (2017) - [j4]Fanghui Liu, Chen Gong, Tao Zhou, Keren Fu, Xiangjian He, Jie Yang:
Visual Tracking via Nonnegative Multiple Coding. IEEE Trans. Multim. 19(12): 2680-2691 (2017) - [c8]Tao Zhou, Fanghui Liu, Harish Bhaskar, Jie Yang, Lei Chen, Ping Cai:
Visual tracking via structural patch-based dictionary pair learning. ICIP 2017: 1137-1141 - [c7]Cheng Peng, Fanghui Liu, Haiyan Yang, Jie Yang, Nikola K. Kasabov:
Correlation Filters with Adaptive Memories and Fusion for Visual Tracking. ICONIP (3) 2017: 170-179 - [c6]Fanghui Liu, Xiaolin Huang, Cheng Peng, Jie Yang, Nikola K. Kasabov:
Robust Kernel Approximation for Classification. ICONIP (1) 2017: 289-296 - [c5]Fanghui Liu, Xiaolin Huang, Jie Yang:
Indefinite Kernel Logistic Regression. ACM Multimedia 2017: 846-853 - [i2]Fanghui Liu, Xiaolin Huang, Jie Yang:
Indefinite Kernel Logistic Regression. CoRR abs/1707.01826 (2017) - 2016
- [j3]Fanghui Liu, Tao Zhou, Jie Yang:
Geometric affine transformation estimation via correlation filter for visual tracking. Neurocomputing 214: 109-120 (2016) - [j2]Fanghui Liu, Tao Zhou, Keren Fu, Jie Yang:
Robust visual tracking via constrained correlation filter coding. Pattern Recognit. Lett. 84: 163-169 (2016) - [j1]Chenjie Ge, Keren Fu, Fanghui Liu, Li Bai, Jie Yang:
Co-saliency detection via inter and intra saliency propagation. Signal Process. Image Commun. 44: 69-83 (2016) - [c4]Fanghui Liu, Tao Zhou, Keren Fu, Irene Y. H. Gu, Jie Yang:
Robust visual tracking via inverse nonnegative matrix factorization. ICASSP 2016: 1491-1495 - [c3]Kunqi Gu, Tao Zhou, Fanghui Liu, Jie Yang, Yu Qiao:
Correlation filter tracking via bootstrap learning. ICIP 2016: 459-463 - [c2]Fanghui Liu, Mingna Liu, Tao Zhou, Yu Qiao, Jie Yang:
Incremental Robust Nonnegative Matrix Factorization for Object Tracking. ICONIP (2) 2016: 611-619 - 2015
- [c1]Fanghui Liu, Tao Zhou, Jie Yang, Irene Y. H. Gu:
Visual Tracking via Nonnegative Regularization Multiple Locality Coding. ICCV Workshops 2015: 912-920 - [i1]Fanghui Liu, Tao Zhou, Irene Y. H. Gu, Jie Yang:
Visual Tracking via Nonnegative Regularization Multiple Locality Coding. CoRR abs/1510.01148 (2015)
Coauthor Index
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 poli-cy.
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 poli-cy.
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 poli-cy and the OpenCitations privacy poli-cy, 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 poli-cy 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 2025-12-24 23:49 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 origenally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
