† Corresponding author
* Equal contribution or alphabetical order
♢ Student author that I supervised/co-supervised for the project
Preprints
S4M: S4 for multivariate time series forecasting with Missing
values
Jing Peng♢, Meiqi Yang,
Qiong
Zhang†, Xiaoxiao
Li
International Conference on Learning Representations (ICLR), 2025
openreview 
arXiv 
code 
poster 
BibTeX
@inproceedings{peng2025s4m,
title={{S4M}: S4 for multivariate time series forecasting with Missing
values},
author={Peng, Jing and Yang, Meiqi, and Zhang, Qiong and Li, Xiaoxiao},
booktitle = {The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=BkftcwIVmR},
}
Gaussian mixture reduction with composite transportation divergence
Qiong
Zhang, Archer Gong Zhang,
Jiahua
Chen
IEEE Transactions on Information Theory, 2023
journal 
arXiv 
code 
slides 
BibTeX
@article{zhang2024gaussian,
title={Gaussian Mixture Reduction With Composite Transportation Divergence},
author={Zhang, Qiong and Zhang, Archer Gong and Chen, Jiahua},
journal = {IEEE Transactions on Information Theory},
year={2024},
volume={70},
number= {7},
page= {5191-5212},
doi={10.1109/TIT.2023.3323346},
}
Distributed learning of finite Gaussian mixtures
Qiong
Zhang,
Jiahua
Chen
Journal of Machine Learning Research, 2022
journal 
arXiv 
code 
talk 
slides 
poster 
BibTeX
@zhang2024gaussian{zhang2022distributed,
title={Distributed Learning of Finite Gaussian Mixtures},
author={Zhang, Qiong and Chen, Jiahua},
journal = {Journal of Machine Learning Research},
year={2022},
volume={23},
number= {1},
page= {4265--4304},
url= {http://jmlr.org/papers/v23/21-0093.html},
publisher={JMLRORG}
}
Minimum Wasserstein distance estimator under finite location-scale mixtures
Qiong
Zhang,
Jiahua
Chen
Springer. Advances and Innovations in Statistics and Data Science, 2022
chapter 
arXiv 
BibTeX
@incollection{zhang2022minimum,
title={Minimum Wasserstein distance estimator under finite location-scale mixtures},
author={Zhang, Qiong and Chen, Jiahua},
booktitle={Advances and Innovations in Statistics and Data Science},
pages={69--98},
year={2022},
publisher={Springer}}
Robustness of Gaussian mixture reduction for split-and-conquer learning of finite
Gaussian
mixtures
Qiong
Zhang,
Jiahua
Chen
3rd International Conference on Statistics: Theory and Applications (ICSTA), 2021
proceedings 
talk 
BibTeX
@inproceedings{zhang2021robustness,
title={Robustness of Gaussian Mixture Reduction for Split-and-Conquer Learning of Finite Gaussian Mixtures},
author={Zhang, Qiong and Chen, Jiahua},
booktitle={Proceedings of the 3rd International Conference on Statistics: Theory and Applications (ICSTA'21)},
year={2021}
}
Boosting the generalization capability in cross-domain few-shot learning via
noise-enhanced
supervised autoencoder
Hanwen
Liang*,
Qiong
Zhang*,
Peng Dai,
Juwei Lu
International Conference on Computer Vision (ICCV), 2021
proceedings 
arXiv 
poster 
BibTeX
@inproceedings{liang2021boosting,
title={Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder},
author={Liang, Hanwen and Zhang, Qiong and Dai, Peng and Lu, Juwei},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={9424--9434},
year={2021}}
Classification beats regression: counting of cells from greyscale microscopic images
based on
annotation-free training samples
Xin Ding*,
Qiong
Zhang*,
William J. Welch
CAAI International Conference on Artificial Intelligence (CICAI), 2021
proceedings 
arXiv 
code 
poster 
BibTeX
@inproceedings{ding2021classification,
title={Classification Beats Regression: Counting of Cells from Greyscale Microscopic Images Based on Annotation-Free Training Samples},
author={Ding, Xin and Zhang, Qiong and Welch, William J},
booktitle={CAAI International Conference on Artificial Intelligence},
pages={662--673},
year={2021},
organization={Springer}
}
Small area quantile estimation via spline regression and empirical likelihood
Zhanshou Chen,
Jiahua
Chen,
Qiong
Zhang
Survey Methodology, 2019
journal 
BibTeX
@article{chen2019small,
title={Small area quantile estimation via spline regression and empirical likelihood},
author={Chen, Zhanshou and Chen, Jiahua and Zhang, Qiong},
journal={Survey Methodology 45-1},
volume={45},
number={1},
pages={81--99},
year={2019}}
Highlighting discrepancies in walking prediction accuracy for patients with traumatic
spinal
cord injury: an evaluation of validated prediction models using a Canadian multicenter
spinal cord injury registry
Philippe
Phan,
Brandon Budhram, Qiong
Zhang, Carly S. Rivers,
Vanessa K. Noonan, Tova Plashkes, Eugene K. Wai, Jérôme Paquet, Darren M. Roffey,
Eve
Tsai,
Nader Fallah
The Spine Journal, 2019
journal 
BibTeX
@article{phan2019highlighting,
title={Highlighting discrepancies in walking prediction accuracy for patients with traumatic spinal cord injury: an evaluation of validated prediction models using a {C}anadian multicenter spinal cord injury registry},
author={Phan, Philippe and Budhram, Brandon and Zhang, Qiong and Rivers, Carly S and Noonan, Vanessa K and Plashkes, Tova and Wai, Eugene K and Paquet, J{\'e}r{\^o}me and Roffey, Darren M and Tsai, Eve and others},
journal={The Spine Journal},
volume={19},
number={4},
pages={703--710},
year={2019},
publisher={Elsevier}}
Generating handwritten Chinese characters using CycleGAN
Bo Chang*,
Qiong
Zhang*,
Shenyi Pan,
Lili Meng
Winter Conference on Applications of Computer Vision (WACV), 2018
proceedings 
arXiv 
code 
talk 
BibTeX
@inproceedings{chang2018generating,
title={Generating handwritten chinese characters using cyclegan},
author={Chang, Bo and Zhang, Qiong and Pan, Shenyi and Meng, Lili},
booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
pages={199--207},
year={2018},
organization={IEEE}}
Small area quantile estimation under unit-level models
Master Thesis, University of British Columbia, Vancouver (2017)
UBC library 
BibTeX
@masterthesis{zhang2017small,
title={Small area quantile estimation under unit-level models},
author={Zhang, Qiong},
year={2017},
school={University of British Columbia}
}
Inference under finite mixture models: distributed learning and approximate
inference
Doctoral Thesis, University of British Columbia, Vancouver (2022)
UBC library 
BibTeX
@phdthesis{zhang2022inference,
title={Inference under Finite Mixture Models : Distributed Learning and Approximate Inference},
author={Zhang, Qiong},
year={2022},
school={University of British Columbia},
type= {{PhD} dissertation},
}