My research interests lie at the interface of statistics and machine learning with the goal of developing effective machine learning algorithms with theoretical guarantees. Currently, I am working on causal inference, disentanglement, and generative models (e.g., GANs).
Surprise sampling: Improving and extending the local case-control sampling Xinwei Shen, Kani Chen, Wen Yu
Electron. J. Statist., vol. 15, pp. 2454-2482, 2021 | paper
In vivo single-cell lineage tracing in zebrafish using high-resolution infrared laser-mediated gene induction microscopy
Sicong He, Ye Tian, Shachuan Feng, Yi Wu, Xinwei Shen, Kani Chen, Yingzhu He, et al.
Elife, vol. 9, e52024, 2020 | paper
Conference Publications:
TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation
Shizhe Diao*, Xinwei Shen*, KaShun Shum, Yan Song, Tong Zhang
Findings of ACL, 2021 (*equal contribution) | paper | code
CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models
Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, Jun Wang
CVPR, 2021 | paper | code