News (March 2023)

• New paper on membership inference came out on ArXiv. [paper]

Research

Papers on Deep Generative Models


Zhifeng Kong, Kamalika Chaudhuri. Data Redaction from Pre-trained GANs from Pre-trained GANs. In SaTML 2023.
[paper] [Tag: GAN, Trustworthiness]

Zhifeng Kong, Scott Alfeld. Approximate Data Deletion in Generative Models. ArXiv preprint.
[paper] [Tag: Generative Model, Privacy, Trustworthiness]

Zhifeng Kong, Kamalika Chaudhuri. Understanding Instance-based Interpretability of Variational Auto-Encoders. In NeurIPS 2021.
[paper] [code] [video] [blog] [Tag: VAE, Interpretability]

• Zhaoyang Lyu*, Zhifeng Kong*, Xudong Xu, Liang Pan, Dahua Lin. A conditional point diffusion-refinement paradigm for 3d point cloud completion. In ICLR 2022.
[paper] [code] [Tag: Diffusion Model, Point Cloud Completion]

Zhifeng Kong, Wei Ping. On Fast Sampling of Diffusion Probabilistic Models. ArXiv preprint.
[paper] [demo] [code] [Tag: Diffusion Model, Sampling]

Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, Bryan Catanzaro. DiffWave: A Versatile Diffusion Model for Audio Synthesis. In ICLR 2021 (oral).
[paper] [demo] [video] [Tag: Diffusion Model, Speech Synthesis]

Zhifeng Kong, Kamalika Chaudhuri. Universal Approximation of Residual Flows in Maximum Mean Discrepancy. In ICML 2021 INNF Workshop.
[paper] [video] [Tag: Normalizing Flows, Theory]

Zhifeng Kong, Kamalika Chaudhuri. The Expressive Power of a Class of Normalizing Flow Models. In AISTATS 2020.
[paper] [slides] [blog] [Tag: Normalizing Flows, Theory]

Zhifeng Kong, Kamalika Chaudhuri. The Expressive Power of Planar Flows. In The Workshop on Theory of Deep Learning: Where next?
[paper] [slides] [poster] [video] [Tag: Normalizing Flows, Theory]

Papers on Audio Processing


Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, Bryan Catanzaro. DiffWave: A Versatile Diffusion Model for Audio Synthesis. In ICLR 2021 (oral).
[paper] [demo] [video] [Tag: Diffusion Model, Speech Synthesis]

Zhifeng Kong, Wei Ping, Ambrish Dantrey, Bryan Catanzaro. Speech Denoising in the Waveform Domain with Self-Attention. In ICASSP 2022.
[paper] [demo] [Tag: Self Attention, Speech Denoising]

Zhifeng Kong, Wei Ping, Ambrish Dantrey, Bryan Catanzaro. CleanUNet 2: A Hybrid Speech Denoising Model on Waveform and Spectrogram. In submission.
[Tag: Self Attention, Speech Denoising]

Papers on Privacy


Zhifeng Kong, Scott Alfeld. Approximate Data Deletion in Generative Models. ArXiv preprint.
[paper] [Tag: Generative Model, Privacy]

Zhifeng Kong*, Amrita Roy Chowdhury*, Kamalika Chaudhuri. Forgeability and Membership Inference Attacks. In AISec 2022.
[Tag: Membership Inference]

Zhifeng Kong*, Amrita Roy Chowdhury*, Kamalika Chaudhuri. Can Membership Inferencing be Refuted? ArXiv preprint.
[paper] [Tag: Membership Inference]

Other Topics


• Zhangyu Wang, Lantian Xu, Zhifeng Kong, Weilong Wang, Xuyu Peng, Enyang Zheng. A Geometry-Aware Algorithm to Learn Hierarchical Embeddings in Hyperbolic Space. In ICLR 2021 GTRL Workshop.
[paper] [Tag: Representation Learning]

• Zhaoyang Lyu, Ching-Yun Ko, Zhifeng Kong, Ngai Wong, Dahua Lin, Luca Daniel. Fastened CROWN: Tightened Neural Network Robustness Certificates. In AAAI 2020.
[paper] [Tag: Robustness]

Zhifeng Kong. Convergence Analysis of Training Two-hidden-layer Partially Over-parameterized ReLU Networks vis Gradient Descent. In ICNIP 2020.
[paper] [Tag: Optimization, Theory]

Zhifeng Kong. Convergence Analysis of the Dynamics of a Special Kind of Two-Layered Neural Networks with l1 and l2 Regularization. ArXiv preprint.
[paper] [Tag: Optimization, Theory]

• Xingyu Wan, Qing Zhao, Jinjun Wang, Shunming Deng, Zhifeng Kong. Multi-Object Tracking Using Online Metric Learning with Long Short-Term Memory. In IEEE ICIP 2018, 788-792.
[paper] [Tag: LSTM, Object Tracking]

Blogs and Technical Reports

• Understanding Instance-based Interpretability of Variational Auto-Encoders. [blog]

• The expressive power of a class of normalizing flow models. [blog]

• Generalization theory for GANs. [slides]

• Notes on generalization properties of GAN metrics. [paper]

• Invertible ResNets. [slides]

Zhihu Blogs

UCSD Machine Learning Blogs

Industrial Experiences

• Research Internship at Baidu Research, USA. (Summer 2020)

• Research Internship at NVIDIA Applied Deep Learning Research. (Summer 2021 & 2022)

Services

I served as reviewers for ICML 2023, ICLR 2023, NeurIPS 2022, ICLR 2022, ICML 2022, ECCV 2022 Workshop UNCV, UAI 2022 Workshop TPM, NeurIPS 2021, ICML 2020 WorkShop WHI.

Talks and Presentations

• I am giving an invited talk at ICML MLAS workshop (July 2022). [video] [panel discussion]

• I am giving a poster presentation at NeurIPS (December 2021). [video]

• I am giving an invited talk at NeurIPS DGM workshop (December 2021). [video]

• I am giving a talk at UCSD AI seminar (November 2021).

• I am giving two spotlight presentations at ICML INNF workshop (July 2021).

• I am giving a spotlight presentation at ICML XAI workshop (July 2021).

• I am giving an invited talk at NVIDIA (June 2021).

• I am giving an invited talk at AI-Time, an AI community (June 2021).

• I am giving an oral presentation at ICLR 2021 (May 2021). [video]

• I am giving invited talks at TechBeat, an AI community (May 2021). [video]

• I am giving a spotlight presentation at AAAI 2020 (Feb 2020).

• I am giving a spotlight presentation at the IAS deep learning theory workshop (Oct 2019). [video]

Contact me!

Email: z4kong at eng dot ucsd dot edu

WeChat: kong-zhi-feng

Twitter: @ZhifengKong

LinkedIn: Zhifeng Kong

Instagram: zhifeng.kong.3

Zhihu: Zhifeng

Make an appointment: Google Calendar