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Feng Zhou 0011
Person information
- affiliation: University of New South Wales, Sydney, NSW, Australia
- affiliation: Data61, CSIRO, Alexandria, Australia
Other persons with the same name
- Feng Zhou — disambiguation page
- Feng Zhou 0001
— Xidian University, Ministry Key Laboratory of Electronic Information Countermeasure and Simulation, Xi'an, China
- Feng Zhou 0002
— Baidu Research, Sunnyvale, CA, USA (and 2 more)
- Feng Zhou 0003
— University of Michigan at Dearborn, Industrial and Manufacturing Systems Engineering, MI, USA (and 2 more)
- Feng Zhou 0004
— Peking University, College of Environmental Sciences and Engineering, Beijing, China
- Feng Zhou 0005
— Central South University China, School of Information Science and Engineering, Changsha, China
- Feng Zhou 0006
— Nanjing University of Information Science and Technology, School of Information and Control, Jiangsu Key Laboratory of Big Data Analysis Technology, China
- Feng Zhou 0007
— Beihang University, State Key Laboratory of Virtual Reality Technology and Systems, Beijing, China
- Feng Zhou 0008
— UISEE (Shanghai) Automotive Technologies Ltd, China
- Feng Zhou 0009
— Army Engineering University of PLA, College of Communications Engineering, Nanjing, China
- Feng Zhou 0010
— Yancheng Institute of Technology, School of Information Technology, China
- Feng Zhou 0012
— Harbin Engineering University, Acoustic Science and Technology Laboratory, College of Underwater Acoustic Engineering, China
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2020 – today
- 2024
- [c9]Yixuan Zhang, Boyu Li, Zenan Ling, Feng Zhou:
Mitigating Label Bias in Machine Learning: Fairness through Confident Learning. AAAI 2024: 16917-16925 - [i12]Zenan Ling, Longbo Li, Zhanbo Feng, Yixuan Zhang, Feng Zhou, Robert C. Qiu, Zhenyu Liao:
Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures. CoRR abs/2402.02697 (2024) - [i11]Yixuan Zhang, Feng Zhou:
Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency. CoRR abs/2403.00625 (2024) - 2023
- [c8]Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen:
Fair Representation Learning with Unreliable Labels. AISTATS 2023: 4655-4667 - [c7]Yixuan Zhang, Quyu Kong, Feng Zhou:
Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes. NeurIPS 2023 - [c6]Yongli Mou
, Jiahui Geng
, Feng Zhou
, Oya Beyan
, Chunming Rong
, Stefan Decker
:
pFedV: Mitigating Feature Distribution Skewness via Personalized Federated Learning with Variational Distribution Constraints. PAKDD (2) 2023: 283-294 - [i10]Zhanbo Feng, Zenan Ling, Ci Gong, Feng Zhou, Jie Li, Robert C. Qiu:
Zero-shot Inversion Process for Image Attribute Editing with Diffusion Models. CoRR abs/2308.15854 (2023) - [i9]Yixuan Zhang, Quyu Kong, Feng Zhou:
Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes. CoRR abs/2310.05485 (2023) - [i8]Yixuan Zhang, Boyu Li, Zenan Ling, Feng Zhou:
Mitigating Label Bias in Machine Learning: Fairness through Confident Learning. CoRR abs/2312.08749 (2023) - 2022
- [j3]Feng Zhou, Quyu Kong, Zhijie Deng, Jichao Kan, Yixuan Zhang, Cheng Feng, Jun Zhu:
Efficient Inference for Dynamic Flexible Interactions of Neural Populations. J. Mach. Learn. Res. 23: 211:1-211:49 (2022) - [i7]Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen:
De-biased Representation Learning for Fairness with Unreliable Labels. CoRR abs/2208.00651 (2022) - 2021
- [j2]Feng Zhou, Simon Luo
, Zhidong Li, Xuhui Fan
, Yang Wang
, Arcot Sowmya, Fang Chen:
Efficient EM-variational inference for nonparametric Hawkes process. Stat. Comput. 31(4): 46 (2021) - [c5]Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen:
Bias-tolerant Fair Classification. ACML 2021: 840-855 - [c4]Feng Zhou, Yixuan Zhang, Jun Zhu:
Efficient Inference of Flexible Interaction in Spiking-neuron Networks. ICLR 2021 - [c3]Xuhui Fan, Bin Li, Feng Zhou, Scott A. Sisson:
Continuous-time edge modelling using non-parametric point processes. NeurIPS 2021: 2319-2330 - [i6]Feng Zhou, Quyu Kong, Yixuan Zhang, Cheng Feng, Jun Zhu:
Nonlinear Hawkes Processes in Time-Varying System. CoRR abs/2106.04844 (2021) - [i5]Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen:
Bias-Tolerant Fair Classification. CoRR abs/2107.03207 (2021) - 2020
- [j1]Feng Zhou
, Zhidong Li
, Xuhui Fan
, Yang Wang
, Arcot Sowmya, Fang Chen:
Fast multi-resolution segmentation for nonstationary Hawkes process using cumulants. Int. J. Data Sci. Anal. 10(4): 321-330 (2020) - [i4]Simon Luo, Feng Zhou, Lamiae Azizi, Mahito Sugiyama:
Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes. CoRR abs/2006.08982 (2020) - [i3]Feng Zhou, Yixuan Zhang, Jun Zhu:
Efficient Inference of Nonparametric Interaction in Spiking-neuron Networks. CoRR abs/2006.12845 (2020)
2010 – 2019
- 2019
- [c2]Feng Zhou, Yixuan Zhang, Zhidong Li
, Xuhui Fan
, Yang Wang
, Arcot Sowmya, Fang Chen
:
Hawkes Process with Stochastic Triggering Kernel. PAKDD (1) 2019: 319-330 - [i2]Feng Zhou:
Efficient EM-Variational Inference for Hawkes Process. CoRR abs/1905.12251 (2019) - [i1]Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya, Fang Chen:
Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation. CoRR abs/1910.13052 (2019) - 2018
- [c1]Feng Zhou, Zhidong Li
, Xuhui Fan
, Yang Wang
, Arcot Sowmya, Fang Chen
:
A Refined MISD Algorithm Based on Gaussian Process Regression. PAKDD (2) 2018: 584-596
Coauthor Index
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last updated on 2024-08-23 18:33 CEST by the dblp team
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