About me
I am a 2nd-year Ph.D. student at Cornell University, advised by Prof. Kevin Ellis. I completed my bachelor and master degrees in Shanghai Jiao Tong University.
My research interest lies in the generalizability of AI models, in particular, generalizing/adapting to out-of-distribution samples (e.g., inputs of larger scales, unseen combinations, and new concepts/domains). I currently focus on neuro-symbolic program synthesis to integrate neural networks with programmatic prior for better generalizability and interpretability. It involves fields such as program synthesis, latent abstraction learning, and learning to optimize.
Publications
- From Perception to Programs: Regularize, Overparameterize, and Amortize
Hao Tang, and Kevin Ellis
ICML 2023 [arxiv],
ICML Differentiable Everthing workshop 2023, PLDI MAPS symposium 2022. - Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs
Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-Liang Lu, and Hao Su
NeurIPS 2020 [arxiv] [code] [short-video] [poster] [pdf] [appendix]. - Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals
Tongzhou Mu*, Jiayuan Gu*, Zhiwei Jia, Hao Tang, and Hao Su
NeurIPS 2020 [arxiv] [code]. - Belief Propagation Neural Networks
Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, and Stefano Ermon
NeurIPS 2020 [arxiv]. - Emotion Recognition using Multimodal Residual LSTM Network
Jiaxin Ma*, Hao Tang*, Wei-Long Zheng, and Bao-Liang Lu
ACM Multimedia 2019 [pdf]. - Investigating Sex Differences in Classification of Five Emotions from EEG and Eye Movement Signals
Lan-Qing Bao, Jie-Lin Qiu, Hao Tang, Wei-Long Zheng, and Bao-Liang Lu
IEEE International Engineering in Medicine and Biology Conference (EMBC) 2019. - Multimodal Emotion Recognition Using Deep Neural Networks
Hao Tang, Wei Liu, Wei-Long Zheng, and Bao-Liang Lu
International Conference on Neural Information Processing (ICONIP) 2017 [pdf].
Misc.
- Top Reviewer of NeurIPS, 2022.