Zhou, Feng (周峰)
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Assistant Professor |
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About me
I received my Bachelor's degree in Electrical Engineering from Beijing Forestry University. I completed my Master's degree at the Institute of Electrical Engineering, Chinese Academy of Sciences, co-supervised by Researcher Yinming Dai and Qiuliang Wang, and earned my Ph.D. from the University of New South Wales, co-supervised by Professor Arcot Somya and Fang Chen. After my Ph.D., I conducted postdoctoral research at Tsinghua University with Professor Jun Zhu. Currently, I am a faculty member at the School of Statistics, Renmin University of China.
News
Two papers accepted for ICML 2024.
One paper accepted for CVPR 2024.
One paper accepted for AAAI 2024.
Two papers accepted for NeurIPS 2023.
One paper accepted for AISTATS 2023.
One paper accepted for Machine Learning.
Enrollment
I am always looking for smart, disciplined, and self-driven students. If there are any opening positions in my group, I will promptly update this information here.
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3-4 master positions open. Contact me if interested.
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鼓励真正对相关研究充满兴趣的同学联系，特别欢迎那些具有先前研究经验或已经对我们的研究领域有一些独到见解的申请者。联系时，请提供一封有深度的信函，而不只是一个普通的简历。非常重视申请同学与相关研究方向的契合程度、准备情况以及潜在合作想法的讨论，期待与优秀的同学共同进步。
Research
My academic background spans various interdisciplinary fields, including initial focuses on applied superconductivity, followed by transitions into areas such as statistical machine learning, stochastic processes, Bayesian methods, and AI4science applications:
Gaussian Process
Stochastic Point Process
Bayesian Nonparametric
Markov chain Monte Carlo
Variational Inference
Deep Generative Models
Preprint
Z. Feng, Z. Ling, C. Gong, F. Zhou, J. Li, RC. Qiu, "Zero-shot Inversion Process for Image Attribute Editing with Diffusion Models".
Z. Deng, F. Zhou, J. Chen, G. Wu, J. Zhu, "Deep Ensemble as a Gaussian Process Approximate Posterior".
S. Lei, Z. Tu, L. Rutkowski, F. Zhou, L. Shen, F. He, D. Tao, "Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer".
S. Luo, F. Zhou, L. Azizi, M. Sugiyama, "Additive poisson process: Learning intensity of higher-order interaction in stochastic processes".
Recent Publications
T. Ke, H. Cao, F. Zhou*, "Accelerating Convergence in Bayesian Few-Shot Classification", ICML 2024. [pdf][code]
Z. Ling, L. Li, Z. Feng, Y. Zhang, F. Zhou, R. Qiu, Z. Liao, "Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures", ICML 2024.
Y. Miao, Y. Lei, F. Zhou*, Zhijie Deng*, "Bayesian Exploration of Pre-trained Models for Low-shot Image Classification", CVPR 2024. [pdf]
Y. Zhang, B. Li, Z. Ling, F. Zhou*, "Mitigating Label Bias in Machine Learning: Fairness through Confident Learning", AAAI 2024. [pdf][code]
T. Ke, H. Cao, Z. Ling, F. Zhou*, "Revisiting Logistic-softmax Likelihood in Bayesian Meta-learning for Few-shot Classification", NeurIPS 2023. [pdf][code]
Y. Zhang, Q. Kong, F. Zhou*, "Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes", NeurIPS 2023. [pdf]
F. Zhou, Q. Kong, Z. Deng, F. He, P. Cui, J. Zhu*, "Heterogeneous Multi-task Gaussian Cox Processes", Machine Learning. [pdf][code]
Y. Mou, J. Geng, F. Zhou*, O. Beyan, C. Rong, S. Decker, "pFedV: Mitigating Feature Distribution Skewness via Personalized Federated Learning with Variational Distribution Constraints", PAKDD 2023. [pdf][code]
Y. Zhang, F. Zhou*, Z. Li, Y. Wang, F. Chen, "Fair Representation Learning with Unreliable Labels", AISTATS 2023. [pdf][code]
Z. Deng, F. Zhou, J. Zhu*, "Accelerated Linearized Laplace Approximation for Bayesian Deep Learning", NeurIPS 2022. [pdf][code]
F. Zhou, Q. Kong, Z. Deng, J. Kan, Y. Zhang, C. Feng, J. Zhu*, "Efficient Inference for Dynamic Flexible Interactions of Neural Populations", Journal of Machine Learning Research. [pdf][code]
X. Fan, B. Li, F. Zhou, S. Sisson, "Continuous-Time Edge Modelling Using Non-Parametric Point Processes", NeurIPS 2021. [pdf][code]
Y. Zhang, F. Zhou, Z. Li, Y. Wang, F. Chen, "Bias-Tolerant Fair Classification", ACML 2021. [pdf]
F. Zhou, S. Luo, Z. Li*, X. Fan, Y. Wang, A. Sowmya, F. Chen, "Efficient EM-Variational Inference for Nonparametric Hawkes Process", Statistics and Computing. [pdf][code]
F. Zhou, Y. Zhang, J. Zhu*, "Efficient Inference of Flexible Interaction in Spiking-neuron Networks", ICLR 2021. [pdf][code]
F. Zhou, Z. Li, X. Fan, Y. Wang, A. Sowmya, F. Chen, "Efficient Inference for Nonparametric Hawkes Processes Using Auxiliary Latent Variables", Journal of Machine Learning Research. [pdf][code]
S. Luo, F. Zhou, L. Azizi, M. Sugiyama, "Learning Joint Intensity in a Multivariate Poisson Process on Statistical Manifolds", NeurIPS 2020. [pdf]
Note: * corresponding author
Academic Service for
ICML, NeurIPS, ICLR, AISTATS, AAAI, TNNLS, MLJ