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For the complete list, please refer to my Google Scholar Profile.

* indicates equal contribution

Benchmark

The adversarially trained NAS benchmark (NAS-RobBench-201) in our ICLR24 paper was released! See [project website] for details.

Preprints

• Can overfitted deep neural networks in adversarial training generalize? – An approximation viewpoint. [arXiv].
  Zhongjie Shi, Fanghui Liu, Yuan Cao, Johan A.K. Suykens.
  

Accepted Papers

• Generalization of Deep ResNets in the mean-field regime. [link].
  Yihang Chen, Fanghui Liu, Yiping Lu, Grigorios Chrysos, Volkan Cevher.
  in the 12th International Conference on Learning Representations (ICLR), 2024. [Spotlight]
  

• Robust NAS benchmark under adversarial training: assessment, theory, and beyond. [link], [project website].
  Yongtao Wu, Fanghui Liu, Carl-Johann Simon-Gabriel, Grigorios Chrysos, Volkan Cevher.
  in the 12th International Conference on Learning Representations (ICLR), 2024.
  

• Efficient local linearity regularization to overcome catastrophic overfitting. [link], [code].
  Elias Abad Rocamora, Fanghui Liu, Grigorios Chrysos, Pablo M. Olmos, Volkan Cevher.
  in the 12th International Conference on Learning Representations (ICLR), 2024.
  

• On the convergence of encoder-only shallow Transformers. [arxiv].
  Yongtao Wu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher.
  in the 37th Conference on Neural Information Processing Systems (NeurIPS), 2023.
  

• Initialization matters: Privacy-utility analysis of overparameterized neural networks. [arXiv].
  Jiayuan Ye, Zhenyu Zhu, Fanghui Liu, Reza Shokri, Volkan Cevher.
  in the 37th Conference on Neural Information Processing Systems (NeurIPS), 2023.
  

• What can online reinforcement learning with function approximation benefit from general coverage conditions?. [arXiv].
  Fanghui Liu, Luca Viano, Volkan Cevher.
  in the 40th International Conference on Machine Learning (ICML), 2023.
  

• Benign Overfitting in Deep Neural Networks under Lazy Training. [arXiv].
  Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Francesco Locatello, Volkan Cevher.
  in the 40th International Conference on Machine Learning (ICML), 2023.
  

• On the double descent of random features models trained by SGD. [arXiv], [code], [slides].
  Fanghui Liu, Johan A.K. Suykens, Volkan Cevher.
  in the 36th Conference on Neural Information Processing Systems (NeurIPS), 2022.
  Presented at Workshop on the Theory of Overparameterized Machine Learning (TOPML) 2022.

• Understanding deep neural function approximation in reinforcement learning via \(\epsilon\)-greedy exploration. [arXiv].
  Fanghui Liu, Luca Viano, Volkan Cevher.
  in the 36th Conference on Neural Information Processing Systems (NeurIPS), 2022.
  

• Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization). [arXiv], [slides].
  Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher.
  in the 36th Conference on Neural Information Processing Systems (NeurIPS), 2022.
  

• Generalization properties of NAS under activation and skip connection search. [arXiv].
  Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher.
  in the 36th Conference on Neural Information Processing Systems (NeurIPS), 2022.
  

• Extrapolation and spectral bias of neural nets with Hadamard product: a polynomial net study. [arXiv].
  Yongtao Wu, Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher.
  in the 36th Conference on Neural Information Processing Systems (NeurIPS), 2022.
  

• Sound and complete verification of polynomial networks. [arXiv].
  Elias Abad Rocamora, Mehmet Fatih Sahin, Fanghui Liu, Grigorios Chrysos, Volkan Cevher.
  in the 36th Conference on Neural Information Processing Systems (NeurIPS), 2022.
  

• Random features for kernel approximation: A Survey on algorithms, theory, and beyond. [arXiv], [code].
  Fanghui Liu, Xiaolin Huang, Yudong Chen, and Johan A.K. Suykens.
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.

• Generalization properties of hyper-RKHS and its applications. [arxiv], [link], [code].
  Fanghui Liu*, Lei Shi*, Xiaolin Huang, Jie Yang, and Johan A.K. Suykens.
  Journal of Machine Learning Research (JMLR), 2021.

• Towards a unified quadrature framework for large scale kernel methods. [arXiv], [code].
  Fanghui Liu, Xiaolin Huang, Yudong Chen, and Johan A.K. Suykens.
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.

• Kernel regression in high dimensions: Refined analysis beyond double descent. [link], [code], [slides].
  Fanghui Liu, Zhenyu Liao, and Johan A.K. Suykens.
  in the 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.

• Fast learning in reproducing kernel Krein spaces via signed measures. [link], [poster], [code].
  Fanghui Liu, Xiaolin Huang, Yingyi Chen, and Johan A.K. Suykens.
  in the 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.

• Analysis of least squares regularized regression in reproducing kernel Krein spaces. [arXiv].
  Fanghui Liu*, Lei Shi*, Xiaolin Huang, Jie Yang, and Johan A.K. Suykens.
  Machine Learning, 2021.

• Learning data-adaptive nonparametric kernels. [link] [code].
  Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, and Li Li.
  Journal of Machine Learning Research (JMLR), 2020.

• Random Fourier features via fast surrogate leverage weighted sampling. [arXiv], [code].
  Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, and Johan A.K. Suykens.
  in the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.

• A double-variational Bayesian framework in random Fourier features for indefinite kernels. [link], [code].
  Fanghui Liu, Xiaolin Huang, Lei Shi, Jie Yang, and Johan A.K. Suykens.
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019.

• Indefinite kernel logistic regression with Concave-inexact-convex procedure. [arXiv], [code].
  Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, and Johan A.K. Suykens.
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2018.