I am a Ph.D. student at University of California, Santa Cruz (UCSC), working with Professor Yang Liu. I am presently focused on enhancing the robustness of machine learning models, particularly by addressing challenges such as improving model resilience when trained on data with noisy labels. This encompasses a variety of data formats, including image, text, and time series data.

Previously, I spent two years at YouTu Lab, Tencent, working with Ke Li and Xing Sun.

News

• Research Intern at ByteDance AI Lab from 2023.03 to 2023.07 (working with Xiaoying Zhang, Yang Liu on Trustworthy Language Model)

• Research Intern at DAMO Academy, Alibaba from 2022.07 to 2023.02 (working with Qingsong Wen, Liang Sun on Robust Time Series Forecasting)

Selected Publication (* denotes equal contribution)

RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies (ICLR 2024)
Hao Cheng, Qingsong Wen, Yang Liu, Xing Sun.
paper code

Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models (ICLR 2024)
Zhaowei Zhu, Jialu Wang, Hao Cheng, Yang Liu.
paper code

Identifiability of Label Noise Transition Matrix (ICML 2023)
Yang Liu, Hao Cheng, Kun Zhang.
paper code

Mitigating Memorization of Noisy Labels via Regularization between Representations (ICLR 2023)
Hao cheng*, Zhaowei Zhu*, Xing Sun, Yang Liu.
paper code

Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations (ICLR 2022)
Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu.
paper code

Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR 2021)
Hao cheng*, Zhaowei Zhu*, Xingyu Li, Yifei Gong, Xing Sun, Yang Liu.
paper code

Pruning Filter in Filter (NeurlPS 2020)
Fanxu Meng*, Hao Cheng*, Ke Li, Huixiang Luo, Xiaowei Guo, Guangming Lu, Xing Sun.
paper code

Filter Grafting for Deep Neural Networks (CVPR 2020)
Fanxu Meng*, Hao Cheng*, Ke Li, Zhixin Xu, Rongrong Ji, Xing Sun, Guangming Lu.
paper code

Local to Global Learning: Gradually Adding Classes for Training Deep Neural Networks (CVPR 2019)
Hao Cheng, Dongze Lian, Bowen Deng, Shenghua Gao, Tao Tan, Yanlin Geng.
paper code

Evaluating Capability of Deep Neural Networks for Image Classification via Information Plane (ECCV 2018)
Hao Cheng, Dongze Lian, Shenghua Gao, Yanlin Geng.
paper code

Services

Reviewer of NeurlPS 2021/2022/2023, ICLR 2022/2023/2024, ICML 2022/2023/2024

Student organizer of IJCAI 2022 workshop on 1st Learning and Mining with Noisy Labels Challenge

Talk on IJCAI 2023 Tutorial: A Hands-on Tutorial for Learning with Noisy Labels