Research

I have great interest in statistical computing, deep learning, exact inference methods, large-scale data analysis, and data visualization.

Publication

Qiu, Y.*, Gao, Q.*, and Wang, X., Adaptive Learning of the Latent Space of Wasserstein Generative Adversarial Networks. *Joint first authors. Journal of the American Statistical Association, accepted, 2024+. Link PDF Code
Tang, Z. and Qiu, Y., Safe and Sparse Newton Method for Entropic-Regularized Optimal Transport. Advances in Neural Information Processing Systems (NeurIPS 2024), 2024. Package Code
Zheng, A. Y., He, T., Qiu, Y., Wang, M., and Wipf, D., Graph Machine Learning through the Lens of Bilevel Optimization. Artificial Intelligence and Statistics (AISTATS 2024), 2024. Link PDF
Lin, K. Z., Qiu, Y., and Roeder, K., eSVD-DE: Cohort-wide Differential Expression in Single-cell RNA-seq Data Using Exponential-family Embeddings. BMC Bioinformatics, 2024. Link PDF Code
Guo, X., Qiu, Y., Zhang, H., and Chang, X., Randomized Spectral Co-Clustering for Large-Scale Directed Networks. Journal of Machine Learning Research, 2023. Link PDF Code
Dai, B.* and Qiu, Y.*, ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence. *Joint first authors. Advances in Neural Information Processing Systems (NeurIPS 2023), 2023. Link PDF Project Page Code
Qiu, Y. and Wang, X., Efficient Multimodal Sampling via Tempered Distribution Flow. Journal of the American Statistical Association, accepted, 2023+. Link PDF Code
Qiu, Y., Lei, J., and Roeder, K., Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning. Biometrika, 2023. Link PDF Code
Zheng, Y., He, T., Qiu, Y., and Wipf, D., Learning Manifold Dimensions with Conditional Variational Autoencoders. Advances in Neural Information Processing Systems (NeurIPS 2022), 2022. Link PDF
Qiu, Y., Wang, J., Lei, J., and Roeder, K., Identification of Cell-type-specific Marker Genes from Co-expression Patterns in Tissue Samples. Bioinformatics, 2021. Link PDF Code
Qiu, Y. and Wang, X., ALMOND: Adaptive Latent Modeling and Optimization via Neural Networks and Langevin Diffusion. Journal of the American Statistical Association, 2021. Link Code
Qiu, Y. and Wang, X., Stochastic Approximate Gradient Descent via the Langevin Algorithm. AAAI Conference on Artificial Intelligence (AAAI 2020), 2020. Link PDF
Qiu, Y., Zhang, L., and Wang, X., Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models. International Conference on Learning Representations (ICLR 2020), 2020. Link PDF Code
Lu, J.*, Qiu, Y.*, and Deng, A., A Note on Type S/M Errors in Hypothesis Testing. *Joint first authors. British Journal of Mathematical and Statistical Psychology, 2019. Link PDF
Qiu, Y., Zhang, L., and Liu, C., Exact and Efficient Inference for Partial Bayes Problems. Electronic Journal of Statistics, 2018. Link PDF
Qiu, Y. and Wei, W., A Scalable Sequential Principal Component Analysis Algorithm (SeqPCA) with Application to User Access Control Analysis. IEEE International Conference on Big Data, 2017. Link
Abraham, G., Qiu, Y., and Inouye, M., FlashPCA2: Principal Component Analysis of Biobank-scale Genotype Datasets. Bioinformatics, 2017. Link PDF Code
Qiu, Y., Wang, X. et al., Web Usage Cluster Analysis Based on Prediction Strength. International Conference on Instrumentation, Measurement, Circuits and Systems, 2011.

Invited Talks

Efficient, Stable, and Analytic Differentiation of the Sinkhorn Loss
The 9th RUC International Forum on Statistics, 2023. Slides
Efficient Multi-Modal Sampling via Tempered Distribution Flow
The 12th ICSA International Conference, 2023. Slides
Gradient-based Sparse Principal Component Analysis with Applications to Gene Co-expression Analysis
City University of Hong Kong Biostatistics Seminar (online), 2023. Slides
Efficient Multi-Modal Sampling via Tempered Distribution Flow
Statistical Learning Methods in Modern AI, Tianyuan Mathematical Center in Northwest China (website), 2021.
Efficient Multi-Modal Sampling via Tempered Distribution Flow
University of Missouri (online), 2021.
Prettier R Graphs and Documents with {showtext}+{prettydoc}
Cleveland R User Group (website), 2020. Slides
Gradient-based Sparse Principal Component Analysis
The 11th ICSA International Conference, 2019. Slides
Adaptive Latent Modeling and Optimization via Neural Networks and Langevin Diffusion
The 36th Annual Quality and Productivity Research Conference, 2019. Slides
Exact Inference with Partially Specified Bayesian Models
2017 ICSA Applied Statistics Symposium, 2017. Slides
SupR: Multi-threaded R Environment
The 9th China-R Conference, 2016. Slides
Large-Scale SVD and Matrix Completion
The 7th China-R Conference, 2014.